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Term: Revenue Growth Management (RGM) - FMCG / CPG

"Revenue Growth Management (RGM) is a data-driven strategy used by FMCG and CPG companies to maximize revenue and profit margins without relying solely on higher sales volume. It optimizes the entire commercial mix to ensure the right product reaches the right consumer on the right occasion." - Revenue Growth Management (RGM) - FMCG / CPG

Margin pressure in fast-moving consumer goods rarely comes from a single source. Retailers push for lower net prices, shoppers become more price sensitive during inflationary spikes, private labels advance, and input costs fluctuate unpredictably. Under these conditions, simply selling more units often destroys value rather than creating it, as volume is bought with discounting, deep promotions, or costly innovation that does not pay back. The central challenge is therefore not how to grow sales at any cost, but how to configure prices, packs, promotions, channels, and trade terms so that every additional unit sold contributes positively to profit.

The commercial problem RGM is trying to solve

Traditional top-line management in FMCG has often relied on blunt tools: across-the-board price increases, promotional intensity ramp-ups, or broad portfolio extensions. These approaches can yield short-term gains but frequently erode long-term brand equity, confuse shoppers, and complicate retailer relationships. When price architecture is inconsistent, promotions undercut base prices, and assortment is bloated, manufacturers end up transferring value to retailers and consumers rather than capturing it themselves.

The underlying mechanism is misalignment between three perspectives that must be reconciled. First, shoppers have discrete willingness-to-pay segments and occasion-based needs that are not fully captured by average price elasticities. Second, retailers focus on category profit, traffic, and basket size, not on any single brand, and they use their shelf space as a scarce asset to be allocated to the most productive SKUs. Third, manufacturers need sustainable gross margin and contribution to cover marketing, overheads, and innovation. Without a structured approach, these perspectives collide in annual negotiations, ad hoc promotions, and reactive pricing decisions, generating volatility instead of disciplined value creation.

Revenue Growth Management (RGM) arises as a response to this multi-sided optimisation problem: it builds a system to align shopper value, retailer economics, and manufacturer profit by orchestrating the full commercial mix with data and analytics.

Substantive meaning beyond the label

In practical terms, RGM is a cross-functional discipline that brings together trade marketing, category management, sales, finance, and revenue management to shape how the portfolio makes money in each market. Rather than treating price, promotion, pack, and trade terms as isolated levers owned by different teams, RGM defines a coherent strategy for how the business should earn its net revenue and margin across channels, customers, and shopper segments.

The aim is to grow both revenue and margin simultaneously by identifying and monetising hidden pockets of value. This may involve monetising convenience or premium attributes at the top of the portfolio, rationalising tail SKUs that dilute margin, redesigning pack sizes to better match occasions and price thresholds, or reallocating trade investment to the promotions that truly change shopper behaviour.

For FMCG and CPG manufacturers, RGM is not only about internal profitability. It also shapes how they collaborate with retailers: setting category growth agendas, defining the role of each brand and pack in the shelf, and agreeing on promotion mechanics that build the category instead of triggering price wars.

The core RGM levers in FMCG

Most frameworks converge on a small set of commercial levers that RGM systematically optimises. Commonly cited levers include pricing, promotion, assortment, pack architecture, trade terms, and sometimes channel or mix.

1. Pricing

Price is the most visible signal of value to shoppers and the main driver of net revenue per unit. In RGM, pricing decisions move from generic increases to carefully crafted price ladders across SKUs, brands, and channels. The goal is to define a price architecture that reflects perceived value tiers, minimises intra-portfolio cannibalisation, and respects retailer value equations.

Data-driven pricing under RGM involves analysing price elasticity by segment and channel, identifying optimal price points for different pack sizes, and simulating the margin impact of alternative list and net price scenarios. Instead of uniform changes, teams vary price moves by brand strength, role (traffic builder versus premium margin driver), and competitive intensity.

2. Promotions

Promotional investment is often one of the largest P&L lines in FMCG but historically has been poorly measured. RGM introduces rigorous promotional effectiveness analysis, seeking to understand which promotions generate true incremental volume versus subsidising base sales. The focus shifts from frequency and depth to efficiency, payback period, and long-term equity impact.

Practices include defining promotion floors and ceilings, limiting unprofitable mechanics, and calibrating event timing to category seasons and competitive activity. Leading companies link promotion plans to precise objectives such as switching, stock-building, or trial, and adjust mechanics accordingly.

3. Assortment

Assortment decisions determine which products appear in which stores and formats. Overexpansion of SKUs increases supply chain complexity and ties up working capital, while underrepresentation reduces availability on key occasions. RGM uses store-level and shopper-level data to identify the contribution of each SKU to category growth and profit, then rationalises or tailors assortments by channel and customer.

The objective is to focus shelf space on productive items that add incremental value rather than duplicating existing offers. This can imply eliminating low-rotation variants, elevating high-margin premium lines, or developing channel-exclusive SKUs that align with retailer strategies.

4. Pack architecture

Pack architecture links physical format, size, and configuration to price points and consumption occasions. By designing a logical ladder of packs that address different affordability thresholds and usage needs, manufacturers can tap into both premiumisation and downtrading trends without eroding margin. RGM analyses demand patterns to define optimal pack sizes for single-serve, family, and bulk formats across channels.

In inflationary contexts, pack resizing and format innovation become particularly powerful levers to manage perceived price increases while maintaining unit margins. Value packs, multipacks, and channel-specific formats (for discounters, e-commerce, convenience) are tuned to local shopper behaviour.

5. Trade and channel terms

Trade investment, discounts, and rebates determine the net price manufacturers realise after the complexities of retailer negotiations. RGM frameworks increasingly treat trade terms as a strategic lever: harmonising conditions across comparable customers, rewarding growth behaviours, and linking investment to joint business plans.

Channel strategy is often considered alongside trade terms, as different routes to market (modern trade, traditional trade, e-commerce, on-premise) require distinct price and pack architectures, as well as differentiated promotional mechanics. Advanced RGM decomposes performance by channel to decide where to allocate scarce commercial resources.

Data, analytics, and mathematical specification

Although RGM is fundamentally commercial, modern practice relies heavily on quantitative modelling. At a basic level, pricing and promotion decisions draw on demand models where sales volume depends on own price , competitor prices , promotion flags , and seasonality : . Elasticities derived from these models help simulate the impact of different actions on volume and revenue.

For example, a simple log-linear model might specify the relationship as , where is own-price elasticity and measures the proportional uplift from promotion. RGM teams use such estimates to project how a price increase or promotion depth change will affect both revenue and margin.

Margin optimisation frequently involves expressing profit as , where is unit cost and is the demand function. The task is to identify price levels that maximise , subject to competitive and retailer constraints. Portfolio-level models extend this to multiple SKUs , considering cannibalisation: .

On promotions, incremental volume is estimated by comparing promoted weeks to a counterfactual baseline , with ROI calculated as . Events falling below threshold ROI are candidates for redesign or elimination. In assortment work, decision rules may be grounded in metrics such as incremental profit contribution or transferability of demand to alternative SKUs, derived from choice models.

While not all organisations deploy complex econometrics, even simpler elasticity tables, price ladders, and promo scorecards embed the same logic: using quantitative relationships between price, volume, and cost to systematically steer the commercial mix rather than relying on intuition alone.

Key parameters and capabilities

For RGM systems to function, a set of parameters and organisational capabilities must be defined and maintained. At a technical level, core inputs include baseline volume, price elasticities by segment, incremental lifts by promotion mechanic, gross-to-net waterfalls by customer, cost-to-serve by channel, and SKU profitability contribution. These parameters underpin scenario simulations and decision guidelines.

On the organisational side, leading FMCG companies build dedicated RGM teams with clear mandates, governance, and links to the annual planning and budgeting cycles. Typical responsibilities include designing the price and pack strategy for a planning period, setting promo guardrails, providing analytical support for customer negotiations, and monitoring post-event performance. RGM often sits at the interface of marketing and sales, with strong involvement from finance to ensure that top-line decisions align with profit and cash generation objectives.

Technology enablers range from data lakes and pricing tools to promotion optimisation platforms and dashboards that track net revenue performance. However, advisory firms consistently highlight that tools alone are insufficient: capability building, incentives, and decision rights are responsible for the majority of impact.

Major schools of thought and frameworks

Although terminology varies, most consultancies and practitioners converge on similar RGM architectures for FMCG and CPG. One school emphasises the five commercial levers: pricing, pack architecture, promotions, trade terms, and channel strategy, and demonstrates how orchestrating these levers together reveals hidden value in portfolios. Another approach, often labelled net revenue management, frames RGM within a broader strategy-levers-enablers model, where a clear revenue strategy and organisational capabilities are seen as prerequisites for effective lever execution.

Some organisations lean towards shopper-centric RGM, starting from occasion-based segmentation and working backwards to define the optimal price-pack architecture and promotion role for each segment. Others adopt a more finance-driven lens, focusing first on gross-to-net leakage, mix effects, and structural margin improvement, then translating insights into commercial tactics. Both perspectives remain compatible and are increasingly integrated into end-to-end frameworks.

Differences also exist in how centralised RGM should be. One school advocates strong global guardrails and tools with local adaptation for market specifics, while another argues for heavily localised teams given the heterogeneity of retailer landscapes and shopper behaviour. Hybrid models, in which central teams define methodologies and platforms and local teams own decisions within those frameworks, have become common.

Tensions, trade-offs, and debates

RGM operates in a landscape of inherent tensions. A recurring debate concerns short-term promotion-driven revenue versus long-term brand equity and pricing power. Aggressive discounting may deliver quarterly targets but teach shoppers to wait for deals, undermining base price and future margin. Conversely, overly rigid adherence to premium positioning can lead to share loss in highly price-sensitive segments or in economic downturns. RGM provides the analytical clarity to quantify these trade-offs but cannot fully resolve the underlying strategic choices.

Another tension lies in retailer relationships. Optimising price and trade terms purely from the manufacturer perspective can harm collaboration if it ignores category profitability or the retailer's competitive context. Many successful RGM programmes actually deepen joint planning, using shared data and models to identify win-win interventions that grow category value rather than simply shifting margin. However, this demands transparency, trust, and advanced data-sharing arrangements, which are not always present.

There is also an internal cultural debate around who owns RGM decisions. Sales teams may view centrally imposed price or promo guidelines as constraints that make customer negotiations harder, while central revenue managers may see local deal-making as a source of margin leakage and complexity. Governance models, incentive schemes, and communication become critical in ensuring that RGM is perceived as an enabler of better deals rather than an administrative constraint.

Finally, the growing use of advanced analytics and AI in RGM raises questions about explainability and human judgment. Algorithmically optimised price and promo recommendations can be difficult to communicate to retailers or internal stakeholders. Many organisations therefore adopt a human-in-the-loop approach, where algorithms generate scenarios and humans adjudicate based on market knowledge and strategic intent.

Why RGM remains strategically important

Several structural trends in FMCG and CPG ensure that RGM will remain central to commercial strategy. Inflationary episodes and cost volatility heighten the need for disciplined price and pack management; manufacturers must pass through cost increases without triggering disproportionate volume losses or retailer conflict. RGM allows them to sequence price moves, adjust formats, and calibrate promotions to protect both share and profitability.

The rise of discounters, e-commerce, and direct-to-consumer channels multiplies price points and promotional environments. Without a coherent RGM system, pricing architecture becomes fragmented, leading to cross-channel conflicts, grey markets, and shopper confusion. Structured management of channel price corridors, promo intensity, and assortment is essential to maintain a stable brand value proposition across touchpoints.

Data availability is another driver. Loyalty data, ePOS feeds, and panel data provide granular visibility into shopper behaviour at the level of store, basket, and occasion. RGM frameworks are the mechanism by which this data is translated into actionable commercial decisions: which SKUs to list, where to place them, how to price and promote them, and where to invest trade budgets.

At the same time, investors increasingly scrutinise organic growth quality rather than just headline revenue expansion. The ability to demonstrate disciplined net revenue management, healthy price-mix contributions, and resilient margins has become a key component of equity narratives for major consumer goods companies.

In a world where unit volume growth is often constrained by demographics, saturation, or sustainability considerations, the ability to extract more value from each unit sold without alienating shoppers or partners becomes a competitive differentiator. Revenue Growth Management, as practised today in FMCG and CPG, provides the structured, data-driven means to do exactly that: orchestrating prices, promotions, packs, assortment, and trade terms so that profitable growth is designed rather than hoped for.

Quote: "Every accusation is a confession." - Unknown

"Every accusation is a confession." - Unknown

Conflict rarely begins with an admission of guilt; it begins with a claim that someone else is at fault. In personal relationships, politics, organisational life and criminal justice, the first move is more often an accusation than a confession. Yet those accusations frequently reveal more about the accuser than about the person being blamed. Understanding why this happens requires looking beneath overt charges of wrongdoing to the psychological need to defend the self, manage anxiety and preserve a coherent identity, even at the cost of distorting reality.

Projection as the hidden engine behind blame

Modern psychology offers a clear mechanism for why a charge against another so often doubles as a self-indictment: projection. In psychoanalytic theory, projection is the mental process by which individuals attribute their own internal thoughts, feelings, impulses or traits to another person or group. Instead of acknowledging "I am angry" or "I am dishonest", the individual experiences the world as populated by angry, dishonest others.

The American Psychological Association defines projection as attributing one's own positive or negative characteristics and impulses to others, often as a defence mechanism against unacceptable feelings or responsibilities. This is not a conscious lie; it is an unconscious rearrangement of experience that allows the ego to disown what it finds threatening. The uncomfortable emotion does not disappear; it is relocated. What was internal is now seen as external.

Freud framed projection as a way of protecting the ego from distress by externalising conflicts that cannot be admitted directly. Later clinicians extended this to everyday life: the person plagued by self-criticism comes to believe that others are constantly judging them; the chronically unfaithful partner insists that their spouse is the one likely to stray; the dishonest manager becomes preoccupied with alleged disloyalty among staff. In each case, accusation and confession are psychologically intertwined. The accusation constitutes a displaced confession, protecting the accuser from direct self-knowledge.

Why the psyche turns confession into accusation

Projection does more than shift blame; it reduces psychological tension. Several overlapping motives make it attractive:

- Ego protection. Owning up to envy, cruelty, prejudice or selfishness threatens self-esteem. Projecting these impulses onto others allows the person to maintain a morally acceptable self-image while still expressing strong feelings.

- Avoidance of responsibility. By insisting that others are the problem, the individual sidesteps the difficult work of change. Responsibility for conflict, failure or harm is assigned outward, and any demand for introspection can be dismissed as unfair.

- Anxiety reduction. Inner conflicts, especially those rooted in early relationships or trauma, generate diffuse anxiety. Projection consolidates this anxiety into a concrete external threat. It may be painful to feel under siege, but it feels more manageable than facing amorphous internal turmoil.

- Worldview reinforcement. People tend to interpret events in ways that confirm pre-existing beliefs. If someone is convinced that the world is full of liars or that no one can be trusted, projection ensures they will "find" supporting evidence in the behaviour of others.

Because projection operates largely outside awareness, the accuser can feel utterly sincere. The sense of conviction, coupled with intense emotion, can make the accusation seem more reliable to observers than it actually is. Yet the very intensity of the feeling is often a clue that the charge may be carrying a hidden personal burden.

From personal quarrels to political paranoia

In intimate relationships, projection frequently surfaces as repeated, emotionally charged accusations. One partner insists the other is cold, controlling or unfaithful, often in the absence of proportional evidence. Over time, such accusations can create an atmosphere of chronic suspicion and defensiveness. As relational psychologists note, recurrent accusations of behaviours the accuser secretly fears or dislikes in themselves are a strong marker that projection is at work.

Beyond family life, projection plays a decisive role in group conflict and propaganda. Social psychologists have documented patterns where leaders or movements attribute their own intentions or methods to adversaries, a tactic sometimes referred to as "accusation in a mirror". A group considering violence accuses the other side of plotting extermination; those willing to suppress speech warn of the opponent's authoritarianism. By pre-emptively projecting their own designs onto others, they both justify aggressive action and mask their true motivations.

Such dynamics contribute to what observers describe as political paranoia: the conviction that one's opponents are guilty of every vice one is most unwilling to acknowledge in one's own camp. The more threatened a group feels, the more attractive projection becomes. It not only absolves the in-group of blame, it supplies a moral mandate to act against the alleged wrongdoers. In this sense, large-scale accusations may encode the very intentions, resentments and fears that group leaders cannot confess openly, even to themselves.

Accusation, authority and the path to confession

There is a second, more literal channel through which accusations turn into confessions: the dynamics of interrogation. Legal psychologists have long studied how the simple fact of being accused, especially by an authority figure, can push individuals towards confession, regardless of actual guilt.

Classic work on the psychology of confession notes that when a person confronts an accusation from authority, two conditions tend to arise: their perceived freedom of action shrinks and they move onto the psychological defensive, feeling on unsure ground. This restriction of "space of free movement" and heightened defensiveness make them more susceptible to pressure, suggestion and the promise of relief if they admit wrongdoing.

Contemporary research distinguishes between voluntary, persuaded and compliant false confessions. Voluntary false confessions may arise without external pressure, often from internal psychological needs such as guilt, desire for attention, or the wish to protect someone else. Persuaded false confessions occur when intense, prolonged interrogation leads suspects to doubt their own memories, sometimes even coming to believe they might have committed the act despite initial certainty of innocence. Compliant false confessions emerge when individuals knowingly confess to crimes they did not commit to escape a more threatening situation, such as promised leniency or threats of harsher punishment.

In this context, the accusation becomes the starting point of a process that can culminate in confession, whether genuine or false. The accusing stance of authority shapes the suspect's psychological landscape: it communicates power, defines the suspect as the one who must defend themselves, and implicitly offers confession as a route back to safety. Research shows that juries tend to treat confession evidence as highly persuasive, even when there are signs of coercion or lack of corroborating evidence, and that confidence in recognising false confessions is largely unfounded. That makes the relationship between accusation and confession not only psychological but deeply consequential for justice.

Hypocrisy, self-deception and the moral charge of blame

Accusations gain much of their emotional force from claims about moral standing. The accuser typically occupies the role of victim, guardian or whistleblower, often presenting themselves as uniquely sensitive to the wrongdoing in question. Yet when projection or self-deception is involved, that moral posture can shade into hypocrisy: condemning in others what one practises oneself.

Moral philosophers distinguish between straightforward wrongdoing and hypocrisy partly because hypocrisy involves an additional layer of misrepresentation. The hypocrite not only violates a standard but actively pretends to uphold it, often demanding it of others. Projection is one route to this stance. By genuinely experiencing the other as the problem, the individual can maintain a self-concept as principled while behaving in ways that contradict those principles.

This interplay between blame and self-deception shows up across domains. Anti-corruption crusaders are sometimes later exposed as corrupt; moralistic public figures denounce sexual immorality while engaging in it; corporate leaders loudly criticise competitors for practices similar to those within their own firms. Observers often react with special indignation, sensing that the accusation functioned as a kind of moral camouflage. In these cases, the accusation was not simply a misjudgment; it was also, retrospectively, a confession of the very values and vulnerabilities that the accuser could not acknowledge.

Debates and limits: when accusation is not confession

It is tempting to invert the problem and treat every accusation as suspect, assuming that those who complain must be hiding their own guilt. That would be a mistake. While projection is a well-established phenomenon, it does not apply to all, or even most, accusations. People frequently identify genuine harms done by others, and to dismiss their claims as mere confession risks silencing victims and shielding perpetrators.

Clinicians emphasise that projection is more likely in specific patterns: repeated accusations about the same trait, disproportionate emotional reactions, and a persistent reluctance to acknowledge one's own contribution to conflicts. The context matters. A single, measured complaint supported by evidence does not carry the same psychological signature as ongoing, obsessive blame directed at multiple people over time. Moreover, projection can mingle with accurate perception. Someone may correctly perceive another's anger while simultaneously projecting their own unresolved rage onto that person, amplifying the intensity of the accusation.

There is also a cultural and political risk in overextending the idea that accusation is confession. Authoritarian regimes and abusers often invert reality by claiming that those who protest or criticise are themselves the real aggressors, weaponising psychological language to delegitimise dissent. Labelling opponents' concerns as projection can become a tactic for avoiding accountability, the very outcome that the concept was meant to expose.

For these reasons, the insight that accusations sometimes reveal the accuser's inner world must be held alongside careful attention to evidence, power dynamics and the concrete details of each case. Psychological patterns are not courtroom rules; they are lenses, to be used judiciously.

Psychological and practical markers of projected accusation

Despite these caveats, certain recurring features make it more likely that a charge is carrying an implicit confession. Therapists and relational psychologists describe several warning signs:

- Repetition across contexts. The accuser levels similar charges against different people in different situations, often with escalating certainty.

- Disproportionate intensity. The emotional reaction far exceeds what the situation appears to warrant, given the available facts.

- Resistance to nuance. The accused is cast in stark terms, with little room for mixed motives or shared responsibility.

- Difficulty taking accountability. The accuser consistently minimises their own role in conflicts, insisting that others are wholly to blame.

- Paranoid flavour. There is a sense of constant threat or persecution, as if almost everyone were engaged in the same alleged behaviour.

In personal life, learning to recognise these patterns can help people respond more constructively. Instead of immediately defending themselves point by point, they might gently question whether the accusation reveals something about the accuser's fears or unresolved issues. In organisational settings, leaders can watch for team members whose criticism of others mirrors their own conduct, using it as a prompt for coaching rather than simply as a reason for punishment.

Introspection as an antidote to weaponised blame

Addressing the tendency to turn confession into accusation requires more than knowledge of projection; it demands sustained self-reflection. Psychologists emphasise practices that increase awareness of internal states before they are displaced onto others. These include regular reflection on emotional triggers, mindfulness techniques for noticing anger or fear as it arises, and seeking feedback from trusted others about how one comes across.

Therapeutic work often focuses on helping individuals tolerate the discomfort of acknowledging unwelcome aspects of themselves. Instead of needing to see others as weak, dishonest or malicious in order to feel strong and virtuous, clients are encouraged to integrate a more complex self-image: capable of both generosity and envy, honesty and evasion. As self-knowledge grows, the impulse to project diminishes, and accusations become more proportionate and reality-based.

At the collective level, institutions can build safeguards against the most damaging forms of projected accusation. In criminal justice, reforms such as mandatory recording of interrogations, careful scrutiny of confession evidence and training on false confession risk factors aim to prevent the path from accusation to confession from becoming a conveyor belt to wrongful conviction. In organisations and politics, procedures that require evidence, transparency and independent review make it harder for accusations driven by personal conflicts or psychological needs to stand unchallenged.

Why the link between accusation and confession matters now

In an era saturated with public blame - from social media call-outs to adversarial politics - the relationship between what people accuse others of and what they cannot face in themselves has become more than a clinical curiosity. The speed and scale at which accusations circulate mean that a single psychologically loaded claim can shape reputations and policies far beyond the immediate context.

Recognising that some accusations carry the structure of confession invites a more reflective public culture. It suggests that listeners should attend not only to the content of a charge but to its vehemence, selectivity and the track record of the person making it. It also asks would-be accusers to examine their own motives before speaking: Is the impulse to expose wrongdoing accompanied by a willingness to scrutinise one's own behaviour, or does it rest on a comforting narrative that all the darkness resides elsewhere?

None of this undermines the importance of genuine whistleblowing, accountability or protest. Rather, it sharpens the tools for distinguishing between necessary moral speech and the displacement of unacknowledged guilt. Where projection dominates, conflict tends to escalate, dialogue breaks down and learning stalls. Where people can own their contributions, including their failures, the path opens for more honest negotiation and repair.

The deeper lesson is that the human mind rarely presents its own motives in a simple, transparent way. Blame directed outward and guilt held inward are bound together in intricate loops. To navigate a world of accusations responsibly, it is not enough to ask whether others are guilty. It is also necessary to ask, quietly and repeatedly, what our own accusations might be saying about us.

Term: Pack-price architecture (PPA) - FMCG / CPG

"Pack-price architecture (PPA), also known as price-pack architecture, is a core Revenue Growth Management strategy in FMCG and CPG. It involves designing and optimising product sizes, formats, and their corresponding price points to meet specific consumer needs, increase accessibility, and drive profitability across different retail channels." - Pack-price architecture (PPA) - FMCG / CPG

Margin pressure in consumer goods rarely stems from a single source. Rising input costs, retailer consolidation, and increasingly promotion-savvy shoppers combine to compress profitability, even as brands are expected to remain affordable and constantly available. In that environment, the specific ways in which products are sized, bundled, and priced across channels can matter as much as headline list prices. Getting that structure wrong traps brands in unprofitable mix and weak price realisation; getting it right turns the everyday shelf into a finely tuned engine for revenue growth.

Why pack and price structure matter more than ever

Three structural shifts have pushed pack-price decisions to the centre of FMCG and CPG strategy. First, inflation spikes have forced manufacturers to raise prices repeatedly, making consumers far more attentive to perceived value and price thresholds. Second, omnichannel retailing has multiplied touchpoints: the same brand now competes in discounters, supermarkets, convenience, cash-and-carry, and e-commerce, each with distinct missions and basket sizes. Third, retailer power means that manufacturers rarely control the shelf price directly; their influence lies in recommended pricing, pack sizes, and funding models.

In this context, relying on across-the-board list price increases or blunt promotion cuts is no longer sufficient. Revenue growth increasingly comes from reshaping the product line-up: adjusting pack sizes to unlock critical price points, creating laddered offerings for different income segments, designing value formats for e-commerce, and pruning unproductive variants that clutter the shelf. The discipline that orchestrates these choices is pack-price architecture.

Substantive meaning: from simple sizing to strategic architecture

In commercial practice, pack-price architecture goes well beyond offering a few different sizes. It is a structured way to define:

- The distinct consumer and shopper occasions (single-serve, family meal, bulk pantry, on-the-go, trial, gifting).

- The pack formats that best serve those occasions (units, volume, bundles, multipacks, variety packs, refills).

- The price ladders across those formats, including absolute price points (for example 0,99; 1,49; 4,99) and implied unit prices.

- The role of each pack in the portfolio (entry, core, premium, trade-up, traffic builder, margin driver).

- The channel-specific manifestations of that line-up (discounters versus e-commerce versus convenience).

At a practical level, a pack-price architecture project typically maps every SKU in a category against volume, net price, margin, and shopper penetration, then clusters them into coherent tiers. The goal is to ensure that each combination of size and price has a deliberate purpose and a clear consumer and channel rationale, rather than being the by-product of ad hoc launches and retailer requests.

The three classic RGM levers: price, promo, and pack

Revenue growth management in consumer goods is often framed around three levers: list price, promotions, and pack size. When costs rise, manufacturers can:

- Increase list prices.

- Reduce promotional depth or frequency.

- Alter pack configurations and sizes.

The third lever has become particularly important because retailers and consumers resist visible price hikes, whereas changes in format can be framed as new options or improved convenience. For example, introducing a slightly smaller pack to hit a psychologically salient price point, while keeping or improving margin per kilogram, can be more acceptable than lifting the ticket price of the existing pack. Equally, upsizing a pack at a modestly higher price may improve perceived value and grow basket size.

In mature RGM setups, price-pack architecture is not treated as a one-off reaction to cost shocks but as an ongoing capability: a repeatable process that continuously tests pack roles, price corridor integrity, and mix profitability.

Core analytical building blocks

Although the language is commercial, the underlying work is analytical. Several measures and relationships are central.

Price per unit and value ladders

At the base is the relationship between pack size and effective price per unit. If a product has a pack price and volume , the effective price per unit is . PPA systematically compares across the portfolio and against competitors to ensure a coherent value ladder: smaller packs typically carry a premium per unit, while larger packs offer a discount per unit, within defined guidelines.

Manufacturers often establish guardrails, for example that a single-serve pack should not exceed the per-unit price of the core family pack by more than a specified percentage, or that a bulk format must deliver a minimum per-unit saving to justify space and consumer stock-up risk.

Price elasticity and pack elasticity

Price elasticity measures how volume responds to a change in price. If volume is and price is , own-price elasticity is defined as . In practice, analysts estimate using historical data on price changes, promotions, and volumes.

Pack-price architecture extends this idea by looking at the sensitivity of demand to changes in pack size at given price thresholds. For example, if a 500 g pack is replaced with a 450 g pack at the same shelf price, the observed impact on volume and value reveals an implicit elasticity to downsizing. This is particularly relevant when exploring strategies that may be perceived as shrinkflation, where consumer reaction can be non-linear and media-sensitive.

Mix, margin, and net revenue

Even where category volume is stable, shifting mix from low-margin formats to higher-margin ones can significantly increase net revenue. Net revenue per pack can be approximated as , where is net list price to the retailer, is cost of goods, and is the net trade spend per pack. PPA diagnostics often plot packs on a volume versus net revenue or contribution margin map, highlighting formats that destroy value despite high turnover and those that could be amplified.

Scenario analysis then evaluates portfolio-level effects: for example, reducing reliance on a low-margin, highly promoted large multipack by introducing a slightly smaller, better-margin alternative, while controlling cannibalisation of higher-value single-serve packs.

Parameter choices and practical levers

Translating analysis into an actionable architecture requires explicit design choices around key parameters.

Pack sizes and structure

Key decisions include:

- Number of pack sizes: how many steps in the ladder from smallest to largest without confusing shoppers or overcomplicating operations.

- Step sizes: whether to use linear increments (for example 250 g, 500 g, 1 000 g) or tailored sizes that match usage occasions (for example portion counts per week or per household).

- Format differentiation: bottles versus sachets, pouches versus tubs, single items versus multipacks, refill systems for sustainability.

These choices interact with manufacturing and logistics constraints, such as line changeover times, packaging material costs, and pallet efficiency. A theoretically attractive pack size may be rejected if it undermines factory throughput or retailer shelf standards.

Price tiers and thresholds

Price architecture defines specific price points for each pack and the spacing between them. The design typically respects:

- Psychological thresholds (for example 0,99; 1,49; 1,99; 4,99), which differ by category and market.

- Channel-specific ceilings and floors, such as the maximum acceptable price for an impulse purchase in convenience.

- Relative gaps between tiers, to maintain clear differentiation and discourage trading down from premium tiers solely on price.

In inflationary periods, PPA often identifies which packs can bear explicit price increases and where to instead adjust pack size while preserving nominal price points. This balance is delicate: repeated stealth downsizing can erode trust, while aggressive list price moves risk volume collapse and retailer pushback.

Channel and retailer tailoring

Because missions differ by channel, the same pack-price structure rarely performs optimally everywhere. Discounters may favour fewer, larger formats with strong value credentials; convenience stores prioritise small, high-velocity packs with higher absolute margins per facing; e-commerce often rewards larger, more efficient packs that reduce shipping cost per unit.

Advanced PPA work therefore builds channel-specific architectures, deciding which packs are global, which are channel-exclusive, and how price ladders flex by route-to-market. This is negotiated with retailers as part of joint business planning, framed around category growth and shopper satisfaction rather than pure margin extraction.

Schools of thought and strategic postures

Practitioners tend to cluster into several broad schools when approaching pack-price architecture.

Value-access school

One approach emphasises affordability and penetration, particularly in emerging markets and low-income segments. It favours creating small, low out-of-pocket packs at accessible price points to keep brands within reach of cash-constrained shoppers. Here, the architecture is designed to open the category to more households, even at higher per-unit prices, with the expectation that some consumers will later trade up to larger, better-value formats.

Premiumisation and mix-up school

A second school focuses on trading consumers up. It engineers formats, claims, and usage occasions that support higher absolute price points and richer margins: multipacks for sharing, premium flavours or ingredients in mid-sized packs, or special designs for gifting. In this view, the architecture is a way to segment the category into tiers, making the core brand stretch upwards without alienating value-seeking shoppers.

Efficiency and simplification school

A third orientation treats PPA as a tool for portfolio rationalisation. The aim is to reduce SKU complexity, eliminate overlapping sizes and price points, and concentrate volume in a smaller number of strategically important packs. Proponents highlight the operational benefits: lower inventory, fewer changeovers, clearer shelf sets, and more negotiating leverage with retailers.

In practice, effective strategies often blend these schools: using small packs to maintain accessibility, a streamlined core to drive volume, and carefully crafted premium formats at the top of the ladder.

Debates, tensions, and controversies

Despite its technical framing, pack-price architecture sits at the centre of several contentious debates.

Shrinkflation and consumer trust

One prominent controversy is the link between PPA and shrinkflation. Critics argue that downsizing packs while keeping prices steady is a deliberate attempt to obscure real price increases, exploiting consumer inattentiveness to volume. Media and advocacy groups have highlighted examples where effective price per unit rose significantly, even as nominal prices remained unchanged.

Supporters counter that modest size changes can help preserve access when cost inflation would otherwise force sharp jumps in shelf prices, and that regulatory regimes typically require clear on-pack volume disclosure. The tension lies in transparency and perceived fairness: architectures that rely heavily on hidden per-unit inflation risk reputational damage and regulatory scrutiny, particularly where changes are repeated and poorly communicated.

Manufacturer-retailer power dynamics

Another tension arises from the differing objectives of manufacturers and retailers. Manufacturers may design an architecture to strengthen premium tiers or protect brand equity, whereas retailers may push for larger, aggressively priced formats to drive traffic and basket size. The negotiation covers not only which packs are listed but also promotional calendars, feature and display, and own-label competition.

Where retailers have strong private label offerings, pack-price decisions become a battlefield for differentiation: a manufacturer may introduce unique intermediate sizes or formats that are harder to copy directly, or invest in packaging features that justify distinct price points. Retailers, in turn, may align their private label sizes and prices to undercut branded per-unit prices at key thresholds.

Data complexity and model risk

Modern architectures are often built on sophisticated demand models and scenario simulations. While this enables granular optimisation, it also introduces model risk: overfitting to historical promotion conditions, underestimating competitor reaction, or misreading structural shifts in shopper behaviour. Organisations that treat model outputs as definitive prescriptions rather than inputs to commercial judgement can end up with overly complex line-ups or misaligned price ladders.

Why pack-price architecture remains strategically important

Several structural trends suggest that PPA will remain a core discipline within revenue growth management.

Inflation and volatile input costs

Periods of cost volatility are unlikely to disappear. Each new wave of inflation or commodity shock reopens the question of how to pass through cost while sustaining demand. Pack-price architecture offers a structured way to distribute that burden: some via new formats, some via price point resets, and some via mix management.

Omnichannel and digital shelf dynamics

Online channels not only change pack economics but also transparency: per-unit prices, promotions, and cross-brand comparisons are easier to display and automate. Architectures must therefore be robust both to physical shelf behaviour and to digital search and filter patterns. For example, bulk formats that perform well in e-commerce may need different price ladders and claims, while small impulse packs must justify their presence through convenience and incremental occasions rather than hidden per-unit margins.

Sustainability and packaging regulation

Environmental pressures and regulation on packaging waste are reshaping what packs are feasible or acceptable. Lightweight materials, refill systems, and concentrated products all alter the relationship between pack size, perceived value, and price. PPA work increasingly incorporates sustainability metrics and compliance constraints alongside cost and demand considerations. A format that looks attractive financially may no longer be viable once extended producer responsibility fees or recycling scheme requirements are factored in.

Capabilities and organisational implications

Turning pack-price architecture into a repeatable advantage is as much about organisation as it is about analytics. Leading CPG companies embed PPA within cross-functional RGM teams that combine insights, finance, sales, marketing, and supply chain. They invest in:

- Granular data on sell-out, promotions, and competitor packs at store and channel level.

- Advanced analytics for elasticity estimation, scenario modelling, and optimisation.

- Governance processes for approving new packs, price changes, and SKU rationalisation, with clear financial criteria.

- Joint planning routines with retailers to align architectures with category strategies and shopper missions.

Crucially, they treat pack and price decisions as part of brand strategy rather than purely operational tweaks. The architecture expresses how the brand shows up for different households, at different incomes, across different occasions. When done well, it reconciles penetration growth, affordability, and sustainable margin expansion into a single coherent design.

Enduring relevance

As consumer goods markets mature and volume growth slows, revenue growth increasingly depends on mix, pricing sophistication, and the intelligent use of pack formats. Pack-price architecture provides the language and toolkit for that task: a disciplined way to map where value is created or destroyed across the shelf, and to reconfigure the portfolio accordingly.

The underlying questions it poses are enduring. How much should a brand ask a consumer to pay at each occasion? How much product should that consumer receive, in what form, and with what implicit promise of value? And how can those answers vary across retailers and channels without fragmenting the brand or confusing shoppers? As long as these questions remain central to FMCG and CPG economics, pack-price architecture will remain a core strategic capability, not a passing technical fad.

Quote: Leo Tolstoy - Russian writer

"If you make it a habit not to blame others, you will feel the growth of the ability to love in your soul, and you will see the growth of goodness in your life." - Leo Tolstoy - Russian writer

Conflict in private life or public affairs rarely escalates because facts are unknowable; it escalates because no one wants to own their share of responsibility. The reflex to search for a culprit before seeking understanding turns ordinary friction into resentment, then into estrangement. What changes when that reflex is deliberately interrupted is not only the emotional tone of a situation but the structure of the self: the person who stops blaming others begins to experience agency differently, to relate to power differently, and to participate in social life in a fundamentally altered way.

The moral psychology of blame

Blame functions as a psychological defence before it becomes a moral judgment. It lets an individual protect a fragile sense of worth by projecting fault outward. Tolstoy, reflecting on human behaviour, noted that those who are quick to denounce others are equally quick to imagine that they themselves would never have fallen into the same error . This projection preserves pride, but it does so at the cost of honesty. Once pride stands guard, it becomes hard to learn from failure or to empathise with weakness.

The habit of blaming others therefore reinforces a dual illusion: that one is uniquely virtuous and that one is uniquely victimised. Both illusions shield the ego but deform character. By dividing the world into perpetrators and sufferers, blame narrows the moral imagination. It encourages a person to scrutinise others with a microscope while regarding their own motives through frosted glass. Over time that asymmetry makes genuine self-knowledge almost impossible.

The deeper problem is that blame is not only a judgment about a past action; it is a forecast about a person's essence. When someone is labelled hopelessly selfish, stupid, or malicious, the label implies that change is unlikely. That narrative kills curiosity: instead of asking what pressures, fears, or misperceptions shaped the behaviour, one settles for an easy story about immutable character. In that sense, blame is a subtle renunciation of hope about other people.

Accountability without accusation

To abstain from blaming others is not to deny responsibility altogether. The tension Tolstoy explored in his work lies precisely here: how to hold individuals accountable without indulging in punitive moralism. In his reflections on history and leadership, he argued that leaders operate within vast webs of necessity, custom, and circumstance that limit their freedom of action . A similar insight applies in ordinary life: people act under constraints that are rarely visible from outside.

Renouncing blame in this sense means shifting from a courtroom model of morality to a diagnostic one. Instead of asking who deserves censure, one asks what combination of motives, incentives, ignorance, and pressure produced the outcome. The point is not to excuse harm but to understand its causes so that it can be prevented or repaired. Responsibility remains, but it becomes a matter of owning contributions and making amends rather than allocating shame.

Such an orientation also changes how people speak to each other. An accusatory sentence - "you always" or "you never" - tends to provoke defensiveness. A statement that begins with a description of one's own part in the conflict, and then names the impact of the other's actions, opens more space for dialogue. The discipline of not blaming forces a person to separate observation from interpretation, and impact from intent. That mental discipline is close to what Tolstoy regarded as moral seriousness: a commitment to truth that begins with truth about oneself.

The growth of love as moral capacity

Tolstoy treated love not as a fleeting emotion but as a demanding moral disposition: a sustained, practical goodwill toward others, grounded in humility. In his later writings he contrasted love with the pursuit of power, arguing that love of power is bound up with pride, cunning, and cruelty rather than with genuine goodness . Love, for him, required a different inner architecture - one that blame quietly corrodes.

When a person gives up the habit of blaming, something counterintuitive happens: vulnerability increases, and yet the capacity to love expands. Without blame, the immediate inward move when hurt is not to strike back or to justify oneself but to ask what the other might have been experiencing. That question does not erase the hurt, but it reframes it within a larger field of mutual frailty. Love becomes possible because the other is no longer reduced to the role of offender.

This growth is experienced as a change in attention. Instead of replaying grievances, the mind starts to notice small acts of kindness, efforts at repair, and moments of courage in others. The more these are consciously observed, the more they shape one's expectations. Over time, love in this Tolstoyan sense is less about warm feeling and more about a stable orientation: a readiness to see in others not only their faults but their capacity for goodness.

The habit of not blaming also breaks the symmetry between how we interpret our own failings and how we interpret those of others. People typically explain their mistakes in terms of circumstances while reading others' mistakes as evidence of character flaws. Love reverses this asymmetry: it becomes more willing to interpret the other charitably while insisting on lucidity regarding one's own motives. That reversal is morally costly; it requires the surrender of moral superiority. Yet it is precisely this surrender that Tolstoy linked with spiritual growth .

Goodness as a visible pattern of life

Goodness, in Tolstoy's moral vocabulary, is never an abstract property; it is something that becomes visible in the texture of daily life - in how people respond to insults, manage power, and treat those from whom they have nothing to gain . The claim that goodness "grows" in a person's life when they stop blaming others suggests that visible patterns of behaviour follow from subtle internal shifts.

Consider a household where the prevailing culture is one of blame: every mishap triggers a search for the guilty party; apologies are weaponised; criticism outpaces gratitude. In such an environment, cooperation is fragile and creativity is timid. By contrast, in a household where individuals take responsibility for their part without rushing to condemn, errors become occasions for learning and mutual support. Over months and years, the second pattern of life will appear markedly "better" in any reasonable moral sense: there will be more trust, fewer simmering resentments, and greater resilience under stress.

Tolstoy's point can be read as an early intuition about feedback loops in moral life. Choices about inner attitudes accumulate into characteristic ways of acting; those actions reshape relationships; those relationships, in turn, reinforce or undermine the original attitudes. Once blame is minimised, the loop begins to amplify patience and generosity instead. Goodness becomes less a matter of isolated heroic acts and more a matter of stable dispositions that others can rely on.

Biographical undercurrents in Tolstoy's moral thought

The authority of this line of thought in Tolstoy's writing is strengthened by its biographical roots. His own life was marked by intense inner conflict, pride, and remorse. Accounts of his spiritual crisis describe a man tormented by the discrepancy between his ideals and his privileges, by the gulf between the simplicity he admired and the aristocratic estate he inherited . He knew from within how self-justification could masquerade as moral insight.

In "A Confession", Tolstoy acknowledged that he had once taken comfort in ideals that conveniently justified his personal whims . That admission is the opposite of blame: it is a public owning of self-deception. When he later extolled humility and warned against spiritual pride, he did so as someone painfully aware of his own capacity to rationalise wrongdoing. The exhortation to refrain from blaming others arises, then, not from naivety about human evil but from a sober realism about his own.

His admiration for peasants and for religious communities that tried to live out non-violence and mutual aid also shaped his view. Observers have noted how he pointed to such communities as examples of "real Christians in action" who worked hard, refused to kill, and tried to embody their moral convictions in daily practice . Their lives suggested to him that goodness was less about correct doctrine and more about habits of compassion and non-retaliation. The rejection of blame fits naturally into that ethic of unarmed love.

Blame, power, and historical responsibility

There is a further, more political dimension to the issue. In his philosophy of history, Tolstoy attacked the cult of great men and the assumption that a small elite of leaders single-handedly directs historical events . He argued instead that events emerge from countless small decisions and structural conditions. To focus blame exclusively on individual leaders, in his view, is to misunderstand how history works and to absolve entire societies of their complicity.

This does not mean that leaders bear no responsibility. Rather, it means that blaming them as isolated villains allows everyone else to escape scrutiny. Citizens can denounce a tyrant while ignoring the habits, fears, and incentives that made tyranny possible. The refusal to indulge in simple blame pushes moral inquiry deeper: what educational systems, economic arrangements, and cultural myths enabled the injustice? How did ordinary people participate, through action or passivity?

Tolstoy's analysis resonates with modern debates about systemic injustice. If wrongdoing is embedded in institutions, then moral progress demands more than replacing one set of leaders with another. It requires rethinking the rules and norms that shape behaviour, and recognising that individuals who benefit from unjust systems are also responsible, even if they never directly commit spectacular crimes. Blame, when confined to a few visible figures, can become a way of protecting the system from genuine reform.

Legal blame and moral accountability

Modern legal systems are structurally committed to assigning blame: courts determine liability, apportion punishment, and declare guilt or innocence. Yet even within legal practice, there is growing recognition that systematic blame can undermine genuine accountability . When responsibility is fragmented among institutions, and each actor points to procedure or hierarchy, harm can occur without anyone feeling personally answerable.

Tolstoy was sceptical about the capacity of law alone to secure moral order. For him, the deeper issue was the conscience of individuals. If citizens and officials cultivate the habit of not blaming others, they become less willing to hide behind rules or superiors. They begin to ask what they could have done differently, where they remained silent, and how they might repair damage. That internal stance produces a more robust form of accountability than any external enforcement.

At the same time, there is a danger that talk of refusing to blame can be misused to silence victims. Systems that are already tilted toward the powerful may urge the wounded to "let go" of blame while doing little to constrain the behaviour of oppressors. Any Tolstoyan reading of non-blame therefore has to be paired with a fierce insistence on justice. Forgoing blame as an inner posture does not mean ceasing to name wrongdoing or abandoning the pursuit of restitution.

Objections: does renouncing blame invite abuse?

Critics might worry that if individuals habitually refuse to blame others, they become easy targets for manipulation. Abusers could exploit their reluctance to accuse, shifting all responsibility onto them. Historical experience confirms that appeals to unconditional love have sometimes been used to keep the marginalised compliant. The risk is real and should be frankly acknowledged.

Tolstoy's own life illustrates the tension. His teachings on non-resistance and universal love inspired many, but they also led some followers into practices that neglected their own well-being. The question, then, is how to distinguish between a morally grounded refusal to indulge in punitive blame and an unhealthy tolerance of injustice.

One way to draw this boundary is to distinguish between judging persons and judging actions. A commitment to love may restrain harsh verdicts on another's inner worth, but it does not forbid clear judgments about the wrongfulness of specific behaviours. A person can refuse to condemn an offender as irredeemably evil while still setting firm boundaries, seeking legal redress, or withdrawing from harmful relationships. The renunciation of blame, properly understood, concerns the spirit in which one confronts wrongdoing, not the clarity with which one names it.

Another safeguard lies in reciprocity. The refusal to blame others is powerful only when coupled with an equally strong refusal to excuse oneself. If a moral teaching operates asymmetrically - demanding self-blame from the weak while exempting the powerful - it has been distorted. Tolstoy's own self-critique suggests that he intended his standards to apply first of all to himself and to those with social advantage, not primarily to those already burdened.

Contemporary relevance: blame in the age of outrage

In contemporary digital culture, blame has acquired unprecedented speed and reach. Social media platforms reward indignation; reputations can be destroyed in hours by cascades of moral condemnation. While such public scrutiny has sometimes brought long-hidden abuses to light, it has also normalised a form of discourse in which nuance and patience are liabilities.

Within this environment, Tolstoy's call to make a habit of not blaming others acquires fresh urgency. It invites individuals to resist the seduction of instant judgment, to pause before sharing denunciations, and to consider the humanity of those being targeted. It does not require silence in the face of injustice, but it does require self-suspicion: am I sharing this out of genuine concern for truth and repair, or to satisfy a craving for moral superiority?

At the interpersonal level, families, workplaces, and communities still wrestle with the same dynamics Tolstoy observed. Organisations that foster cultures of blame tend to stifle learning; employees conceal mistakes and innovation suffers. By contrast, cultures that encourage open acknowledgement of error without humiliation tend to be more adaptive and creative. Leaders who model non-blaming accountability - owning their misjudgements and inviting critique - often find that trust and cooperation increase rather than weaken .

Why this moral posture matters

The refusal to blame others habitually is demanding; it asks individuals to move against deeply ingrained instincts of pride and self-defence. Yet the payoff, as Tolstoy and his interpreters suggest, is transformative . As blame recedes, love becomes more than sentiment; it becomes a disciplined way of seeing others. And as love deepens, patterns of behaviour that can reasonably be called "good" - patience, generosity, integrity, courage - begin to appear more consistently in one's life and relationships.

In a world saturated with accusation - political, cultural, and personal - the decision to cultivate this posture is both countercultural and strategically wise. It restores a sense of agency by shifting attention from what others have done to what one can do now. It widens the field of moral concern from the search for culprits to the search for possibilities of repair. And it grounds the hope that human beings, even when they fail badly, remain capable of change.

Term: Joint Business Planning (JBP) - FMCG / CPG

"Joint Business Planning (JBP) in the FMCG/CPG industry is a collaborative, strategic framework where manufacturers and retailers co-create commercial plans, moving away from transactional negotiations to align on shared growth goals. Both parties co-invest in optimized assortments, promotional calendars, and supply chain efficiencies." - Joint Business Planning (JBP) - FMCG / CPG

Pricing disputes, last-minute promotional changes, out-of-stocks, and margin pressure are symptoms of a deeper misalignment between consumer goods manufacturers and retailers. When each side optimises only its own profit and loss, the result is duplicated effort, wasted trade spend, and a fragile relationship that cracks under volatility. Joint Business Planning emerged in fast-moving consumer goods as a way to replace this adversarial pattern with a structured process that links investments on both sides to a single, quantified growth agenda.

From trading terms to shared growth engines

Historically, annual negotiations in FMCG revolved around trading terms: base price, discounts, listing fees, and conditional rebates. Manufacturers committed trade spend in exchange for distribution, shelf space, and loosely defined promotional support. The discussion focused on "how much" rather than "to what end". As promotional intensity rose, both parties found they were spending more but not necessarily unlocking incremental demand or sustainable category growth.

Joint Business Planning (JBP) reorders this sequence. Instead of starting from cost-to-serve and haggling over funding, the conversation begins with category, shopper, and channel objectives and works backwards to the investments, mechanics, and operating model required to deliver them. Trading terms become an enabler of a plan, not the plan itself. This shift is particularly important in FMCG/CPG, where thin margins, high promotional reliance, and retailer consolidation magnify the consequences of misaligned decisions.

In practice, a JBP between a consumer goods company and a retailer is documented in a shared plan that covers business objectives, channel and category roles, assortment and space, pricing and promotion, joint marketing, supply chain and service levels, data and insight sharing, and governance. It typically spans a 12-month horizon, with quarterly reviews and rolling updates, making it a living roadmap instead of a once-a-year spreadsheet exchange.

Substantive definition and scope

Substantively, a Joint Business Plan in FMCG is a structured collaboration process in which a manufacturer and a retailer:

- Define common commercial and category objectives in volume, value, penetration, and share terms.

- Agree a set of initiatives and investments to achieve those objectives, including assortment, merchandising, promotions, pricing, and shopper marketing.

- Clarify roles, responsibilities, and timelines across both organisations, from commercial to supply chain to marketing.

- Establish shared metrics and scorecards to track performance and trigger course corrections.

- Codify these agreements in a single plan that is co-created, signed off, and revisited through formal governance.

Where a traditional trade agreement focuses on "terms and conditions", JBP adds a forward-looking, integrated commercial and operational plan. It is less about who pays for a specific promotion and more about which combination of initiatives yields the highest total return for the category and the joint business.

Practical meaning at the account and category level

For account teams, JBP translates into a structured annual and in-year workflow rather than a single negotiation moment. The typical sequence includes internal alignment, joint discovery, plan design, contracting, and ongoing execution management.

Internal alignment requires the manufacturer to clarify its own brand, channel, and customer strategies before any JBP discussions. This includes understanding the retailer's role in the portfolio (e.g. top 3 growth engine vs marginal account), recent performance, and constraints on trade investment. Retailers undergo a similar process, defining category growth strategies, private-label objectives, and customer experience priorities.

During joint discovery, both sides share views on the "state of the brand" and the "state of the shopper". Manufacturers bring brand equity, innovation pipeline, and media plans; retailers provide performance benchmarks versus peers, shopper demographics, missions, and loyalty or e-commerce data. The goal is not to sell a preset programme but to find overlapping opportunities: under-penetrated shopper segments, emerging subcategories, channel migration, and unmet missions such as quick top-up or health-driven baskets.

Plan design translates these opportunities into specific commercial levers:

- Assortment and space: which SKUs to list, which to rationalise, and how to allocate shelf and digital space by store cluster, with an explicit category role (traffic builder, margin driver, image shaper).

- Promotional strategy: the balance between depth and frequency, mechanics (price cuts, multibuy, bundles), and the alignment with key retailer events and seasonal peaks.

- Pricing and everyday value: a strategy that respects brand positioning, competitive benchmarks, and the retailer's price image, including relative roles for EDLP-style pricing versus high-low tactics.

- Joint marketing: campaigns that combine brand assets with retailer channels (in-store media, CRM, apps, digital banners) and are tailored to shopper missions.

- Supply chain and service: aligned forecasts, minimum service levels, collaborative planning and replenishment, and targeted investments such as shelf-ready packaging or dedicated logistics capacity.

The resulting plan is not just a list of activities but a sequenced calendar linking initiatives to expected commercial outcomes and financial commitments. Execution then becomes a matter of managing variance versus that plan: tracking events, performance, and compliance, and adjusting the programme when shopper behaviour or competitors change.

Core metrics and an analytical backbone

A critical difference between modern JBP and earlier, more relationship-driven collaboration models is the analytical backbone built on data sharing. Retailers and manufacturers increasingly rely on daily or weekly sell-out data, loyalty or panel information, and digital metrics to quantify the impact of their joint actions.

At minimum, a JBP will define target and actual values for key performance indicators such as sales volume, net revenue, gross margin, category share, promotional uplift, and supply chain service levels. More advanced plans incorporate shopper KPIs (penetration, frequency, basket size), media metrics (reach, cost per click, conversion rate), and digital shelf indicators (content quality, availability, search rank).

Analytically, the relationship between investment and outcome is often framed through return metrics. For example, return on marketing investment can be expressed as , where is revenue attributable to a specific joint campaign and is the associated marketing spend. If a joint promotion generates and , then , corresponding to a 300 % return. This kind of calculation allows both sides to compare alternative uses of scarce trade or media budget and to focus the JBP on activities with demonstrable incremental impact.

Similarly, category growth objectives can be framed in terms of baseline and incremental sales. If denotes expected baseline sales without the plan and the incremental volume attributable to JBP initiatives, total planned sales are . The JBP process is, in effect, an attempt to decompose into contributions from assortment, price-pack architecture, promotions, and media, so that investment can be allocated to the most productive levers.

Key parameters and design choices

Designing a robust JBP requires a series of parameter choices, many of which involve trade-offs that reflect each party's risk appetite and strategic priorities.

Time horizon is one such parameter. Most JBPs cover a 12-month period, but some strategic accounts in FMCG operate with rolling, multiyear frameworks to support large investments such as category redesigns or new supply chain infrastructure. A longer horizon enables bolder moves but increases exposure to market volatility and leadership changes.

Investment intensity is another. Manufacturers must decide what proportion of their total trade and shopper budget to allocate to a given retailer, based on that retailer's role in the portfolio, growth trajectory, and relative returns. Retailers judge how much space, promotional support, and data access to provide, balancing one supplier's requests against others and against private-label ambitions.

The degree of data openness sets the ceiling on analytical sophistication. A retailer willing to share near real-time, SKU-level, and shopper-level data enables much richer evaluation of promotional mechanics, cross-category effects, and long-term brand health. Manufacturers, in turn, may share modelling approaches, demand forecasts, and media planning to synchronise investment. However, competitive and privacy considerations mean that JBP rarely involves fully open books; instead, carefully scoped data exchanges and joint analytics are negotiated as part of the plan.

Finally, governance structure determines whether the plan remains active. Leading JBPs define joint steering committees, escalation paths, and regular performance reviews, often monthly for operational KPIs and quarterly for strategic course corrections. Without this structure, the plan risks becoming a static document disconnected from in-store and online execution.

Schools of thought: category-led, retailer-led, manufacturer-led, and data-led

Different organisations and markets have developed distinct philosophies about what JBP should optimise.

A category-led school, influenced by category management, emphasises the category's role as the unit of value creation. The objective is to grow the total category profit pool at the retailer, through better assortment, improved space allocation, and promotions that shift shoppers into higher-value segments rather than simply rotating volume between brands. JBP, in this view, is an extension of joint category planning in which brands accept some cannibalisation in exchange for a larger and more stable category.

A retailer-led school treats JBP primarily as a vehicle to execute the retailer's strategy more efficiently and with higher funding. Here, the retailer defines clear asks around investment levels, support for strategic initiatives (e.g. omnichannel integration, sustainability, private label differentiation), and compliance with standardised processes and scorecards. Manufacturers gain access to better visibility and placement if they align closely with these asks, but they have limited influence over the strategic direction.

A manufacturer-led school, more common where brands hold significant equity or innovation power, uses JBP as a way to secure privileged support for hero SKUs, new platforms, or shopper programmes. The plan is designed around brand growth ambitions, and the retailer is asked to align in return for higher investment, exclusive launches, or differentiated in-store experiences.

The emerging data-led school cuts across these perspectives. Its advocates argue that JBP should start not from power dynamics or broad strategy statements but from granular, shared evidence about what drives incremental value for shoppers and for the joint P&L. Under this approach, hypotheses about assortment, price, and media are tested through controlled pilots; results feed into the next planning cycle, creating a learning loop. This requires both parties to invest in data infrastructure, analytics, and experimentation capabilities.

Tensions and recurring debates

While the rhetoric of partnership is widespread, the practice of JBP in FMCG is shaped by structural tensions that are not easily resolved.

One persistent debate concerns value capture. Even when a plan clearly grows category revenue, the distribution of profit between retailer and manufacturer can be contested. Retailers may view trade funds as a cost of entry and push for higher contributions without guaranteeing equivalent incremental value; manufacturers worry about subsidising baseline sales and eroding brand equity. JBP attempts to solve this through transparent objectives and post-event analysis, but power imbalances and differing internal incentives can blunt its effectiveness.

Another tension lies between standardisation and tailoring. Retail chains seek standardised formats, promotions, and scorecards to manage complexity across hundreds or thousands of stores and dozens of suppliers. Manufacturers, however, want retailer-specific, and sometimes store-cluster-specific, strategies to exploit differential strengths. JBP structures often compromise: a common planning framework and calendar, within which key elements such as assortment, mechanics, and marketing assets are tailored to the retailer's shopper base.

There is also a cultural debate about how collaborative JBP can really be. Some practitioners criticise "paper partnerships" where one side drafts the plan and the other merely signs, undermining the promise of co-creation. Best-practice guidance emphasises building the plan together from the ground up, sharing risks and assumptions, and being explicit about responsibilities and potential conflicts. Yet time pressure, asymmetric information, and negotiation habits mean many JBPs still resemble sophisticated vendor programmes rather than genuine joint ventures.

Measurement is a further source of friction. Agreement on KPIs does not guarantee agreement on attribution. A retailer might credit uplift to its loyalty campaign, while a manufacturer attributes the same gain to a national media burst. Without robust joint measurement frameworks, including controlled tests and shared models, JBP reviews risk devolving into anecdotal scorekeeping instead of evidence-based optimisation.

Why JBP still matters in FMCG/CPG

Despite these challenges, JBP remains strategically important in FMCG/CPG because it offers one of the few mechanisms through which two interdependent but separate organisations can systematically align their decisions in a volatile, low-margin environment.

First, the category and shopper landscape is fragmenting. Growth pockets are shifting towards smaller, more diverse segments, channels, and missions while digital and omnichannel journeys complicate the path to purchase. No single manufacturer or retailer can fully decode these shifts alone. Joint Business Planning provides a forum to combine shopper data, brand insight, and operational capabilities to respond coherently rather than with fragmented, overlapping initiatives.

Second, investment risk is rising. Media fragmentation, inflation, and promotion fatigue make it harder to predict returns on trade and marketing spend. By structuring hypotheses as part of a shared plan, defining expected outcomes, and agreeing in advance how to measure them, JBP allows both parties to take calculated risks instead of playing safe with repeating last year's activities.

Third, execution complexity is increasing. The same brand may run different pack-price architectures across discounters, supermarkets, and e-commerce; promotions might require synchronisation between on-site media, off-site advertising, and in-store activation. JBP offers a way to orchestrate these elements at account level, ensuring that supply chain, digital shelf, field execution, and media plans are mutually reinforcing rather than working at cross purposes.

Finally, JBP matters for organisational learning. The annual planning cycle, when combined with disciplined post-mortems and mid-year reviews, creates a feedback loop in which both sides refine their understanding of what drives incremental value. Over several cycles, this can transform a transactional relationship into a strategic partnership characterised by higher trust, better data sharing, and more innovative joint propositions.

In that sense, the enduring relevance of Joint Business Planning in FMCG/CPG is less about the document created each year and more about the behaviours it fosters: transparency about objectives, shared accountability for outcomes, and a willingness to treat the category and shopper as the true north, even when commercial pressures pull partners back towards zero-sum negotiation.

Quote: John Waldron - Goldman Sachs

"I still think that the vast majority of the price discovery in these transactions will come from the large institutional pools of capital. And so, at the end of the day, that's really the thing that that's going have to go well in the context of making these deals [such as SpaceX, OpenAI and Anthropic] work." - John Waldron - Goldman Sachs

Control over where and how prices are set in late-stage technology financings has become one of the defining questions for the next decade of markets. It determines who captures upside from artificial intelligence breakthroughs, how resilient capital structures will be when sentiment turns, and whether the path from private valuation to public listing remains orderly or becomes a series of dislocations. In the case of AI champions such as SpaceX, OpenAI, and Anthropic, the sheer scale of capital required, the concentration of economic power, and the opacity of private rounds make the locus of price discovery a critical systemic issue rather than a technical footnote.

The underlying tension is simple: AI platforms with global reach need tens of billions in compute, data, and talent investment, yet their equity prices are being inferred from relatively thin slices of secondary trading and bespoke private transactions, not from broad and continuous public markets. The question is whether retail and smaller pools of speculative capital can meaningfully set prices in this environment, or whether large institutional investors will remain the primary mechanism through which valuations are negotiated, disciplined, and transmitted into the wider market. For financial intermediaries that underwrite, structure, and distribute such deals, the answer determines both risk management and revenue opportunity.

From public markets to private price signals

For most of the twentieth century, price discovery for major corporates was anchored in public exchanges, with day-to-day prices shaped by a mix of institutional flows, retail activity, and specialist dealers. The microstructure was noisy, but there was at least a transparent quote for almost every security at almost every moment. Over the past 20 years, however, late-stage venture and growth equity markets have grown to a scale where the most important technology firms can delay public listings for far longer and can raise 5, 10, or even 20 billion in private capital before an initial public offering. That shift has relocated a substantial part of price discovery from open exchanges to private deal rooms.

In those rooms the dominant actors are not day traders but sovereign wealth funds, large asset managers, pension schemes, insurance companies, and major hedge funds. These institutions write cheques measured in billions, not millions. Their internal models, risk committees, and benchmarking processes determine the ranges at which boards are willing to transact. Even when smaller investors participate via feeder funds or structured vehicles, the indicative price is typically set by a handful of large anchors whose willingness to provide capital at a given valuation is decisive.

In principle, public markets might eventually overwrite these private valuations once a listing occurs. In practice, the initial price discovery at IPO is also heavily shaped by institutional demand. Book-building processes rely on indications of interest from a curated list of long-only funds, hedge funds, and other professional investors. Retail orders, even when meaningful in aggregate, usually follow the range signalled by this group. That means the same large pools of capital that dominated late-stage private rounds often remain central in the first liquid price that the market sees.

AI platforms and the capital intensity of scale

The dynamic is amplified in AI. Training frontier models, building global data centres, and integrating AI into industrial and consumer workflows require extraordinary upfront expenditure. Cloud credits and partnership structures can ease the burden, but for independent or semi-independent entities such as OpenAI or Anthropic, the capital stack must still support long-duration research and infrastructure commitments. SpaceX, while a space and launch company rather than a pure AI play, shares a similar profile: massive capex, long development cycles, and complex regulatory risk.

These companies are therefore engaged in a multi-round capital formation process in which each new raise both funds operations and crystallises an implicit valuation step. When a round prices OpenAI or Anthropic at, say, 60 billion or 80 billion, that number is not a market consensus but a negotiated outcome between management and a relatively small syndicate of investors. It may reference comparable multiples from public AI-exposed companies and discounted cash-flow scenarios for long-run monetisation, but the final figure is fundamentally a deal term. Subsequent secondary trades by employees or early backers tend to cluster near that negotiated level, unless a macro shock or major news event forces a repricing.

Because the needs are so large, the marginal buyer that matters in these rounds is almost always institutional. A single sovereign wealth fund or mega-fund can bridge a financing gap of several billions; thousands of smaller players cannot coordinate quickly enough or accept the same information constraints. That concentration of bargaining power means that price discovery is heavily conditioned by the opportunity cost of capital for these institutions, their internal risk constraints, and their forward view on AI adoption curves. Retail enthusiasm in listed AI-adjacent names may inform sentiment, but it rarely changes the cheque sizes or target returns required by the limited partners who ultimately supply the money.

Who John Waldron is, and why his perspective matters

The speaker of the quote occupies a vantage point that is not merely observational but operational. As president and chief operating officer of a major global investment bank, John Waldron oversees a platform that arranges, underwrites, and distributes some of the world's largest equity and debt transactions. His firm advises both issuers such as SpaceX and AI start-ups, and the institutional investors that buy into these deals. It also operates trading, prime brokerage, and asset management businesses that interface with hedge funds, pension funds, and other large pools of capital.

From this position, Waldron is acutely aware of the difference between headline narratives in media or social platforms and the practical conditions under which multi-billion capital commitments are made. When he emphasises institutional pools of capital as the primary source of price discovery, he is effectively describing the way his own franchise operates: block trades negotiated with a small number of lead accounts; structured IPO allocations built around core anchor investors; and private rounds in which a single cornerstone order can validate or derail a proposed valuation.

His perspective is also informed by risk. As a senior executive, he bears responsibility for firm-wide balance sheet usage, reputational risk, and client outcomes. Deals that clear at artificially inflated prices may generate short-term fees, but they also raise the probability of painful down-rounds, post-IPO collapses, and strained relationships with both issuers and investors. Insisting that institutional price discovery go well is therefore not only a prediction; it is a condition for the long-term sustainability of the franchise and, more broadly, for market stability.

The mechanics of institutional price discovery

In the background, price discovery by large institutions combines hard modelling, peer comparables, and soft information from networks and management interaction. Analysts will typically build cash-flow projections under multiple scenarios for adoption, margin evolution, and regulatory constraints. Even when the business is early-stage and cash-flow negative, they attach probabilities to different outcome trees, discount them at hurdle rates that reflect perceived risk, and derive a range of plausible enterprise values. The conversation then becomes one of where in that range the issuer and the investor can agree.

For AI companies, the modelling challenge is acute. Revenue visibility may be limited, with usage-based pricing and platform economics that depend on partner ecosystems still being formed. Compute costs can change rapidly as hardware generations evolve. Regulatory shifts on data usage, safety requirements, and liability could materially alter future profitability. As a result, institutional investors often use scenario-based frameworks. They might consider a base case in which enterprise adoption follows a gradual S-curve, a bull case in which AI rapidly unlocks productivity across multiple sectors, and a bear case in which regulatory or technical setbacks slow deployment.

Within such frameworks, what matters most is not a single point estimate but the sensitivity of valuation to key assumptions. An investor may accept a higher headline valuation if given structural protections such as liquidation preferences, anti-dilution provisions, or governance rights that allow for intervention if execution falters. Conversely, a governance-light structure might require a lower entry price to compensate for the inability to influence outcomes. These trade-offs are rarely visible to public observers yet are central to the effective price paid for risk.

Strategic and technological tensions in AI financing

Price discovery in AI deals is not a purely financial exercise; it is entangled with strategic alliances, sovereignty concerns, and technological competition. Large cloud providers that partner with AI firms may participate in equity financing at valuations that reflect strategic value rather than traditional investment metrics. Governments and quasi-sovereign funds may consider national competitiveness in AI when deciding whether to support domestic champions. These considerations can push prices up relative to what a purely financial analysis would suggest.

For intermediaries, this creates a tension between facilitating strategic capital and maintaining valuation discipline. If one investor is willing to pay a premium to secure preferential access to models or infrastructure, others must decide whether to follow at that price or to walk away and risk being structurally underexposed to a transformative technology. In hot markets, fear of missing out can erode discipline; in cooler periods, risk aversion can overshoot in the other direction. Waldron's focus on institutional pools as the key site of price discovery implicitly recognises that managing this tension requires actors with both the scale and the governance structures to resist the most extreme swings.

There is also a technological feedback loop. High valuations enable AI companies to raise more capital, hire more researchers, and purchase more compute, reinforcing their lead. However, they also set high expectations for growth and profitability that may be difficult to achieve in practice. If institutional investors collectively overestimate the addressable market or underestimate the regulatory drag, a later correction could be sharp. Conversely, if they are too conservative, they risk slowing down innovation or pushing companies to seek funding from less transparent sources.

Debates and objections: can retail and crypto-style markets do more?

Not everyone accepts the premise that institutional capital should dominate price discovery. The experience of meme stocks, cryptoassets, and retail-driven speculative booms has shown that large swarms of small investors, coordinated through digital platforms, can move prices dramatically. Proponents argue that this democratises finance, surfaces diverse information, and challenges potential conflicts of interest among large intermediaries.

However, the suitability of such mechanisms for financing companies like SpaceX, OpenAI, and Anthropic is contested. These businesses require patient capital willing to tolerate long periods of negative free cash flow, complex technical risk, and binary regulatory outcomes. Retail flows are often momentum-driven and may be less tolerant of prolonged volatility or extended lock-ups. Moreover, the disclosures required for public retail ownership may conflict with the competitive need to keep certain technical and strategic information confidential until a more mature stage.

There are also concerns about systemic risk. If retail-driven price discovery sets valuations at levels far above what institutional fundamentals would justify, the eventual correction could erode trust not only in specific names but in AI as an investable theme. This in turn might complicate financing for more grounded, less glamorous AI applications in industry and public services. Institutional gatekeepers, for all their flaws, provide a buffer between sentiment and capital allocation, absorbing information and adjusting positions in a more measured fashion.

Why the success of institutional price discovery matters

When Waldron says that this mechanism has to go well for the deals to work, he is pointing to several layers of dependency. First, the issuers themselves need valuations that are high enough to fund ambitious plans without excessive dilution, but not so high that every future round becomes an exercise in defending a fragile narrative. Second, investors require returns that compensate them for risk, within mandates that are accountable to pensioners, policyholders, and endowments. Third, intermediaries need both sides to feel fairly treated if they are to maintain long-term client relationships and reputational capital.

If institutional price discovery fails - because models prove systematically over-optimistic, governance proves too weak to correct course, or strategic pressures override financial discipline - a wave of disappointing outcomes could follow. Down-rounds in private markets can demoralise employees whose equity is underwater, prompt litigation, and limit future fund-raising options. Public market disappointments, especially after heavily marketed IPOs of AI leaders, can spark political scrutiny, regulatory tightening, and demands for stricter control over both finance and AI development.

Conversely, if it succeeds, the benefits extend beyond the firms directly involved. A credible valuation structure for AI unicorns provides benchmarks against which smaller companies can be priced, informs M&A activity, and helps public investors calibrate multiples for AI-exposed incumbents. It also gives policymakers clearer signals about where private capital sees sustainable value, which can guide decisions on public research funding, infrastructure investment, and education priorities.

The evolving architecture of capital pools

It is also important to recognise that the category of "large institutional pools of capital" is itself evolving. Traditional pension and insurance funds are being joined by sovereign wealth funds from emerging markets, large family offices with institutional-grade capabilities, and specialist AI or technology funds that raise many billions from global limited partners. Some of these actors operate with longer horizons and higher risk tolerance than classic institutions; others are more constrained by domestic politics or public scrutiny.

The result is a more heterogeneous set of views feeding into price discovery. For AI deals, this diversity can be helpful, as it allows issuers and intermediaries to match specific risk-return propositions with compatible investors. Yet it also means that coordination is harder. A single change in regulatory perception or geopolitical alignment can reprice risk for a subset of investors, shifting demand and creating cross-currents in valuations. Managing such complexity is part of why major investment banks emphasise their role as translators between issuer needs and investor constraints.

In this context, Waldron's focus on these pools is both descriptive and aspirational. Descriptive, because they are already the primary providers of bulk capital for AI and space-related deals. Aspirational, because the hope is that their processes, governance, and experience will be robust enough to steer valuations through cycles of hype and disappointment, ensuring that transformative technologies are funded at scales commensurate with their potential without triggering destabilising bubbles.

Implications for future IPOs of SpaceX, OpenAI, and Anthropic

When considering eventual IPOs or other liquidity events for SpaceX, OpenAI, and Anthropic, the present configuration of private price discovery sets the starting conditions. If late-stage rounds have been anchored by rigorous institutional assessment, public market investors may see the transition as a natural extension of existing valuation logic. If, instead, valuations have floated far ahead of fundamentals, the listing event may become a de-rating rather than a celebration.

For SpaceX, questions about launch cadence, satellite broadband economics, and defence and intelligence contracts will shape institutional models. For OpenAI and Anthropic, topics such as model commoditisation, cloud revenue-sharing arrangements, and the balance between safety obligations and monetisation will dominate. In each case, the terms on which institutions are willing to finance the next phase of growth - pre-IPO and at IPO - will reflect how they resolve these uncertainties.

The broader market will watch these processes closely, not only because of the companies' intrinsic importance but because their deals will serve as reference points for a whole generation of AI-related offerings. If the transactions clear smoothly, with robust books and post-listing performance that broadly matches expectations, the template will reinforce the central role of institutional capital in orchestrating innovation financing. If they misfire, pressure will grow for alternative mechanisms of price discovery, whether through more retail participation, more direct state involvement, or new forms of digital asset-based funding.

In that sense, the quality of institutional price discovery for these AI and space leaders is not just a technical matter for bankers and portfolio managers. It is one of the levers through which societies decide how quickly to scale transformative technologies, how to distribute the economic gains they generate, and how to allocate the risks that accompany them. The stakes extend far beyond any single deal.

Term: Jobs to Be Done (JTBD) framework - FMCG / CPG

"In FMCG/CPG, the 'Jobs to Be Done' (JTBD) framework posits that consumers do not simply buy products, but rather 'hire' them to make progress or solve a specific problem in a given circumstance. By shifting focus away from traditional customer demographics and toward the functional, emotional, and social motivations behind a purchase, this consumer-centric philosophy uncovers unspoken needs." - Jobs to Be Done (JTBD) framework - FMCG / CPG

Most FMCG categories suffer from crowded shelves, marginal differentiation, and relentless price pressure. Yet some brands cut through, commanding loyalty and price premia despite offering near-identical features. The underlying driver is rarely a clever flavour extension or an eye-catching pack; it is whether the product helps people reliably achieve the progress they are seeking in a specific situation. That progress may be small and mundane, like getting out of the door faster on a weekday morning, or more identity-laden, like feeling like a competent, caring parent. When teams fail to decode these underlying goals, they mistake surface behaviours for true demand, and innovation becomes guesswork.

From categories and consumers to situations and progress

Conventional FMCG strategy starts with consumer segments and product categories: young urban families in the breakfast cereal market; health-conscious millennials in beverages; value-seeking shoppers in household cleaning. Demographics and attitudinal segments remain useful, but they often say little about what triggers an actual purchase at a specific moment or why people switch between options that sit in different categories but compete for the same use case. A rushed commuter may grab a chocolate bar, a protein shake, or a pastry from the same shelf because the underlying objective is to stay energised and avoid hunger until lunch, not to consume a particular category.

The Jobs to Be Done perspective reorganises this picture around circumstances and progress. Instead of asking "Who is our consumer?" brand teams examine recurring situations in which people feel a gap between where they are and where they want to be. The gap might concern a functional outcome (clean a kitchen quickly), an emotional state (feel proud when guests arrive), or a social perception (avoid seeming careless or unhygienic to others). Products become tools people "hire" to bridge that gap; rival products, and even non-consumption, become alternative ways of getting the job done.

This shift has several practical implications for FMCG and CPG businesses. Category boundaries become less important than competitive sets defined by shared jobs. Research designs evolve from measuring likes and preferences to mapping decision contexts and constraints. Innovation briefs move away from "develop a premium variant" and toward "help parents reduce weekday breakfast stress within 5 minutes and without extra washing up". Marketing stories pivot from describing product features to narrating how the brand fits into real-life scenarios of progress.

The substance of a "job" in FMCG

A job is not a vague desire for a product, nor a loyalty to a brand, but a description of the progress someone wants in a given situation without assuming any specific solution. In the FMCG context, jobs typically combine three intertwined dimensions:

- Functional: what concrete outcome the person needs, such as removing stains, satisfying hunger, freshening breath, or soothing a child's skin.

- Emotional: how they want to feel during and after the event, for example reassured, in control, pampered, or less guilty.

- Social: how they wish to be perceived by others, such as being seen as a good host, a responsible parent, or a savvy, eco-conscious shopper.

Consider a laundry detergent bought before a family gathering. The core functional job may be "ensure clothes look and smell clean", but there is also an emotional component (avoid the anxiety of being judged) and a social dimension (signal that the household is well kept). A budget detergent that excels only on functional cleanliness may underperform if it fails to support the emotional and social aspects of the job as experienced in that circumstance.

Crucially, the same consumer can "hire" very different products for distinct jobs across the week. A shopper might buy a budget bulk coffee for weekday mornings (job: "wake up sufficiently and save money") and a premium single-origin pack for weekends (job: "create a small ritual of pleasure and self-treating"). Demographic data remains identical, but the job, context, and evaluation criteria change significantly.

Decomposing jobs: outcomes, steps, and metrics

One influential stream of JTBD thinking distinguishes between the job as a process and the outcomes consumers use to assess success. In this view, a core functional task can be broken into sequential steps that describe how people try to get it done, and each step has associated desired outcomes that act as metrics.

For example, the job "get a quick, satisfying weekday breakfast" can be mapped into steps such as planning, shopping, storing, preparing, consuming, and cleaning up. Each step involves specific success criteria: minimise time to prepare, reduce mess, avoid hunger before lunchtime, avoid complaints from children, limit sugar intake, and so on. From a JTBD standpoint, these desired outcomes effectively define the spec against which consumers judge competing solutions.

Although day-to-day FMCG work rarely requires heavy formal mathematics, this outcomes view can be expressed conceptually. Let denote a particular job and denote the set of desired outcomes associated with that job. For a given product , a consumer intuitively evaluates performance against each outcome, yielding perceived scores for . The overall suitability of for job can be thought of as a function , where is the perceived utility of "hiring" that product for the job.

Different shoppers and situations weight these outcome-scores differently. A busy parent might care more about "time to prepare" and "mess", while a fitness-focused individual emphasises "satiety" and "nutritional profile". Explicitly articulating outcomes and their relative importance allows CPG teams to see where existing offerings overserve on some dimensions and underserve on others, creating space for repositioning or new propositions.

Parameters that define a job in FMCG practice

In applied CPG work, several parameters usually define and differentiate jobs:

- Situation: when and where the job arises (weekday breakfast at home, late-night snacking on the sofa, cleaning just before guests arrive, on-the-go hydration during commuting).

- Trigger: what event or feeling initiates the job, such as hunger, embarrassment about odours, worry about germs, time pressure, or boredom.

- Constraints: budget limits, time windows, dietary restrictions, household equipment, and store availability that shape what is realistically hireable.

- Desired outcomes: the specific functional, emotional, and social end states by which success is judged.

- Existing alternatives: not just other brands in the same category, but cross-category solutions and workarounds, including non-consumption (skipping breakfast, using water instead of a specialised cleaner).

Capturing these parameters requires methods that observe or reconstruct behaviour in context: in-home interviews, shop-alongs, usage diaries, and ethnographies that probe not only what is purchased but what was considered, rejected, or never even noticed. Quantitative research can then size how common particular jobs and outcomes are, estimate their frequency and economic value, and link them to observed purchasing behaviours.

Major schools of thought within JTBD

Within the broader Jobs to Be Done landscape, several schools of thought influence how FMCG teams apply the concept.

One stream, often associated with Tony Ulwick, emphasises jobs as stable functional tasks and uses a structured needs framework. It distinguishes types of customers (job executors, those supporting the product lifecycle, and buyers) and breaks jobs into steps to surface measurable desired outcomes. This approach tends to lead to detailed job maps and quantitative opportunity scoring, where teams systematically assess which outcomes are underserved or overserved and prioritise innovation accordingly.

Another stream, linked to Clayton Christensen, puts more weight on the narrative of hiring and firing solutions. It focuses on switching moments, the stories of when people stop using one solution and adopt another, and the emotional and social frictions that drive this change. In this view, understanding the "job" is closely tied to understanding the circumstances under which people re-evaluate their options and reconfigure their routines.

A third, more design-oriented strand emphasises qualitative discovery in context. It treats JTBD as an empathy-building lens embedded into design research, experience mapping, and service blueprints. Consumer researchers and UX teams use job narratives to identify friction points in the whole experience of buying, using, and disposing of FMCG products, not just in the moment of consumption.

These perspectives are complementary rather than mutually exclusive. CPG organisations often blend them, using qualitative hiring stories to define jobs and then applying a structured outcomes framework to quantify the opportunity for new products, pack formats, or claims.

Debates, tensions, and misapplications

Because the JTBD label has become popular, practice varies widely and has generated several debates relevant to FMCG.

Job definition scope. One tension concerns how broad or narrow a job should be. Defining a job too narrowly ("open this particular type of pouch") can reduce it to a feature-level task and obscure more strategic insights. Defining it too broadly ("live a healthier life") yields aspirations too diffuse to guide specific innovation. In CPG, a useful middle ground tends to be the recurring scenario that is stable enough to design for but specific enough to expose concrete trade-offs, such as "provide a quick after-school snack that feels like a treat but is not seen as unhealthy".

Functional bias vs emotional and social layers. Another debate arises around the balance between functional and non-functional elements. Some interpretations foreground functional steps and metrics, risking underestimation of emotional and identity-driven motivations, which are often decisive in personal care, baby care, and indulgent food categories. Others focus so much on emotional storytelling that they lose the operational precision needed to design packaging, formula, or merchandising that reliably changes behaviour. Robust FMCG work tends to insist that every job description explicitly include all three dimensions.

Jobs vs segments. JTBD thinking sometimes invites the claim that traditional segmentation should be discarded. In practice, FMCG teams usually need both. Jobs describe situations and progress; segments describe clusters of people who are more likely to experience particular jobs, hold certain outcome priorities, and accept specific constraints. Ignoring segments can create propositions that are conceptually strong but economically weak if the job occurs rarely within high-value consumers. Ignoring jobs leads to segments that are easy to describe yet hard to activate because they do not map to moments of decision in store or online.

Over-simplification. A further risk is treating the "hire" metaphor literally and looking for a single dominant job per product, when many FMCG items serve multiple jobs across occasions. A multi-pack of snack bars may be used for children's lunchboxes, adult office snacks, and emergency on-the-go meals, each with different outcome priorities. Forcing a one-job narrative can blind teams to profitable secondary jobs or to tensions between them that require separate variants or pack designs.

Why the framework still matters in FMCG and CPG

Despite waves of new methodologies and data sources, JTBD remains influential because it tackles a persistent gap: the difficulty of linking observed behaviour to underlying motivation in a way that is actionable for product, packaging, and marketing decisions.

First, it improves innovation hit rates by grounding ideas in recurring jobs rather than abstract category trends or technology push. When teams start from a carefully researched job, they can articulate design specifications in terms of outcomes: for example, reducing the variance of preparation time, limiting the number of steps, or increasing perceived control in mess-prone tasks. Even without formal equations, thinking in terms of outcomes and constraints forces clarity on what a new product must improve and what trade-offs are acceptable.

Second, it reframes competition. From a jobs lens, a ready meal competes not only with other ready meals but with takeaway, meal kits, and the decision to skip cooking entirely. A fabric freshener may compete with full washing cycles and with changing clothes. CPG players that understand this can identify under-served jobs where their categories barely feature today and design offerings that insert the brand into those occasions.

Third, it enhances communication and branding. Marketing built around jobs answers the question "when and why should I think of this product?" by showing recognisable situations and the progress achieved, instead of listing generic features. This orientation aligns with the view that effective marketing tells the story of what the product does for the consumer and how it fits into their life, not just what it contains or how it is made.

Fourth, it provides a shared language across functions. R&D, insights, brand, design, and sales teams can align more easily around well-defined jobs than around abstract brand values. A statement such as "we are targeting the job of helping young professionals assemble a healthy weekday lunch in under 8 minutes with minimal cognitive effort" is clearer for packaging designers, product developers, and trade marketing than a broad aspiration like "own health-conscious convenience".

Incorporating JTBD into FMCG research and decision-making

Embedding JTBD thinking in CPG organisations usually involves layering it onto existing research and development processes rather than replacing them.

- Qualitative discovery. Teams use in-home or digital diaries, accompanied shops, and immersion sessions to uncover recurring jobs, triggers, and workarounds. Interviewers focus on specific episodes: "Tell me about the last time you..." rather than abstract preferences, and probe what other solutions were considered or tried.

- Job definition and mapping. Insights and cross-functional teams synthesise narratives into a concise job statement, a stepwise job map, and a list of desired outcomes. They explicitly separate functional, emotional, and social components to avoid collapsing them into a single label.

- Quantitative sizing. Surveys or behavioural data are used to estimate how many people experience each job, how often, what they currently hire, and which outcomes are most important yet least satisfied. Opportunity scores can be computed by combining prevalence, importance, and dissatisfaction measures, helping prioritise where to innovate first.

- Concept development and testing. New product ideas, pack formats, or claims are evaluated against the defined job and outcomes: does the concept improve key outcomes without worsening others beyond acceptable trade-offs? Concept tests can explicitly frame scenarios that match the job to ensure respondents evaluate the proposition in the right context.

- Ongoing refinement. As products launch, consumption data, reviews, and further qualitative feedback are used to update the job understanding. Shifts in culture, technology, and retail environments may create adjacent jobs or change constraints, prompting iteration.

Done consistently, this approach cultivates a portfolio view of jobs rather than just of categories, allowing CPG businesses to track where they are strong or weak across the spectrum of everyday situations in which consumers seek help.

Enduring relevance in a changing marketplace

Digital grocery, direct-to-consumer brands, and subscription models have expanded how people access FMCG products, but they have not changed the basic reality that purchase decisions are rooted in attempts to make discrete progress in specific circumstances. If anything, the explosion of choice and information makes it more important to anchor innovation and marketing in clearly understood jobs. Algorithms can optimise assortments and promotions, but they still require a human understanding of what problems products solve and for whom.

By focusing on what people are genuinely trying to accomplish, how they evaluate success, and what stands in their way, the Jobs to Be Done lens continues to offer FMCG and CPG teams a disciplined way to cut through noise. It redirects attention from demographic stereotypes and feature lists to lived situations, trade-offs, and progress, keeping consumer packaged goods grounded in the everyday lives they are meant to serve.

Quote: John Waldron - Goldman Sachs

"If there are problems in software, it will first start to show up in the riskiest part of the capital structure, which is equity. So that stands to reason. To me, too much press has been spilled on private credit and not enough focus on the fact that there's a whole bunch of equity underneath that that is the first loss position, and that's where you probably want to pay the most attention earliest." - John Waldron - Goldman Sachs

Stress in software and technology financing rarely appears first where commentary is loudest. It tends to surface where contractual protection is weakest, cash flow sensitivity is highest, and valuations have drifted furthest from fundamentals. In contemporary capital structures supporting sponsor-backed software companies, that weak link is typically common equity, not the private credit sitting above it . Understanding why requires tracing how software businesses were capitalised through the low-rate era, how private credit stepped into the vacuum left by banks, and why equity investors quietly became shock absorbers for the entire structure.

The factual backdrop is a decade in which recurring-revenue software became the poster child for aggressive leverage. Ultra-low policy rates, abundant venture and growth equity, and a wave of sponsor-led buyouts combined to push valuation multiples to levels that assumed durable double-digit growth and minimal cyclicality. Private credit managers rushed to fund these deals, confident that high gross margins and subscription models would protect cash flows, while covenant packages were progressively loosened. Recent research from Goldman Sachs notes that roughly 23 % of the private credit market is now tied to software loans, a concentration that even industry insiders characterise as excessive by historical standards . Against that backdrop, the remark that problems will show up in equity first is not a reassurance so much as a reminder of how the hierarchy of pain actually works.

The speaker, John Waldron of Goldman Sachs, sits at the junction of several relevant vantage points: advisory to private equity sponsors, underwriting and distribution of leveraged finance, and a rapidly expanding private credit platform that now manages roughly USD 145 billion in private credit exposure . The firm has reorganised its global banking and markets unit into a Capital Solutions Group designed explicitly to intermediate across financing, origination, structuring, and risk management for corporate and sponsor clients . That reorganisation underscores an institutional bet that privately negotiated credit and hybrid financing will remain central to the funding of software and other growth sectors, even as public markets remain volatile.

The substantive claim embedded in the remark is straightforward: equity is structurally the first-loss tranche in a typical leveraged capital structure. Debt instruments in private credit deals are generally senior secured, with priority claims on assets and cash flows, contractual interest payments, and in many cases maintenance covenants that give lenders the right to intervene early. Equity, by contrast, has no contractual claim to cash, absorbs valuation volatility, and can be diluted, written down or wiped out without triggering defaults on the debt. When a software company encounters operational or growth problems, the first place the stress is visible is equity pricing, sponsor mark-downs, and downward revisions of forward projections, long before a payment default appears in the loan book.

To see this more formally in a finance framework, one can model the equity of a levered firm as a call option on the enterprise value. Let denote the enterprise value at time , the face value of debt maturing at , and the risk-free rate. In a structural model, equity can be represented as:

where is the cumulative normal distribution and:

Here is the volatility of enterprise value. This framing makes Waldron's intuition transparent. As rises or expectations for fall due to weaker software fundamentals, equity values react sharply because they are effectively out-of-the-money or near-the-money options. The debt value, especially when senior secured, is comparatively insensitive until the probability that becomes meaningful. In other words, early tremors show up in the equity option; the credit claim is hit later.

Software business models appeared to offer unusually favourable parameters for this option-like equity: high gross margins, recurring revenue visibility, and scalable cost structures. Sponsors extrapolated these characteristics into aggressive capital structures, assuming that revenue growth and pricing power would sustain interest coverage and allow rapid de-leveraging. Private credit funds, competing to provide unitranche and first-lien loans, often underwrote based on forward-looking pro forma EBITDA and contractual renewals rather than historic downturn performance. As long as software multiples remained high, equity cushions looked deep and reassuring.

However, that apparent cushion is acutely sensitive to small shifts in top-line growth and discount rates. Consider a simplified discounted cash flow representation for a software firm where enterprise value is approximated by:

with representing next period free cash flow, the perpetual growth rate and the discount rate. For high-growth software companies priced as if is only marginally below , even modest reductions in or increases in can trigger sharp contractions in . If the firm carries substantial debt , the equity value can be compressed or eradicated long before the debt principal appears at risk. That is the mechanical reason why equity is where deterioration manifests first.

The post-pandemic tightening cycle intensified this sensitivity. Rising base rates increased funding costs, while inflation and wage pressures squeezed operating margins. At the same time, growth expectations for many software companies were revised downward as customers rationalised licences and delayed digital transformation projects. The equity markets reacted quickly: multiples for many high-growth names compressed, IPO windows partially shut, and late-stage venture funding became more selective. Yet the private credit portfolios financing the same companies often looked stable on the surface because borrowers continued to make interest payments and because portfolio valuations lagged public market repricing.

Waldron's criticism that commentary has focused too heavily on private credit and too little on the equity beneath reflects this asymmetry in information and narrative. Private credit is relatively opaque: positions are not traded with public price discovery, covenants are confidential, and managers disclose performance with a delay. This opacity invites concern that a hidden bubble may be building among software loans. But in many leveraged software platforms, the true point of fragility is not the senior secured loan paying a 7 % to 9 % cash coupon; it is the equity slug that funded the acquisition at a 20x revenue multiple based on aggressive growth plans. When those plans falter, equity valuations must be recalibrated, and sponsors may find themselves injecting additional capital, selling assets, or accepting marked-down exits.

The creation of Goldman Sachs's Capital Solutions Group is a strategic response to exactly this environment . By combining financing, origination, and risk management, the firm is effectively positioning itself to manage the full stack of capital from senior loans through mezzanine to equity and preferred instruments. That multi-layered vantage point reveals where risk truly sits in a structure. For a typical sponsor-backed software company, the capital stack might include a senior secured term loan, a revolving credit facility, possibly a second-lien or mezzanine tranche, and a substantial equity contribution from the sponsor and management. The private credit piece grabs headlines because of its size and growth, but the equity is what first absorbs the impact when revenue growth slows, churn rises, or new competitors erode pricing.

The technological context also matters. Software lending has been buoyed by the perceived defensiveness of subscription revenue and the rise of software-as-a-service in mission-critical functions. Yet the AI wave has created new uncertainty. On the one hand, incumbents may face disruption as AI-native competitors undercut them or offer better features. On the other, AI-driven productivity tools promise efficiency gains, but also require capital expenditure and strategic repositioning. Goldman's own commentary on AI investment expectations suggests that it anticipates continued robust capital expenditure on AI infrastructure, particularly led by US hyperscalers . For software firms dependent on those platforms, changes in pricing, infrastructure costs, or competitive dynamics can rapidly alter unit economics, again feeding through to equity valuations long before credit metrics breach covenants.

Debate around private credit risk often centres on whether underwriting standards have weakened and whether concentration in sectors like software is dangerous. Critics argue that covenant-lite structures, aggressive leverage multiples, and a lack of mark-to-market transparency could store up trouble. Proponents counter that private credit managers have tighter relationships with borrowers, better information rights, and the ability to work constructively through periods of stress. Waldron's emphasis subtly reframes the debate: even if some stress does emerge in software-linked private credit, it is unlikely to be the first or primary place where losses accumulate. Instead, equity investors in highly levered software deals are already experiencing a reset that will change the risk profile of the debt above them.

From a risk-transfer perspective, this is simply the pecking order of claims operating as designed. In a stylised capital structure, suppose a software company has enterprise value , senior debt , junior debt , and equity . If operational issues reduce to , the entire decline hits equity first, leaving while both tranches of debt remain fully covered. Only once do junior lenders face principal impairment, and senior lenders are not at risk until falls below . Empirically, that means that by the time private credit portfolios start to show meaningful realised losses, equity investors are likely to have endured a prolonged period of write-downs, down-rounds, and unfavourable exits.

One objection to the argument is that equity valuations in private markets are themselves opaque, so relying on equity as an early warning signal may not be straightforward. Sponsor marks can lag reality, especially where there is no transaction to force a repricing. However, several practical indicators exist: reduced bidding for new software assets, an increase in broken auction processes, widening gaps between buyer and seller expectations, more frequent use of structured equity solutions, and sponsors injecting additional preferred equity to support over-levered portfolio companies. These behaviours are the qualitative signs that equity is absorbing stress. Lenders involved in capital solutions work will see these signals well before a payment is missed on a private credit instrument, which strengthens Waldron's case that attention should be directed there.

Furthermore, software as a sector amplifies equity risk because its intangible asset base offers limited hard collateral. When lenders underwrite against recurring cash flows and customer contracts, recovery values in a default scenario are highly uncertain. Source code, customer lists, and data have value, but often far less than the optimistic projections baked into the original deal. This makes the presence of a deep equity cushion more critical: equity holders are the ones effectively underwriting the uncertainty in those intangible values. A thin or rapidly eroding equity layer should therefore be a red flag not only for sponsors but for lenders who have relied on that buffer as part of their loss-absorbing structure.

The comment also points to a broader narrative tension: the media appeal of a looming private credit crisis versus the less dramatic but more probable story of prolonged equity pain and restructuring in software portfolios. Predicting a systemic private credit event is headline-grabbing, yet the more subtle reality may be a drawn-out process of sponsor-led recapitalisations, minority stake sales, and operational turnarounds that gradually reconcile inflated entry valuations with more modest cash flow outcomes. For institutions like Goldman Sachs, which straddle advisory, lending, and asset management, the objective is not only to avoid losses but to position themselves as indispensable intermediaries in this adjustment process.

In that sense, the expansion of Goldman's private credit and alternatives capabilities is both a risk and an opportunity . On the one hand, increased exposure to software-heavy private credit portfolios means the firm is deeply intertwined with the sector's fortunes. On the other, the ability to deploy fresh capital into recapitalisations, structure preferred equity or convertible instruments, and engineer liability management transactions allows it to influence where value ultimately settles in the capital structure. If equity investors have already absorbed substantial losses, new capital providers can negotiate favourable terms, effectively resetting the stack in their favour.

For allocators evaluating private credit funds with material software exposure, the practical implication of Waldron's point is clear: analysis cannot stop at lender protections and yield. It must extend to the resilience of the equity beneath. Key questions include the entry valuations at which sponsors acquired assets, the magnitude and terms of any subsequent equity injections, the extent of operational improvements realised versus pro forma, and the degree to which AI and other technological shifts may disrupt core customer value propositions. Lenders who merely look at historical default and recovery statistics for software lending without interrogating these equity dynamics risk underestimating tail risk.

Equally, for software company executives and boards, the message is that equity market discipline is already back. The era when abundant private capital would fund perpetual growth without close scrutiny has faded. As AI changes cost structures and competitive moats, and as higher rates persist, equity will continue to bear the brunt of experimentation and missteps. Private credit remains available, but is likely to price risk more discriminately and insist on clearer visibility into how equity sponsors will support businesses through volatility.

The broader significance of this perspective lies in its reminder that capital structures are ecosystems. Focusing on a single layer in isolation, whether equity or private credit, can be misleading. In software-heavy portfolios, where cash flows are promising but uncertain, and technological trajectories are in flux, the interaction between layers determines where losses land and where new value is created. By insisting that scrutiny turn first to the equity that stands in the first-loss position, Waldron is not exonerating private credit from risk; he is demanding a more sophisticated conversation about where fragility actually resides and how it will propagate through the stack over time.

References

Sonali Basak, "Goldman's AI Expectations" (LinkedIn analysis of Goldman Sachs commentary on AI and infrastructure spending).

Institutional Investor, "Goldman Expands Private Credit Ambitions With Major Overhaul".

ETF Database, "Scaling RIA Growth: The Goldman Sachs AI Playbook".

Goldman Sachs Press Release, "Goldman Sachs Announces Creation of Capital Solutions Group".

Goldman Sachs Asset Management, "Private Equity" product overview.

Goldman Sachs Asset Management, "Private Credit" product overview.

Goldman Sachs Research, "Cracks in Private Credit" (redacted report on sectoral concentration and leverage in private credit).

Private Debt Investor, "Goldman Sachs chases demand for private credit".

Term: Tertiary sales (also known as sales-out or offtakes) - FMCG / CPG

"In the FMCG/CPG industry, tertiary sales (also known as sales-out or offtakes) represent the final step in the distribution chain where an end consumer purchases a product from a retailer. Unlike primary sales (manufacturer to distributor) or secondary sales (distributor to retailer), tertiary sales reflect true market demand." - Tertiary sales (also known as sales-out or offtakes) - FMCG / CPG

Misjudging consumer demand in fast-moving categories rarely fails quietly. A misread signal at the end of the chain can cascade upstream into over-stretched factories, bloated distributor stocks, trade schemes that burn cash, and retailers losing patience with the brand. In high-velocity fast-moving consumer goods, the real constraint is not production capacity but the ability to align inventory and activity with how shoppers actually buy, store by store and week by week.

The distribution ladder: from invoice flow to consumption

In consumer packaged goods with high turnover, the supply chain is typically structured around three distinct rungs of sales measurement. At the top sit manufacturer invoices to channel partners, often referred to as primary sales; these transactions determine factory load, credit exposure and reported revenue in many organisations. One level down, secondary sales capture the movement from distributors or wholesalers into retail outlets, indicating how well the channel is absorbing stock. The final rung, and the one most tightly coupled with economic reality, is the moment when shoppers take product off the shelf and pay for it - the set of transactions described as tertiary sales, sales-out or offtakes.

This distinction matters because the three levels can move out of sync for extended periods. A manufacturer can report robust primary sales while distributors are quietly accumulating excess inventory, or retailers can be pushing units through at an accelerating pace while upstream players are slow to replenish. In both cases, the misalignment arises when decision-making is anchored on the wrong rung of the ladder. Tertiary data is the only layer that cannot be sustained by push tactics for long; it reflects the intersection of price, promotion, availability, competition and shopper behaviour in real time.

Why tertiary sales approximate true demand

FMCG and broader CPG categories are characterised by high frequency of purchase, low unit value and short shelf life for many products. The low involvement nature of these purchases means that shoppers switch easily in response to changes in price, visibility or availability. In such an environment, the volume leaving retail shelves is the closest observable manifestation of the underlying demand curve at the prices and conditions prevailing in that period.

Primary and secondary flows can be distorted by a range of mechanisms: quarter-end loading to meet targets, aggressive trade promotions that incentivise forward-buying, adjustments to credit limits, or even errors in demand planning. These interventions can inflate shipments without any corresponding change at the checkout. Conversely, tertiary sales cannot be inflated sustainably by upstream tactics; once retailer back rooms and household pantries are full, incremental shipping ceases to translate into additional offtake.

For this reason, tertiary measures often sit at the core of FMCG performance frameworks alongside market share, product penetration and share-of-wallet metrics that all depend on observed consumption rather than ship-in volumes. When category data is available at panel or scanner level, it is tertiary sales that allow brands to benchmark their performance relative to the total market, diagnose whether they are winning through penetration gains or increased loyalty, and understand the effectiveness of in-store execution.

From concept to practice: what counts as tertiary sales?

In practical terms, tertiary sales comprise all shopper purchases mediated through retail and other points of sale for a given SKU, channel and period. This may be captured through retailer EPOS data, independent scanner services, distributor sell-out tracking tools, or survey-based consumption panels. Each source brings different levels of granularity and reliability, but all aim to measure the same fundamental event: consumer offtake.

At a store level, tertiary data is often broken down by SKU, pack type and promotional status, and can be mapped against merchandising metrics such as on-shelf availability, number of facings, and share of shelf. By combining sales-out with these indicators, a brand can distinguish between an offer that is not selling despite strong visibility and one that is constrained by out-of-stock incidents or insufficient space. For instance, an SKU that sells strongly wherever it is listed but has low distribution and share of shelf indicates an opportunity for heavier listing and planogram negotiation rather than a price or concept problem.

In emerging markets, where formal EPOS coverage is limited, field sales applications and distributor management systems attempt to approximate tertiary sales by capturing order patterns from individual outlets and reconciling those with inventory movements. Although this introduces additional noise, the intent remains the same: to understand how fast stock is turning at the last mile, not merely how much has been pushed into trade channels.

Mathematical specification and linkage to other KPIs

Because tertiary sales sit at the heart of many FMCG KPIs, it is useful to formalise some of the core relationships. Let denote the tertiary volume of SKU in period , measured in units or cases. A simple identity relates stock and flows at store level:

where represents secondary sales (deliveries into the store). Rearranging highlights how tertiary sales can be inferred when stock counts and deliveries are known:

At brand level, volume market share over a given geography and period is commonly expressed as:

where the numerator aggregates tertiary sales for all SKUs belonging to the brand and the denominator sums tertiary sales for the entire product category in the same universe. A similar relationship can be written for value share by replacing volumes with sales value at retail prices.

Penetration - the proportion of households purchasing a product in a period - is another pivotal measure derived from tertiary transactions. If denotes the number of households that bought at least one unit of the brand during period , and represents the total number of households in the market, then:

From a forecasting perspective, planners often approximate future tertiary sales using time-series models where is expressed as a function of its own history, promotional flags, seasonality indicators and explanatory variables such as price and distribution. While the exact specification can vary, the goal is to predict with enough accuracy to set production and shipment plans, avoiding both stock-outs and excessive inventory.

Parameter meanings and drivers of tertiary demand

The parameters that influence tertiary sales in FMCG tend to cluster around four broad domains: consumer demand, retail execution, competitive dynamics and macro factors. Consumer demand parameters include underlying category need states, household income, demographic structure and cultural consumption patterns. Retail execution parameters cover numeric distribution, weighted distribution, on-shelf availability, shelf space allocation, price compliance and adherence to promotional plans.

Competitive dynamics introduce additional parameters such as the breadth and depth of competitor portfolios, their pricing strategies, and their promotional pressure over time. Macroeconomic variables - inflation, employment levels, input cost shocks - can compress or expand category volumes, particularly in discretionary sub-segments of CPG. A coherent tertiary sales model must either explicitly include these parameters or ensure they are absorbed by appropriate fixed effects and seasonal adjustments.

Through the lens of these parameters, practitioners often decompose changes in tertiary sales into building blocks: distribution gains, increased velocity per store, pack mix shifts, price increases, and promotion uplift. For example, if a brand grows its tertiary volume by 10 % over a year, the decomposition might reveal that 6 percentage points came from more stores carrying the product, 3 from higher units per store per week, and 1 from enhanced promotional responsiveness. This analytical discipline keeps focus on structural drivers rather than headline volume alone.

Schools of thought: primary-led vs tertiary-led management

Within FMCG organisations there are broadly two philosophies regarding which rung of the distribution ladder should anchor decision-making. A primary-led approach centres on factory shipments and distributor offtake; targets, incentives and planning are most heavily tied to these figures. This school emphasises asset utilisation, credit recovery and internal revenue recognition. It tends to dominate in environments where visibility to retail data is limited or fragmented.

A tertiary-led approach, by contrast, treats sales-out as the primary object of management. Here, the central question is not how much has been shipped, but how much is leaving shelves relative to potential. Success is defined in terms of market share, household penetration, on-shelf availability and perfect order rates, with primary and secondary flows treated as consequences of getting demand generation and execution right. Where EPOS data is rich, this philosophy can permeate everything from annual planning to daily store-level actions.

Hybrid models also exist, particularly in markets where some modern trade retailers provide detailed sell-out feeds while traditional trade remains opaque. In such cases, companies might manage modern trade by tertiary metrics and traditional trade by a blend of secondary sales and retail audit estimates, while slowly increasing the coverage of sell-out measurement tools. The tension between the two schools manifests in debates about incentive structures: should sales teams be rewarded for shipments, for offtake, or for a weighted combination of both?

Key debates and tensions around tertiary focus

One recurring debate concerns the reliability and ownership of tertiary data. Retailers may be reluctant to share detailed sell-out information, or may provide it in inconsistent formats with quality issues. Even when high-quality data is available, there can be disputes over the timing and inclusion of returns, voids or substitutions. Organisations that place heavy weight on tertiary measures must invest in robust data governance, alignment on definitions, and reconciliation processes between sell-in and sell-out views.

Another tension arises from the lag between investment and observable impact at tertiary level. Trade promotions, new distribution openings and marketing campaigns often aim to shift shopper behaviour, but their effects may take several cycles to manifest in stable offtake patterns. If tertiary metrics are used too rigidly for short-term performance evaluation, they can discourage experimentation and lead to under-investment in long-term brand-building activities that primarily influence future demand.

There is also a practical concern about attribution. Changes in tertiary sales are the net outcome of many moving parts - price, promotion, shelf execution, assortment, competition and macro conditions. Over-emphasising tertiary performance without a structured framework to dissect causes can create a culture of reactive firefighting. For example, a sudden dip in offtake may prompt hurried price discounting when the underlying issue is a competitor gaining an additional facing in a key retailer or a supply chain disruption causing intermittent on-shelf unavailability.

Finally, in some regulatory environments, the point at which revenue can be recognised for accounting purposes is tied more closely to primary sales than to tertiary offtake. Finance and commercial teams must therefore balance the external reporting requirements anchored in shipments with the internal management need to understand and influence end-consumer demand. This duality is a source of ongoing debate about which metrics should drive bonuses, budgets and strategic plans.

Tertiary sales as the organising KPI for execution

When used thoughtfully, tertiary sales provide a unifying thread connecting diverse operational and strategic KPIs. At the most basic level, store-level offtake is linked to merchandising measures such as on-shelf availability, out-of-stock rate, number of facings, and presence of point-of-sale materials. If tertiary volume is below expectation, these metrics are the first ports of call in diagnosing execution gaps: is the product present, visible, priced correctly and supported by appropriate materials?

At a higher level, tertiary sales underpin market share and penetration analyses that guide portfolio strategy. Brands can segment outlets by their offtake profiles, distinguishing high-potential stores with under-developed sales from low-potential stores that are already saturated. Field teams can then be routed and incentivised based on their capacity to unlock incremental tertiary volume in priority outlets, measured by uplift in sales-out rather than just the number of visits or orders logged.

From the finance and supply chain perspective, tertiary data supports more accurate demand planning, better inventory turnover and improved order fulfilment rates. Understanding how quickly stock is selling at retail, and how that varies by region and channel, enables more precise allocation of production and more responsive replenishment. This reduces both the risk of stock-outs, which directly suppress tertiary sales, and the risk of obsolete inventory that must be discounted or destroyed.

Why tertiary sales still matter in a digitising CPG world

The increasing digitisation of commerce and data flows might suggest that the distinction between primary, secondary and tertiary sales is becoming less salient, but the opposite is occurring. As online grocery platforms, quick-commerce players and direct-to-consumer channels proliferate, the number of nodes at which traditional FMCG products meet the end consumer is expanding. Each of these nodes generates its own variant of tertiary data: app-based transactions, subscription replenishment streams, click-and-collect orders, and more.

For brands, this explosion of touchpoints intensifies the need for a coherent view of tertiary sales that transcends channels. Without a consolidated understanding of how much product is actually being consumed, portfolio and pricing decisions risk being skewed by pockets of over or under-performance in specific routes to market. The same product may exhibit very different elasticity and promotional responsiveness online versus offline, and tertiary data is the primary mirror in which these differences become visible.

Moreover, consumer expectations of availability and service levels continue to rise, with shoppers increasingly intolerant of stock-outs or misleading online stock indicators. Meeting these expectations demands planning systems that treat tertiary demand as the starting point and work backwards to determine optimal primary and secondary flows. As sustainability concerns grow, there is also mounting pressure to minimise waste from over-production and expired stock - a task that can only be tackled effectively when the cadence of actual consumption is well understood.

In this sense, tertiary sales retain their relevance not merely as a reporting metric, but as the organising principle for how FMCG and broader CPG businesses design their supply chains, structure their incentives, and evaluate their success. The closer decision-making is tethered to genuine offtake, the less room there is for illusions created by loading the channel or chasing short-term shipment spikes. In categories defined by high velocity and thin margins, that discipline is often the difference between durable category leadership and an impressive but fragile volume story that unravels when the shelf, and the shopper, are finally heard.

Quote: John Waldron - Goldman Sachs

"Ultimately, I don't know what [AI] means in terms of our headcount and how many people are doing which jobs. I'm sure there'll be some new jobs that get developed, but one thing I'm pretty convinced of is that our really capable, smart people are going get more capable... Hopefully that means our client relationships become stickier, the value proposition becomes stronger." - John Waldron - Goldman Sachs

Large banks are treating artificial intelligence less as a discrete project and more as a pervasive change in how work is organised, priced and delivered to clients . In this shift, the most consequential decisions are not about which model to deploy, but about how to reconfigure the relationship between scarce human expertise and increasingly capable machine systems. John Waldron's comments sit precisely in this tension: the technology is advancing faster than any credible workforce forecast, yet the competitive edge of a firm like Goldman Sachs still rests on human judgement, relationships and trust, not just on compute.

In practice, this means senior leadership must hold two apparently conflicting ideas at the same time. On the one hand, there is genuine uncertainty about how many roles will be automated, reshaped or created as AI diffuses through trading, investment banking, risk, operations and wealth management. On the other hand, there is a very specific conviction: the people who are already top performers will gain disproportionate leverage from these tools, amplifying their impact with clients. The quote does not duck this contradiction; it treats workforce numbers as a contingent outcome while treating talent amplification and client value as the non-negotiable strategic horizon.

The backdrop is an industry-wide arms race. Capital markets and investment banking are data-dense domains where milliseconds, marginal insights and slightly better client service translate directly into fee pools and franchise value. Firms are experimenting with large language models, agentic systems that can orchestrate tasks across multiple internal platforms, and domain-specific models trained on proprietary research and transaction data. Waldron's framing reflects an awareness that simply swapping humans for machines misses the deeper opportunity: using AI to turn each high-value banker, salesperson, risk manager or wealth adviser into a far more productive and differentiated node in the client network.

One way to read the remark about not knowing what AI means for headcount is as a rejection of simplistic automation narratives. Banks have lived through several waves of technology-induced transformation: the move from floor to electronic trading, the rise of algorithmic execution, the digitisation of retail banking, and the first generation of robotic process automation in operations. Each time, job counts moved in complex ways. Some roles vanished, others morphed, and entirely new categories - from electronic trading quants to cybersecurity teams - emerged. AI promises a wider, deeper transformation, but its labour market effects are still non-linear and hard to predict with precision.

Yet the conviction that the most capable people will become more capable is not just comforting rhetoric. It is grounded in how AI tools actually work at scale inside a financial institution. Generative models and AI agents are particularly good at reducing search costs, summarising complex information, drafting first versions of documents, and running scenario analyses that would previously have taken days. For a high-performing banker or portfolio manager, this does not replace the job; it changes the mix of time spent between low-value tasks and high-value tasks. Instead of manually synthesising dozens of analyst reports, regulatory filings and market moves, they can spend more time on structuring bespoke solutions, negotiating, and reading subtle client signals.

In that sense, AI functions as a force multiplier on human capital. It compresses the information-gathering and analysis cycles while leaving the judgment-laden, relationship-heavy decisions to people. This is particularly true in contexts where the stakes are high and the outcomes are path-dependent: complex M&A transactions, large capital raisings, cross-border restructurings and bespoke hedging strategies. Even the most advanced models struggle with accountability and with understanding the political, cultural and interpersonal nuances that govern whether deals succeed. Senior leaders at firms like Goldman Sachs therefore have an incentive to push AI hardest into the parts of the stack where repeatable pattern recognition and calculation dominate, while reinforcing the human ownership of outcomes.

That is also where the idea of "stickier" client relationships enters the picture. In investment banking and institutional sales, stickiness is not just about price or product; it is about the client's sense that their adviser understands them better than anyone else and can move faster and more creatively on their behalf. AI, deployed well, can support this in two ways. First, it enhances institutional memory: systems can surface prior deal structures, historical interactions, risk incidents and bespoke constraints instantly, making each interaction feel more tailored. Second, AI agents can coordinate execution across trading, research, legal, risk and operations, reducing friction and error. When a client's experience shifts from cumbersome and bureaucratic to anticipatory and seamless, they are less inclined to shop around.

However, this potential is contingent on a careful balancing act between automation and authenticity. If clients perceive that their supposedly "trusted" relationship is mediated primarily by bots, templated responses or generic recommendations, trust erodes. Waldron's emphasis on making capable people more capable implicitly recognises this. The strategic bet is that AI will remain largely invisible at the surface of the relationship, embedded behind the scenes in analytics, workflow orchestration, compliance checks and personalisation engines, while the main interface remains a human team. The more that team is equipped with timely insights, contextual prompts and operational support, the more distinctive their service feels.

Factual context also matters. Goldman Sachs has been investing in technology infrastructure and data platforms for years, building the plumbing needed for more advanced AI deployments. Initiatives in transaction banking, electronic execution, consumer platforms and internal developer tooling have all contributed to a landscape where data is more accessible and systems more modular than in a traditional, siloed bank. Public commentary from the firm's leadership emphasises not only generative AI but also narrow models embedded in risk management, fraud detection and operational resilience . This context helps explain why the uncertainty is about people rather than about the technology itself: the leadership is betting that the technical foundations are already good enough to support ambitious AI use cases.

There is also an internal cultural dimension. Investment banks are talent-driven organisations, and senior management must signal to their workforce how to feel about AI. If the message were primarily about cost cutting and role elimination, the effect would be corrosive, driving away exactly the kind of high-initiative employees who are most needed to integrate AI into real workflows. By contrast, Waldron's framing positions AI as a capability enhancer for "really capable, smart people". This is not just flattery; it is a way of aligning incentives. The people most motivated to experiment with, and adopt, new tools are those who can see a direct line from that adoption to their own performance and client impact.

Yet sceptics will point out that large financial institutions are unlikely to ignore the cost-reduction potential of automation. Operations, middle-office functions, compliance checks, trade processing and certain forms of research are all ripe for productivity gains. Over time, this will almost certainly translate into fewer people doing some categories of work. The unresolved question is whether the new roles created - AI product managers inside banking divisions, data stewards, governance specialists, model risk experts, prompt engineers embedded in deal teams - will be enough to offset the roles displaced. Waldron does not pretend to know the net number, and that humility is itself telling: credible leaders today avoid making precise headcount forecasts about technologies whose adoption curves and regulatory constraints are still in flux.

Strategically, the remark hints at a broader shift from thinking about "jobs" to thinking about "tasks" and "capabilities". Even if a job title, such as equity research analyst or loan operations specialist, persists, the task composition of that job may change radically. Routine tasks are more exposed to automation; non-routine, interpersonal and judgment-heavy tasks are more likely to be augmented. From a management perspective, the right question is no longer "Which jobs will disappear?" but "Which tasks can we automate so that scarce human attention is reallocated to higher-value activities?" AI agents that can execute multi-step workflows across internal systems accelerate this decomposition of work into modular components.

For example, in a simplified depiction of a workflow, a human banker today might spend a significant share of time assembling data, populating pitch books, and manually checking constraints across risk, legal and tax. With AI agents orchestrating these steps in the background, the banker's time is reweighted towards designing creative structures, rehearsing negotiation angles, and cultivating the client relationship. The measurable productivity of that banker - deals originated per year, win rate on mandates, revenue per head - could rise substantially. From the firm's perspective, it becomes rational to concentrate more resources around such high-leverage individuals, further reinforcing the idea that the best people become even more central, not less.

This naturally raises concerns about inequality within the workforce. If AI disproportionately augments those already at the top of the performance distribution, while automating away more routine work, the result could be a more polarised organisation with a smaller middle. Junior staff might worry that the traditional apprenticeship model - learning by doing the grunt work - will erode if AI handles many of the foundational tasks. Leaders need a plan for how to maintain skill development pathways in a world where a model can generate 80 % of the first draft and a set of agents can assemble the data room overnight.

One plausible response is to redesign training and progression so that early-career professionals are explicitly taught how to supervise, critique and improve AI outputs. Instead of spending years perfecting the mechanics of spreadsheet work or drafting, they might move more quickly into roles that require critical thinking, risk awareness and client interaction, with AI handling the routine mechanics under their supervision. This could make the profession more, not less, intellectually demanding from the outset, but it also demands a higher standard of digital literacy and model awareness from new hires. Institutions that manage this transition well will likely find themselves with a more capable and adaptable talent base.

The external regulatory environment adds another layer of complexity. Banks operate under intense scrutiny regarding model risk, fairness, data privacy and operational resilience. Any large-scale use of AI, especially generative models, must be embedded in rigorous governance frameworks: model validation, bias testing, audit trails, human-in-the-loop controls, and clear lines of accountability when things go wrong. This governance overhead can slow down the deployment of AI into certain decision-making processes, keeping humans formally "in charge" in ways that preserve many existing roles. Over time, however, as regulators become more comfortable with well-governed AI systems, the frontier between human and machine decision-rights may shift, with corresponding effects on headcount.

There is also a competitive dynamic among clients themselves. Many of Goldman Sachs's corporate and institutional clients are deploying AI aggressively inside their own organisations. They will expect their financial counterparties and advisers to understand AI-driven business models, valuation dynamics, and operational risks. For a banker, salesperson or risk adviser, being "more capable" means not only using AI tools internally but also engaging credibly with clients on their AI strategy. This creates a feedback loop: the more AI reshapes the real economy, the more valuable are advisers who can straddle both finance and technology, and the more sense it makes to augment those advisers with powerful internal AI systems.

Critics might argue that narratives centred on augmenting smart people risk obscuring legitimate concerns about transparency and accountability. If decision-making becomes a hybrid of human judgement and opaque model outputs, it can be harder for outsiders - clients, regulators, even boards - to know who is responsible. The same tools that make client relationships stickier can also increase information asymmetries, as banks deploy proprietary AI to extract insights from data that clients cannot see. Addressing these concerns will require a mix of explainable AI practices, transparent governance and, importantly, a culture that treats AI as an advisory input rather than an oracular verdict.

Another objection is that emphasising "stickier" relationships and stronger value propositions could be read as a soft way of saying "higher margins" and "greater pricing power". Clients may worry that AI allows banks to segment them more finely, extract more surplus, or cross-sell more aggressively. There is some truth to this: better analytics and personalisation do sharpen commercial strategies. But the counterpoint is that sophisticated clients are not passive; they can benchmark, negotiate and multi-home across providers. In this environment, the institutions that use AI to genuinely improve outcomes - better execution quality, more resilient portfolios, more innovative deal structures - will likely be rewarded with loyalty, while those that use it primarily to squeeze clients may face backlash.

What makes Waldron's comments distinctive is the explicit refusal to present AI as a neat, linear story about job counts. Instead, the focus is on a direction of travel: towards a firm where the human core remains central but is surrounded by increasingly capable digital infrastructure, and where client relationships are deepened through better insight and execution rather than merely defended by incumbency. The underlying bet is that in a market where many players will have access to similar foundational models, the differentiator will not be the model itself but the combination of data, culture, governance and human talent wrapped around it.

For Goldman Sachs, and for its peers, the stakes are high. If they over-automate, they risk hollowing out the very human capabilities that justify premium fees and enduring relationships. If they under-invest, they risk being outcompeted by more agile players, including technology firms encroaching on financial intermediation. Steering between these extremes requires exactly the kind of nuanced view reflected in Waldron's statement: technological confidence paired with organisational humility, and a willingness to let the precise headcount outcomes emerge from a series of experiments rather than from a predetermined spreadsheet.

Ultimately, what matters is not whether the aggregate number of jobs goes up or down by some percentage, but whether the institution manages to re-architect work so that human potential is amplified rather than diminished. If AI makes the best people better, gives more clients access to that enhanced capability, and does so under robust governance, then the technology will have served as a catalyst for a new kind of financial advisory practice. If, instead, it leads to shallow automation, brittle systems and eroded trust, the promise of stickier relationships and stronger value propositions will remain unfulfilled. Waldron's remarks implicitly challenge his organisation - and, by extension, the industry - to aim for the former path, even while admitting that the journey's precise employment contours cannot yet be mapped.

References

Sonali Basak, "Goldman's AI Expectations" (LinkedIn post).