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Strategy Tools: Pareto (80/20) analysis
Pareto Analysis is a statistical technique for decision making that is used for selecting a number of tasks that produce significant overall effect.1 It is based on the Pareto Principle (the 80/20 rule) which states that by doing 20% of the work you can generate 80% of the benefit of doing the whole job. The Pareto Analysis is named after Vilfredo Pareto, an Italian economist who lived in the late 19th and early 20th centuries. In 1897, he presented a formula that showed that income was distributed unevenly, with about 80% of the wealth in the hands of about 20% of the people.2
The figures 80 and 20 are illustrative; the Pareto Principle illustrates the lack of symmetry that often appears between work put in and results achieved. For example, 13% of work could generate 87% of returns. Or 70% of problems could be resolved by dealing with 30% of the causes. The sum of the two numbers does not need to add up to 100 all the time.
The following conclusions are illustrative of potential Pareto outcomes2:
- 80% of customer complaints arise from 20% of your products or services.
- 80% of delays in schedule arise from 20% of the possible causes of the delays.
- 20% of your products or services account for 80% of your profit.
- 20% of your sales-force produces 80% of your company revenues.
- 20% of a system’s defects cause 80% of its problems.
Fast Facts
While African insurance premiums have been growing they have not kept up with GDP growth
The African insurance industry is predominantly group life insurance business, and due to limited spending power there has been much slower uptake of individual insurance policies.
Poverty has been reduced somewhat in Africa but this is primarily in the lowest income bracket of the middle class who are prone to falling back into poverty.
Furthermore, policyholders are typically unaware or sceptical of the benefits of owning insurance products, they are difficult to reach and often do not earn regular incomes.1
Microinsurance products are growing more quickly – this presents an opportunity for targetting lower income groups.
Selected News
Term: Regression Analysis
“Regression Analysis for forecasting is a sophisticated statistical and machine learning method used to predict a future value (the dependent variable) based on the mathematical relationship it shares with one or more other factors (the independent variables). – Regression Analysis
Regression analysis for forecasting is a statistical method that models the relationship between a dependent variable (the outcome to predict, such as future revenue) and one or more independent variables (predictors or drivers, like marketing spend or economic indicators), using a fitted mathematical equation to project future values based on historical data and scenario inputs.1,2,3
Core Definition and Mathematical Foundation
Regression analysis estimates how changes in independent variables ((X)) influence the dependent variable ((Y)). In its simplest form, linear regression, the model takes the equation:
[ Y = \beta<em>0 + \beta</em>1 X<em>1 + \beta</em>2 X<em>2 + \dots + \beta</em>n X<em>n + \epsilon ]
where (\beta0) is the intercept, (\betai) are coefficients representing the impact of each (Xi), and (\epsilon) is the error term.3,5 For forecasting, historical data trains the model to fit this equation, enabling predictions via interpolation (within data range) or extrapolation (beyond it), though extrapolation risks inaccuracy if assumptions like linearity or stable relationships fail.1,3
Key types include:
- Simple linear regression: One predictor (e.g., sales vs. ad spend).2,5
- Multiple regression: Multiple predictors, common in business for capturing complex drivers.1,2
It overlaps with supervised machine learning, using labelled data to learn patterns for unseen predictions.2,3
Applications in Forecasting
Primarily used for prediction and scenario testing, it quantifies driver impacts (e.g., 10% lead increase boosts revenue by X%) and supports “what-if” analysis, outperforming trend-based methods by linking outcomes to controllable levers.1,4 Business uses include revenue projection, demand planning, and performance optimisation, but requires high-quality data, assumption checks (linearity, independence), and validation via holdout testing.1,6
| Aspect | Strengths | Limitations |
|---|---|---|
| Use Cases | Scenario planning, driver quantification, multi-year forecasts1,4 | Sensitive to outliers, data quality; relationships may shift over time1,3 |
| Vs. Alternatives | Explains why via drivers (unlike time-series or trends)1 | Needs statistical expertise; not ideal for short-term pipeline forecasts1 |
Best practices: Define outcomes/drivers, clean/align data, fit/validate models, operationalise with regular refreshers.1
Best Related Strategy Theorist: Carl Friedrich Gauss
The most foundational theorist linked to regression analysis is Carl Friedrich Gauss (1777–1855), the German mathematician and astronomer whose method of least squares (1809) underpins modern regression by minimising prediction errors to fit the best line through data points—essential for forecasting’s equation estimation.3
Biography: Born in Brunswick, Germany, to poor parents, Gauss displayed prodigious talent early, correcting his father’s payroll at age 3 and summing 1-to-100 instantly at 8. Supported by the Duke of Brunswick, he studied at Caroline College and the University of Göttingen, earning a PhD at 21. Gauss pioneered number theory (Disquisitiones Arithmeticae, 1801), invented the fast Fourier transform, advanced astronomy (predicting Ceres’ orbit via least squares), and contributed to physics (magnetism, geodesy). As director of Göttingen Observatory, he developed the Gaussian distribution (bell curve), vital for regression error modelling. Shy and perfectionist, he published sparingly but influenced fields profoundly; his work on least squares, published in Theoria Motus Corporum Coelestium, revolutionised data fitting for predictions, directly enabling regression’s forecasting power despite later refinements by Legendre and others.3
Gauss’s least squares principle remains core to strategy and business analytics, providing rigorous error-minimisation for reliable forecasts in volatile environments.1,3
References
1. https://www.pedowitzgroup.com/what-is-regression-analysis-forecasting
2. https://www.cake.ai/blog/regression-models-for-forecasting
3. https://en.wikipedia.org/wiki/Regression_analysis
4. https://www.qualtrics.com/en-gb/experience-management/research/regression-analysis/
5. https://www.marketingprofs.com/tutorials/forecast/regression.asp
6. https://www.ciat.edu/blog/regression-analysis/
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