Every decision-maker wants to know the same thing: what actually drives our results? What factors move the needle on sales, satisfaction, loyalty, or conversion? You can look at correlation tables all day and still not know whether a relationship is causal, incidental, or masked by something else entirely. When multiple variables interact simultaneously — as they always do in real markets — intuition alone won't sort out what matters from what doesn't.
Regression analysis is the statistical method we use at Meraki Research to answer these questions definitively. It quantifies the relationships between a dependent variable (the outcome you care about) and one or more independent variables (the factors you suspect are driving it). The output is a regression equation that tells you not just whether a relationship exists, but how strong it is, in what direction, and whether it holds up when other variables are accounted for.
We've applied regression analysis across virtually every industry and research context — from predicting purchase intent based on brand perception data to modelling how pricing, promotion, and weather interact to drive foot traffic. Our analysts combine deep statistical expertise with market research experience, which means we don't just deliver numbers. We deliver answers your team can act on.
Our Approach: The Meraki Driver Modeling Framework
We built our regression analysis practise around a principle that too many analysts overlook: a technically correct model that doesn't map to business reality is useless. Our Driver Modeling Framework moves through five phases, each designed to ensure the analysis produces actionable, trustworthy results.
Phase 1: Hypothesis Development and Variable Mapping
Before we touch the data, we work with your team to define clear hypotheses. What outcome are you trying to explain or predict? Which variables do you believe influence it, and why? This isn't just a formality — it grounds the analysis in business theory and prevents the fishing-expedition approach that produces statistically significant but meaningless findings.
We map every potential predictor variable, assess its measurement quality, and document the expected direction of each relationship. For example, if you're modelling customer satisfaction in the South Africa market, we might hypothesize that product quality, service responsiveness, and price fairness each positively influence overall satisfaction, while wait times negatively affect it. These expectations guide our modelling decisions throughout.
Phase 2: Data Preparation and Diagnostic Testing
Raw data rarely meets the assumptions that regression analysis requires. We conduct thorough diagnostics: checking for multicollinearity among predictor variables, testing for normality, identifying outliers that could distort results, and confirming that relationships between variables are actually linear (or applying transformations when they're not).
This diagnostic work is where many regression projects go wrong. Skip it, and you end up with coefficients that look precise but are actually unreliable. We treat this phase as non-negotiable.
Phase 3: Model Building and Estimation
We select the appropriate regression method based on your data and objectives. The types of regression we work with include:
- Multiple linear regression for continuous outcomes influenced by several factors simultaneously
- Logistic regression for binary outcomes (purchased vs. didn't purchase, churned vs. retained)
- Ordinal regression for ranked outcomes like satisfaction scales
- Hierarchical regression when you need to test whether adding new variables improves prediction beyond a baseline model
- Stepwise and penalized methods when the number of potential predictors is large relative to your sample
Each method produces a regression equation — a mathematical expression of the relationships in your data. But we go further than the equation. We examine standardized coefficients to compare the relative importance of each predictor, calculate confidence intervals, and test whether each variable's contribution is statistically and practically significant.
Phase 4: Validation and Robustness Checks
A model that fits your current data perfectly but fails on new data is overfit and unreliable. We validate every regression model using holdout samples, cross-validation, or both. We also test for interaction effects — places where two variables combine to produce outcomes that neither would predict alone.
For marketers running ongoing tracking studies, we test model stability across time periods. A driver model that shifts dramatically quarter to quarter is picking up noise, not signal. We ensure the relationships we report are durable.
Phase 5: Interpretation and Strategic Prioritization
The final phase translates statistical output into a prioritization framework your team can use immediately. We create driver importance charts that rank variables by their impact on the outcome, controlling for all other factors. We identify which drivers are "table stakes" (necessary but not differentiating) versus "performance drivers" (where improvement yields the greatest return).
This is where our market research experience matters most. A generic statistician can tell you that variable X has a beta of 0.34. We tell you that improving your staff responsiveness score by one point is associated with a 2.1-point lift in overall satisfaction, and here's what that looks like in terms of retention and revenue. The analysis becomes a strategic tool.
When Regression Analysis Is the Right Approach
Regression analysis fits when you need to:
- Identify drivers of a specific outcome. Whether it's satisfaction, purchase intent, NPS, or revenue, regression quantifies which factors matter most and by how much.
- Predict future outcomes. The regression equation can be used to forecast results under different scenarios — for example, what happens to conversion if you improve your mobile experience score by 10%?
- Control for confounding variables. Unlike simple correlation, regression isolates the unique contribution of each predictor, revealing which relationships hold up when competing factors are accounted for.
- Quantify the impact of marketing actions. Marketers across the South Africa and globally use regression to measure campaign effectiveness, media mix optimisation, and pricing elasticity.
When Regression Analysis Is NOT the Right Fit
- You need to reduce a large set of variables to underlying dimensions. That's factor analysis, not regression.
- You want to group customers into segments based on similarity. That calls for cluster analysis.
- Your data has no clear outcome variable — just a set of attributes you want to understand the structure of.
- You need to model trade-offs among product features and price. Conjoint analysis or discrete choice modelling is purpose-built for that.
What We Deliver
Every regression analysis engagement includes:
- Driver model report with standardized and unstandardized coefficients, significance levels, and explained variance (R-squared). Presented in a business-friendly deck and supported by a full technical appendix.
- Driver importance ranking — a visual chart showing each variable's relative contribution to the outcome, formatted for executive presentations.
- Simulation tool (when applicable) — an Excel-based or interactive model that lets your team input different performance scenarios and see predicted outcomes based on the regression equation.
- Segment-level models when your research supports it. Drivers often differ by customer segment, geography, or product line. We build separate models where the data warrants it and highlight where relationships diverge.
- Actionable recommendations that translate coefficients into strategic priorities. We tell you not just what drives your outcome, but where the biggest opportunities sit given your current performance levels.
Timelines range from 2-4 weeks depending on data complexity and the number of models required. We define scope and deliverables precisely in our initial proposal.
Industry Applications
Retail & E-Commerce: Modeling the factors that drive purchase conversion, basket size, and repeat purchase across online and physical places. We help retailers across the South Africa pinpoint which experience touchpoints yield the highest return on investment.
Financial Services: Identifying the variables that predict customer churn, cross-sell likelihood, and satisfaction in banking, insurance, and wealth management. Regression models help compliance-heavy industries focus limited resources where they matter most.
Healthcare: Quantifying which aspects of the patient experience drive satisfaction scores, treatment adherence, and facility recommendation. Health systems use our driver models to allocate improvement budgets strategically.
Consumer Packaged Goods: Measuring the relative impact of brand equity factors, price perception, and distribution on market share. Regression analysis cuts through the noise of complex brand trackers to surface the levers that move share.
Technology & SaaS: Predicting renewal and expansion based on product usage patterns, support interactions, and onboarding experience. Our methods help tech companies focus their customer success teams on the variables that actually predict retention.
Hospitality & Travel: Modeling the relationships between service attributes, pricing, seasonal factors like weather, and booking behaviour. Regression helps hospitality brands optimise the guest experience with precision.