Regression and econometric models estimate how an outcome varies with one or more influences. A simple correlation can look persuasive even when seasonality, selection or another factor explains the relationship. Marketways begins with the business mechanism and data-generating process, then specifies and tests the model against credible alternatives. This shows which relationships are supported and whether the design justifies prediction, explanation or a causal interpretation.
The decision this method supports
We use Regression & Econometric Modelling to help clients answer: Which factors are associated with the outcome, by how much and under which assumptions?
How the method works
Regression estimates how an outcome changes when one or more explanatory factors change. Econometrics adds careful attention to economic reasoning, model structure, identification and the assumptions needed to interpret a relationship. The analysis establishes which conclusions the evidence can support.
A business example
A business may want to know whether advertising increased sales. A simple comparison may confuse advertising with seasonality, price changes or store expansion. An econometric model can represent these factors and estimate the advertising relationship, while making clear whether the result is associative or plausibly causal.
How the client uses the result
Regression and econometrics help a business understand which factors move with an outcome and how strongly they matter. Marketways chooses the specification from the business mechanism, data-generating process and inferential question rather than the result management hopes to obtain. This can improve pricing, investment, marketing and policy decisions by separating supported drivers from background variation and convenient correlation.
What we deliver
We produce estimates, comparisons, intervals, model results and sensitivity tests. A manager should be able to see the size of the result, the uncertainty around it, the assumptions required and which interpretation the evidence does not support.
Limits and complementary methods
Econometric models favour explanation, interpretable assumptions and effect estimates. A flexible predictive model may forecast better, while a regression relationship still needs a credible design before it can be treated as causal.
Selected methods and techniques
We select from these established methods according to the decision, evidence and operating conditions.
- Classification and regression modelling: Use input variables to predict either a categorical outcome through classification or a numerical outcome through regression.
- Driver analysis / regression: Use regression or a related statistical model to estimate how a defined outcome is associated with measured factors while accounting for the other variables represented in the model. State the outcome, population, period, predictors and comparison; document missing data, influential cases, dependence, nonlinearities and subgroup differences; and report model performance, uncertainty and validation. Results depend on the variables and design available, so cross-sectional or self-reported associations do not establish that changing a predictor will cause the outcome to change. The method is reusable across domains: it may populate an Engagement Driver Model for employees or a Satisfaction / Loyalty Driver Model for customers without making those outputs the same object.
- Econometric analysis: Use statistical models informed by economic reasoning to estimate relationships, test hypotheses or make forecasts from economic data.
- Linkage / regression to customer outcomes: Link measures of customers' experiences to later outcomes and estimate how they are related, allowing for relevant customer differences. A relationship in the records alone does not establish cause and effect.
- Panel-data analysis: Analyse repeated observations of the same people, organisations, locations or other entities over time. Use the repeated structure to distinguish changes within an entity from persistent differences between entities, while accounting for common time effects and dependence between observations from the same entity. Define the panel, timing and comparison; inspect missing periods, entry, exit and attrition; and state any weighting or model assumptions. Panel data can strengthen temporal comparison, but repeated observation alone does not establish causality. A series of different samples drawn at each date is repeated cross-sectional data, not a panel.
- Pay-equity regression analysis: Estimate pay differences between groups using regression models that account for specified pay-related factors, such as job, grade, experience, location and performance, and report the remaining adjusted difference with its uncertainty. The factors included must be justified against the question being asked: controlling for grade may be appropriate for estimating within-grade pay differences but can remove disparities arising from unequal access to higher grades when examining the organisation-wide earnings gap. The method identifies patterns requiring investigation and does not by itself prove discrimination.
- Regression / dynamic regression: Fit a regression forecast that relates a time-indexed target to observed, planned or separately forecast predictors and, where needed, to lags, trends, seasonal terms or serially related errors. Define the target, units, population, vintage and horizon, make future predictor paths explicit, use only information available at each historical forecast date and test the model on later periods. Dynamic regression is the broader family of regression specifications that represent dependence through time; ARIMAX is one form that models remaining errors with an ARIMA structure. Coefficients can support prediction without showing that changing a predictor would cause the forecast outcome to change.
- Regression / econometric modelling: Estimate how a clearly defined outcome varies with selected explanatory factors in a stated population and period. Specify the functional form, timing, controls and error structure; inspect missing data, outliers, dependence, instability and model fit; quantify uncertainty; and test performance on later or held-out observations when forecasting. A useful predictive relationship does not show that deliberately changing a factor will change demand. Causal interpretation requires a defensible design and assumptions about confounding, selection and reverse causation. Forecasts outside the observed ranges or after structural change require separate support rather than automatic extrapolation.
- Regression / multilevel modelling: Relate a defined outcome to one or more measured predictors while reporting the size and uncertainty of the relationships. Multilevel modelling extends ordinary regression when observations are nested or repeated, such as assessments within employees and employees within teams, so person and group variation are represented separately. Specify the unit, period, variables, functional form, missing-data treatment and validation. The coefficients are adjusted associations unless a credible causal design justifies an intervention claim.
- Regression / multivariate analysis: Use regression to model one defined outcome from one or more predictors, or use a named multivariate method when several outcomes or variables must be analysed jointly. State the analytical question and technique instead of treating multivariable regression and multivariate analysis as interchangeable. Specify the population, period, outcomes, predictors, timing and comparison; address missing data, repeated observations, collinearity, nonlinear relationships, interactions, influential cases and subgroup differences; and report fit, uncertainty and suitable validation. The result depends on the variables and design represented. An adjusted association can support diagnosis or prediction but is not a causal effect unless a separate causal design and assumptions justify that interpretation.
- Regression testing: Rerun a versioned set of previously passed tests after a model, prompt, data, tool or workflow change to detect lost supported behaviour. Include critical failures and acceptance boundaries and distinguish intended changes from unintended regressions. Passing a regression set protects covered behaviour only; it does not establish that the new feature works or reveal unrepresented failures.
- Structural benchmarking: Compare an organisation's structural features, such as spans, layers, function size relative to revenue or headcount, and the ratio of support to frontline roles, with selected peers or reference data. Adjust for differences in scale, business model and operating context before interpreting gaps. A structural benchmark indicates where to look; it does not determine the correct design.
Parent method family
Statistics & Econometrics explains how this method connects to adjacent methods and relevant services.
Related service families
These service families contain business questions supported by this method. Service pages link to the wider method family so readers can understand the complete analytical approach.