Driveranalyse

Driveranalyse, or driver analysis, is a statistical approach used to identify the factors that most influence a particular outcome. The goal is to understand which variables drive variation in a metric such as customer satisfaction, sales, or product quality, and to prioritize actions accordingly.

Analysts typically combine data from surveys, transactions, web analytics, and operational systems. Common techniques include regression analysis (linear or logistic), correlation analysis, and more advanced models such as structural equation modeling or SHAP-based explanations to quantify the impact of each driver. In practice, drivers may be continuous or categorical, and the analysis seeks to translate statistical significance into practical importance.

Process steps usually involve defining the outcome variable, gathering and cleaning data, exploring candidate drivers, building a model to quantify the influence of each factor, and validating the model with holdout data or cross-validation. The results are then interpreted to identify actionable drivers, followed by implementing changes and monitoring impact, often through controlled experiments or A/B testing where feasible.

Applications of driveranalyse span marketing analytics to improve campaigns, product management to prioritize features, customer experience to raise satisfaction, operations optimization, and pricing strategy. For example, in a customer satisfaction study, drivers such as perceived value, ease of use, and service quality may emerge as the strongest influencers.

Limitations include that correlation does not imply causation, model quality depends on data and variable selection, and multicollinearity can obscure true effects. Results may not generalize beyond the analyzed population, so driveranalyse is frequently complemented by experimental or quasi-experimental methods to establish causality.