Decision Processes

Analyzing and forecasting purchase decisions are central themes in market research. The most important method for analyzing preferences for multi-attribute products and services is conjoint analysis. It allows to infer consumers’ preferences for different product aspects. Fundamental research at GfK Verein has focused on two procedures that put emphasis on psychological plausibility: hierarchical individualized limit conjoint analysis (HILCA) and greedoid algorithms to estimate lexicographic heuristics.

HILCA offers for the first time a commercially usable conjoint instrument that  is capable of handling a large number of product features at the individual level and at the same time delivers valid results. HILCA was developed as a refinement of the traditional additive part-worth utility model.

However, as products become ever more complex, simple heuristics for product evaluation gain in importance. In the fields of cognitive psychology and behavioural economics, lexicographic decision heuristics have received much attention; these enable consumers to make efficient decisions when faced with an increasing numbers of product alternatives and technical features. GfK Fundamental Research is working on new estimation procedures to capture such lexicographic, non-compensatory decision processes.


HILCA assumes that products often have many features. For each consumer, however, only a few, individually different features are essential for purchase decisions.

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Noncompensatory Conjoint

Greedoid algorithms are a method for analyzing conjoint data, assuming non-compensatory, lexicographic heuristics as preference model rather than the standard additive partworth model.

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Holger Dietrich