How simulation should adapt to category

The same simulation model does not work in all categories. Purchase process, involvement, repertoire behavior and price sensitivity vary, and the simulation model must reflect that reality.

A common miss in market simulations is applying the same model configuration to all categories. But a car purchase simulation and a soft drink category have fundamentally different decision processes. The car is a conscious, extended choice with extensive information search. The soft drink is a habit-based, low-involvement choice often made impulsively.

Category adaptation affects several dimensions of the simulation model: choice of first choice vs share of preference, price elasticity model (linear, threshold-based, or logarithmic), handling of availability (distribution) as a constraining factor, and whether external effects (advertising, shelf placement) should be included.

Reflect has a category diagnostic that we run before configuring the simulation model. We map involvement level, purchase process, repertoire behavior and price sensitivity. Then we configure the model specifically for the category. It takes more time but produces forecasts that actually match market outcomes.

Key takeaways

The same model does not work in all categories
Involvement, purchase process and repertoire behavior determine configuration
First choice vs share of preference: category-dependent
Price elasticity model should be chosen per category
Category diagnostics before simulation give markedly better forecasts

Example

The same simulation tool was applied to toothpaste (low involvement, repertoire) and insurance (high involvement, exclusive choice). With identical configuration, the toothpaste forecast missed by 8 percentage points. With category-adapted configuration, the forecast landed within 1.5 percentage points.

When conjoint works and when it does not

Conjoint works best when the consumer makes conscious trade-offs between clear attributes. It works poorly in low-involvement categories, with habitual behavior, and when price dominates the decision.

First choice vs share of preference

First choice shows what the consumer picks first. Share of preference shows how preference is distributed. Which metric is right depends on the category's purchase behavior, and they often give entirely different answers.

Individual level, not aggregate

The average consumer does not exist. Simulation models that work at the individual level capture heterogeneity in preferences and give markedly better forecasts than aggregated models.

Reflect simulation model

Our simulation model works at the individual level, is calibrated against observed data, and adapts to the category's purchase behavior. The result is forecasts that hold up, not just in the presentation but in the market.

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How simulation should adapt to category

Key takeaways

Example

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