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.

In a simulation model, you can calculate market shares in two ways. The first-choice model gives the entire "vote" to the alternative the consumer prefers most. The share-of-preference model distributes preference proportionally — if a consumer likes A 60% and B 40%, A gets 0.6 and B gets 0.4 of that person's share.

The difference is not trivial. In categories with exclusive choice — where you buy one product at a time — first choice is often more realistic. In repertoire categories — where the consumer regularly buys several brands — share of preference reflects reality better. Using the wrong model produces systematically incorrect market share forecasts.

Reflect chooses simulation configuration based on the category's purchase behavior. We diagnose first: is this an exclusive choice category or a repertoire category? Then we configure the simulation model accordingly. It sounds obvious but it is rarely done in practice — most use the same model regardless of category.

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First choice vs share of preference vs share of volume — three simulation modes

Key takeaways

First choice: the entire vote goes to the favorite, works for exclusive choice
Share of preference: proportional distribution, works for repertoire buying
Wrong model choice gives systematically incorrect market shares
The category's purchase behavior determines which model is right
Reflect diagnoses purchase behavior before configuring simulation

Example

The same conjoint data was analyzed with both models for a soft drink category. First choice gave the market leader 41% share. Share of preference gave 32%. Actual market share was 33% — the repertoire model was the right choice because consumers regularly vary their soft drink purchases.

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.

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.

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.

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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First choice vs share of preference

Key takeaways

Example

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