Why Conduct Market Simulations?

Looking only at average preferences (part-worth utilities) can mask important market forces caused by patterns of preference at the segment or individual level. Marketers are often not interested in averages, but in the targetable, idiosyncratic behavior of segments or individuals.

For example, consider the following three respondents, and their preferences (utilities) for color:

Utilities for Color

Blue   Red   Yellow

Respondent A       50     40    10

Respondent B        0     65    75

Respondent C       40     30    20

Average:           30     45    35

Looking only at average preferences, we would pronounce that red is the most preferred color, followed by yellow. However, if one of each color was offered to each respondent, red would never be chosen under the First Choice model, yellow would be chosen once, and blue twice — the exact opposite of what aggregate part-worth utilities suggest. While this is a hypothetical example, it demonstrates that average part-worth utilities do not always tell the whole story. Many similar, complex effects can be discovered only through conducting simulations.

Some reasons for conducting conjoint simulations include:

1.

Conjoint simulations transform raw utility data into a managerially useful and appealing model: that of predicting market choice (Share of Preference) for different products. Under the proper conditions, shares of preference quite closely track with the idea of market share — something most every marketer cares about.

2.

As demonstrated earlier, conjoint simulations can capture idiosyncratic preferences occurring at the individual or group level. These "hidden" effects can have a significant impact on preference for products in market scenarios. When multiple product offerings have been designed to appeal to unique segments of the market, capturing such effects is especially important for accurately predicting preference.

3.

Conjoint simulations can reveal differential substitutability (cannibalism/cross-elasticity effects) between different brands or product features. If two brands are valued highly by the same respondents (have correlated preferences), these brands will tend to compete more closely. Product enhancements by one of these brands will result in more relative share being lost by the correlated brand than by other less similar brands within the same simulation. Examining aggregate part-worth utilities cannot reveal these important relationships.

4.

Conjoint simulations can reveal interaction effects between attributes. If the same respondents that strongly prefer the premium brand are also less price sensitive than those who are more likely to gravitate toward a discount brand, sensitivity simulations will reflect a lower price elasticity for the premium relative to the discount brand. A similar interaction effect can occur between many other types of attributes: such as model style and color.

Note when using CBC data: It is important to note that complex effects other than two-way interactions such as cross-effects cannot be reflected using the model of aggregate-level logit offered by our CBC system. Latent Class is a technique for estimating part-worth utilities and reflecting respondent differences at the group/segment level, and CBC/HB (Hierarchical Bayes) is a way to estimate utilities at the individual level for CBC data. Because they are built on individual-level preferences, simulators based on HB models are able to reflect the important and complex behaviors mentioned earlier. It is not surprising that Latent Class and CBC/HB have been shown to outperform similarly-defined aggregate level logit models in terms of predictive validity.

ACA (Adaptive Conjoint Analysis) and CVA (Full-Profile Conjoint Analysis) capture respondent-by-respondent preferences and are thus very useful inputs to this and other market simulation models.