Must Consider the Competitive Landscape
Knowing respondents' part-worth utilities (their preferences) isn't enough to guide effective business strategy. You must take into account the current offerings in the competitive landscape.
For example, if you learned that 80% of the market preferred round widgets and 20% preferred square widgets, which shape widget should you take to market? In the absence of additional information, you'd obviously want to market a round widget. But, if you learned that there were already ten firms offering round widgets in the market and no firms offering square widgets, then you'd probably see greater opportunities in producing a square widget. This illustration shows how knowing buyers' preferences isn't enough to guide effective marketing strategy. You must also incorporate your knowledge of the available offerings. By considering both what people want and what is currently available to satisfy those desires, market simulators can point to opportunities.
Average Utilities Cannot Tell the Entire Story
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.
Summary of Reasons to Conduct Choice Simulations
Some reasons for conducting choice simulations include:
| 1. | Choice 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, choice simulations can capture idiosyncratic preferences occurring at the individual or group level. These "under the surface" 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. | Choice simulations can reveal differential substitution (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. | Choice 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. |