Abstract
In this paper we propose a new method of eliciting market research information. Instead of asking respondents for their personal choices and preferences, we ask respondents to predict the choices of other respondents to the survey. Such predictions tap respondents’ knowledge of peers, whether based on direct social contacts or on more general cultural information. The effectiveness of this approach has already been demonstrated in the context of political polling. Here we extend it to market research, specifically, to conjoint analysis. An advantage of the new approach is that it can elicit reliable responses in situations where people are not comfortable with disclosing their true preferences, but may be willing to give information about people around them. A theoretical argument demonstrates that predictions should yield utility estimates that are more accurate. These theoretical results are confirmed in four online experiments.
Highlights
Market research begins by asking potential customers for their personal preferences and intentions, and projects sample data to the entire market
A recent test found that election forecasts based on respondents’ judgments of their social circle voting intentions are superior to forecasts based on own intentions [2]
In this paper we hope to contribute to the body of research in the following way: 1. We introduce peer-choice in binary choice conjoint analysis instead of personal choice, 2
Summary
Market research begins by asking potential customers for their personal preferences and intentions, and projects sample data to the entire market. A recent test found that election forecasts based on respondents’ judgments of their social circle voting intentions are superior to forecasts based on own intentions [2] This method of tapping into the “local” wisdom of crowds is complementary to approaches that attempt to neutralize the common-source or shared information bias, which leverage respondents’ predictions of the averaged survey results [3,4,5]. Conjoint analysis is a method of measuring consumer’s preferences about a product, service, project or policy [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. The total product utility can be decomposed into feature
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