Stabilizing Customer Preferences for Really New Products through Internal and External Learning the Role of Different Information Combinations

In many situations in B2C as well as B2B environments customers are confronted with products or services they do not have any prior experience with. For some of these products many unbiased sources of information are available. This is not the case, however, for so-called Really New Products (RNP), which defy straightforward classification in terms of existing product categories and create a new category rather than reallocate shares within existing categories. For RNP no source of reference yet exists. One factor which is crucial for RNP success is the stability of customer preferences. Preference stability describes a situation where there is low variance around the range of subjectively acceptable alternatives. As customers possess only limited knowledge about these products, they face substantial uncertainty when trying to predict the future utility of RNP. When market research is used to evaluate RNP, this uncertainty leads to instability in customers’ revealed priorities among design features. Customers have not yet developed stable preferences. Hence, their frame of reference is still highly volatile. This lack of preference stability poses a major threat for companies. If configured or altered based on instable preferences, RNP might not meet true customer needs, which seriously threatens companies’ interest of establishing long-term customer relations by maximizing the long-term satisfaction of their customers. Against this background it is crucial to identify forms of knowledge transfer, which enable respondents to learn about RNP and help them to stabilize their preferences.