Identifying Emerging Customer Requirements in an Early Design Stage by Applying Bayes Factor-Based Sequential Analysis

Abstract

Rapid changes of new technologies, market dynamics, and swift fluctuation of customer tastes acerbate the needs for companies to identifying the emerging customer requirements and incorporate them in the conceptual design stage. Discovering emerging customer needs has great potential to create new product opportunities for the success of business. However, identifying customers' requirements in an early design stage has not been well addressed in the traditional design methodology. Because emerging needs are usually not obvious at the budding stage, the related observations are rare in dataset. Traditional methods fall short of providing enough support of eliciting the below-the-radar needs. This paper reports a new approach to identify emerging customer needs in the growing popular online interactive environment with customer participation. The Bayes factor, a methodology to quantify the occurrence possibility of a certain event, is used to calculate the likelihood that current offering cannot meet the customer's requirements whenever a new specification is incorporated. With the sequential input from customers, a series of the Bayes factor value can then be calculated as the weight of evidence that emerging customer needs appear. We show that the decreased value will be the potentially emerging needs which cannot be satisfied by the current product family. Numerical and analytical results are derived to demonstrate the viability and effectiveness of the proposed approach.