'Listening In' to Find Unmet Customer Needs and Solutions

In many industries, such as the automotive, travel, and health industries, customers routinely use the Internet to gather information on product features and to refine their choice sets. As a result, many websites (e.g., Kelley's Blue Book) now provide virtual advisors to help customers narrow their searches. Customer preference data from these virtual advisors are available at little incremental cost and provide a natural source of ideas for new product platforms. In this paper, we explore a practical marketing research methodology to identify new high-potential unmet-need segments for product development by "listening in" to these ongoing dialogues between customers and web-based virtual advisors. The methodology is designed to be practical when there are large numbers of options (e.g., as many as 150 trucks when studying the automotive market) and many potential customer needs. Calibration is based on activities, interests, and opinions data (AIO) that are collected routinely by many firms. For example, automotive firms collect AIO data periodically for a variety of purposes. Calibration can evolve with incremental data collection as new products enter the market and as new customer needs are identified. The methodology, designed to complement extant methods, uses five modules - a Bayesian virtual advisor, a listening-in trigger, a virtual engineer, a design palette, and a mechanism to identify the underlying unmet-need segments. We also provide an "opportunity-sizing" method which takes product costs into account and provides a rough estimate of the size of the opportunity using available data. The modules are flexible; most can be used with other extant methods. We describe the methodology and examine its properties through formal analysis and Monte Carlo simulation. We demonstrate feasibility by applying the methodology to the pickuptruck category (over 1,000 web-based respondents). The methodology identified two ideas for new truck platforms worth approximately $2.4-3.2 billion and $1-2 billion, respectively.