Fast Lead User Identification Framework

Abstract Large portion of product innovation and development is accomplished by customers and only a small segment of the customer population engages in such innovation activities. Empirical research has shown that users in this subgroup, called lead users, tend to experience needs before the rest of the marketplace and stand to benefit greatly by finding solutions to those needs. To meet the challenge of quickly and effectively identifying lead users and uncovering their innovation ideas, the authors propose a fast and systematic approach, called Fast Lead User IDentification (FLUID), utilizing data mining techniques to identify lead users on social networking sites. The paper describes the steps taken to build and optimize the FLUID system to effectively identify lead users on the micro-blogging site Twitter. This entails studies using validated lead user questionnaires resulting in clusters of lead and non-lead Twitter users for a single product. The gathered online user metadata and behavior are then used as training data for the automated system. An overview of data processing techniques and relations to the empirically derived lead user characteristics are presented. Finally, classification algorithms that help to separate lead users from non-lead users are discussed, including optimization leading to the validation of the proposed approach. By making use of data-mining techniques on data rich sites like social networking sites, the FLUID approach minimizes the resource and time costs in identifying lead users and this provides a step towards systematizing the fuzzy-front end of the new product development process.