Rating Customers According to Their Promptness to Adopt New Products

Databases are a significant source of information in organizations and play a major role in managerial decision-making. This study considers how to process commercial data on customer purchasing timing to provide insights on the rate of new product adoption by the company's consumers. Specifically, we show how to use the separation-deviation model (SD-model) to rate customers according to their proclivity for adopting products for a given line of high-tech products. We provide a novel interpretation of the SD-model as a unidimensional scaling technique and show that, in this context, it outperforms several dimension-reduction and scaling techniques. We analyze the results with respect to various dimensions of the customer base and report on the generated insights.