Smartphone customer segmentation based on the usage pattern

The dimension used to measure user heterogeneity plays a key role in the customer segmentation. For most traditional products, customer requirements (CRs) for products are often related to their basic characteristics and psychological characteristics. Therefore, in the traditional market segmentation theory, the dimensions used to distinguish customer differences include the demographic attributes such as gender, age, income etc., or the customer psychology. Customer behaviour, especially the purchasing behaviour, is also used as an important dimension for market segmentation. However, for the smart product like smartphone with rich functionality and multi-interactions, the customer’s interaction preference can be fully released. That makes the heterogeneity of customer stems from the usage characteristics rather than the traditional demographic attributes. Hence, the usage pattern is defined and proposed as the description of the usage characteristics and be used to measure the heterogeneity of customers in this research. The Equivalence CLAss Transformation (ECLAT) algorithm is employed to identify the customer’s APP frequent sets from the operating data and to construct the usage pattern. Thereafter, the customer can be segmented based on the distance among customers’ usage pattern. Compared with the demographic attributes, the usage pattern can provide more reliable and truthful measures for the smartphone customer segmentation.