Supporting telecommunication product sales by conjoint analysis

Abstract

Conjoint analysis is widely used as a marketing research technique to study consumers’ product preferences and simulate customer choices. It is used in designing new products, changing or repositioning existing products, evaluating the effect of price on purchase intent, and simulating marketshare. In this work the possibility of conjoint analysis usage in telecommunication filed is analyzed. It is used to find optimal products which could be recommended to telecommunication customers. First, a decision problem is defined. Next, the conjoint analysis method and its connections with ANOVA as well as regression techniques are presented. After that, different utility functions that represent preferences for voice, SMS, MMS and other net services usage are formulated and compared. Parameters of the proposed conjoint measures are determined by regression methods running on behavioral data, represented by artificially generated call data records. Finally, users are split in homogenous groups by segmentation techniques applied to net service utilities derived from conjoint analysis. Within those groups statistical analyses are performed to create product recommendations. The results have shown that conjoint analysis can be successfully applied by telecommunication operators in the customer preference identification process. However, further analysis should be done on real data, other data sources for customer preference identification should be explored as well.