Constructing the Customer Journey Map of Competitive Brands: A Complex Time-series Analysis

In today's rapidly evolving consumer markets, obtaining a quantitative grasp of the customer journey (the sequence of touch points where customers and brands meet, which is important for marketing strategy) requires analysis of extremely high-dimensional data. Existing studies ignore the effects of touch points of multiple brands that are mutually competitive. We propose to apply a novel method called complex Hilbert principal component analysis (CHPCA) to allow unbiased, model-free analysis, and construct a synchronization network using Hodge decomposition. We apply this method to Japanese beer market data and show that it is suitable for the construction of the customer journey map both within-brand and across brands, the latter reflecting competition among firms. Furthermore, we capture customer heterogeneity by calculating the coordinates of each customer in the space derived from the results of CHPCA. Lastly, we discuss the policy and managerial implications, the limitations, and further development of the proposed method.