Design and deployment of a customer journey management system: the CJMA approach

Customer journey management has been recently in high demand across organizations as a means of better understanding customer behavior, predicting user needs, and enhancing customer experience to achieve their business goals. Therefore, there is an urgent need for an affordable solution assisting enterprises, especially small and medium-sized enterprises (SMEs), in automatically extracting valuable customer journey insights from their existing data sources. For this reason, this paper presents an approach, called CJMA (Customer Journey Master) approach, for designing and deploying a customer journey management system. The proposed approach incorporates several journey analysis capabilities based on three process mining methods: process discovery, trace clustering, and decision mining. The proposed system was developed on top of Python's Django web framework with four main functions: data centralizing, process modeling for all customer paths, customer journey clustering and customer decision predicting throughout journeys. The performance of the system has been evaluated based on the three criteria: execution time, accuracy and understandability of analytical findings, which produced high outcomes using the Google Merchandise Store dataset.