Data Scientists from Tenzai implemented a multipronged approach to help the client implement an effective customer journey and engagement framework.
To get end-to-end visibility into the customer journey, data was integrated from multiple data sources like CRM, browsing history, online sales, POS data, and campaign systems.
Exploratory analysis was conducted to analyze the search, browsing and purchase behavior of clients across multiple dimensions. Post analysis, journey milestones, end of journey success metrics and in-journey signals were defined.
Based on the insights from exploratory analysis, Tenzai helped the client to define new customer segments based on demographics, purchase value, price propensity and category preference. Customer cohorts were identified based on their online purchase behavior, search and app usage.
As a first step, in customer journey analysis, customer search and online purchase behavior within the website and app were analyzed using NLP and text mining. The analysis helped to cross-reference search information to identify the category, sub-category, and product details.
An analysis to arrive at the paths to purchase was conducted for both the overall customer base and every customer segment. The analysis helped the marketing and product teams to identify the triggers, friction points and conversion hot spots along the customer journey.
Based on the analysis, the revenue potential and lost revenue opportunity for each segment were identified. Marketers were now able to identify the right points of intervention and types of nudges required across the customer lifecycle.
To engage with the customers at the right time, personalized recommendations were developed using product affinity and sequential models that would recommend the right products and messages.