Resolved Customer Churn in Multi-Channel Purchase Journeys with AI

Disconnected customer data across platforms

• Solution: Bacancy implemented an AI-powered Customer Data Platform (CDP) leveraging entity resolution algorithms and graph neural networks to connect scattered touchpoints. This enabled a 360-degree view of each customer’s multi-channel behavior, creating a unified identity and journey path.
  
  Problem:- Customer interactions on the website, mobile app, and physical stores were stored in isolated systems, making it impossible to create a unified view of the customer journey.

Inability to predict churn due to inconsistent behavioral signals

• Solution: Our AI experts built a multi-source behavioral churn prediction model using sequence modeling (LSTM networks) and Bayesian probability mapping. The model processed interactions across time and channels to calculate real-time churn probabilities and recommend preventive actions.
  
  Problem:- Traditional churn models failed to account for fragmented engagement signals, like browsing on mobile but abandoning in-store purchases, which leads to missed churn indicators.

Lack of actionable insights to re-engage at-risk customers

Bacancy deployed a real-time AI decision engine using reinforcement learning to recommend the most effective re-engagement strategy, such as email, app push notifications, SMS, or personalized in-store offers, based on user habits, sentiment analysis, and past response data.

Problem:- Even when churn risks were suspected, the marketing team lacked tools to target the right customer at the right time on the right channel.

• Solution: Bacancy deployed a real-time AI decision engine using reinforcement learning to recommend the most effective re-engagement strategy, such as email, app push notifications, SMS, or personalized in-store offers, based on user habits, sentiment analysis, and past response data.
  
  Problem:- Even when churn risks were suspected, the marketing team lacked tools to target the right customer at the right time on the right channel.

No mechanism to track and improve channel-switching experiences

• Solution: We integrated AI-based journey analytics using Markov Chain models to trace common switch-points and drop-off nodes. Combined with heatmaps and predictive insights, this allowed the UX and operations teams to fine-tune the experience and optimize channel transitions.
  
  Problem:- Customers often began their journey online and completed it in-store (or vice versa), but the system didn’t capture cross-channel drop-offs or pain points.