CASE STUDY

Proactively Manage Customer Churn to Improve Retention and Increase Profitability

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Industry

Retail

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Ecosystem

AI/ML-Driven Customer Analytics

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Metrics

Customer Retention Rate
Predictive Accuracy of Churn
Revenue Impact from Retained Customers

Business Case

In today’s competitive retail market, companies face challenges with unpredictable customer behavior. Some customers only shop during sales, others respond to coupons, and many stop buying over time. To stay competitive, retailers need to predict which customers are likely to stop purchasing and take quick action by offering personalized promotions and discounts to keep them engaged.

With so many choices available to consumers, it’s harder than ever to keep them loyal. The key challenge is to understand customer behavior by analyzing data like purchase history, promotion response, and market trends. By predicting which customers are at risk of leaving and acting quickly, companies can reduce churn, build stronger customer relationships, and increase long-term value.

Solution Provided

UCBOS delivered a Zero-Code AI/ML-powered Customer Churn Prediction system that tracks customer purchase behavior, sale vs. non-sale buying patterns, and their response to coupons. The system uses advanced machine learning models to analyze patterns such as customer inactivity, frequency of purchases, and engagement with promotions.

By leveraging internal data like transaction history and external data such as seasonal trends or market changes, the platform identifies which customers are at high risk of churning. With UCBOS’s Zero-Code platform, the retail company can set up these predictive models without technical expertise and receive real-time alerts when customers show signs of churn. This allows them to offer timely incentives, such as personalized offers or exclusive discounts, aimed at retaining high-risk customers.

Outcome

With UCBOS’s AI/ML-driven solution, the retail company significantly improved their ability to identify potential customer churn. The system accurately predicted which customers were most likely to stop purchasing and allowed the company to take preemptive action through targeted promotions. This resulted in higher customer retention rates, increased repeat purchases, and an overall boost in revenue. By integrating predictive churn data with their marketing and customer outreach programs, the company was able to proactively engage at-risk customers, reducing churn while increasing the average lifetime value of each customer.

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