Case Studies

Lower customer churn with personalized attrition prevention campaigns

Challenge

A major US tech company provides smart home devices to households and businesses. The company has a big customer base with a growing demand for its products. However, they recently has recorded an increasing trend in attrition among tenured customers The company needed rapid support from QubitNexus to build a proactive solution that will identify key risk factors and select customers for an outreach campaign to prevent attrition.

The Solutions

01

We integrated distinct customer and product data into a unified 360-degree customer view, updated in real-time with demographics, geographic, financial, orders, product usage, and service interactions. This provides a comprehensive, up-to-date understanding of customer behavior across both online and offline channels.

02

Then we applied machine learning models to identify key drivers of attrition. Top risk factors include: Recent drop in product and app usage Issues with the product navigation and connectivity poor customer service response

03

We build an early warning system to detect real-time trend changes in key risk factors. We developed a suite of live visualization to track attrition drivers.

04

Finally, we implemented a machine learning model that automatically identifies current customers with a high risk of attrition. Each customer is matched with an attrition prevention policy, based on their unique risk factors. A personalized message is triggered through an optimal communication channel e.g. text, email, or dispatched for an outbound call.

The Impact

We tested the attrition prevention policy over the course of a few months. We observed an annualized reduction in attrition of up to 10% among active customers. The attrition prevention policy is being continuously tested with a closed-loop measurement and updated through an iterative learning