A software company gains a 360-degree customer view, fueled by machine learning and predictive analytics, to feed renewals and additional sales. The customer churn algorithm now predicts at 80% accuracy.
It’s always costlier to acquire customers rather than to retain them. However, this software company had no “window” into customers as subscriptions for its software and services approached renewal. Sales managers and senior executives lacked a central repository of information on past transactions as well as information on accounts, and product-related issues – the kinds of details that would help sales expand and close more deals.
What to do then? As the company decided to move all of its transactional systems to the cloud, the time was right to act on its interest in predictive analytics and churn probability. Cognizant had already worked with the company’s data warehouse and business analysis for several years. Based on our longstanding relationship, the client chose our team for the customer 360-analytics project.
Incorporating machine learning into the analytics model elevated the customer 360-solution in several ways: it helped to spot patterns and to identify the customers most receptive to cross-sell and upsell.
Now, the solution’s flexible dashboard enables views of customer performance at all levels, such as how many orders they have booked and the products they have purchased. Details about churn probability, propensity-to-buy scores, and customer lifetime value are shown. With the information in hand, sales managers can extend offers of discounts and premium support to reduce churn. They can also identify potentially high-value customers.
The use of the model has helped the company to identify the key factors driving customer churn and to estimate total revenue at risk. The algorithm predicts customer churn with 80% accuracy. The solution’s automation of data ingestion, massaging, and analytics reduced manual efforts by 75 to 80%.
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