For data-driven enterprises, customer retention is as important as acquisition. Even small improvements in reducing churn can have outsized impacts on revenue and growth. A leading global analytics, data, and AI company wanted to anticipate customer attrition before it occurred—turning reactive retention strategies into proactive engagement opportunities. Karma Advisory worked with the client to implement a predictive modeling solution that transformed customer data into actionable insights.
The Business Challenge
A leading global analytics, data, and artificial intelligence company wanted to proactively identify potential customers at risk of leaving the cloud data management platform in the near future.
The Solution & Implementation
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Implemented a Random Forest with weighted parameters prediction model trained on internal data to forecast customer churn
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Assigned a BRAG (Being Retained, At Risk, Gone) status for each customer directly in Salesforce
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Incorporated multiple data sources into the model, including:
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Number of support tickets
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Sentiment analysis of support tickets
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Number of users/licenses
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Frequency of user logins
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Usage statistics (overall trends and per-user profiles)
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Product reviews and feedback
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The Business Impact
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Reduced Churn: Achieved a 30% reduction in customer churn within the first quarter of deployment by enabling proactive retention strategies
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Increased Product Usage: Boosted product usage among lower quartile users by 25%, driven by targeted engagement strategies based on predictive insights
Conclusion
By applying predictive analytics to customer behavior, the client transformed churn management from a reactive process into a proactive strategy. The deployment of a weighted Random Forest model embedded into Salesforce empowered teams to act on early warning signs, reducing churn while simultaneously increasing engagement. This case demonstrates how advanced machine learning can unlock tangible business impact—improving both retention and product adoption.
