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Churn / Retention  -  AI Churn Prediction

Developed a predictive model harnessing AI to forecast user churn from behaviour and usage patterns in food delivery apps. Complemented by in-depth research, uncovered the core reasons for customer Churn/drop-off and devised targeted strategies to bolster loyalty and retention.

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I Leveraged AI to develop a predictive model for user churn in food delivery apps, rooted in behavior and usage patterns. 

My research unveiled a formulaic approach to understand churn, emphasizing the significance of the Peak-End Rule in user experiences. Affordability emerged as the pivotal 'Peak' factor, while Seamless Delivery and Quality stood out as decisive 'End' experiences. To holistically address churn across operations, logistics, growth, and more, I championed the Gaps Model and SERVQUAL Model. 


These strategic frameworks are instrumental in bridging inter-departmental disparities and elevating service quality, aligning customer expectations with actual service delivery. With these insights, I architected an AI model poised to revolutionize user retention strategies.

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