Ioannis Kogias

Predicting Churn Before It Happens

Keeping customers engaged for longer helps build purchasing habits that can significantly increase their lifetime value. But how can we identify disengaged customers before it’s too late? At Dunelm, a cross-functional team came together to build & deploy a machine learning model to identify customers at risk of churning, and to successfully integrate it into existing CRM processes to optimise & personalise customer targeting. In this talk, I will walk you through the journey of what it took to make this project a success; from a technical standpoint the considerations we faced when building such a model, to the level of close collaboration that was required between business, data engineering, and data science teams.

Ioannis is a Lead Data Scientist at Dunelm, with a background and PhD in Quantum Information. His advanced analytics & consultancy experience spans 5 years across various industries including Retail, FMCG and Oil & Gas, with experience on propensity modelling, timeseries forecasting, and predictive maintenance, among others. Ioannis led the development of an ML project that won the DataIQ Awards 2020 - Most innovative use of AI. He is currently developing Dunelm’s first data science products.

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