Predicting Payment Behavior in PAYGo: Machine Learning Can Power Customer Retention

Jacob Winiecki
Jul 13, 2018 · 6 min read
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Watch how FIBR Zola Electric used data to better serve PAYGo customers
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Sorted precision of confidence in churn risk probability. This graph is a distribution of how confident the model is that each customer will churn. So for some people the model is very confident, and so for those you really want to do something, and for a lot of others it’s less confident. For an intervention that’s particularly expensive, you probably only want to intervene with those in the upper left.
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Machine learning can quickly digest hundreds of variables. In our work with ZOLA, we were able to send 150+ variables through a random forest model to identify which are most influential to churn. This could be done on a regular basis in a few hours with ML, which can be much faster and more efficient than having humans run regressions.

How could this improve upon business as usual for PAYGo operators


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Finance for Life

Building more sustainable and inclusive economies through data, finance and technology innovation

Jacob Winiecki

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Posts on energy access, digital finance, pay-as-you-go solar and financial inclusion at @BFAGlobal. @SimpaNetworks co-founder #paygo #solar

Finance for Life

BFA Global is a research, advisory, data analytics and product innovation firm focused on the intersection of finance, data and technology. We aim to improve the lives of vulnerable and underserved communities around the world.

Jacob Winiecki

Written by

Posts on energy access, digital finance, pay-as-you-go solar and financial inclusion at @BFAGlobal. @SimpaNetworks co-founder #paygo #solar

Finance for Life

BFA Global is a research, advisory, data analytics and product innovation firm focused on the intersection of finance, data and technology. We aim to improve the lives of vulnerable and underserved communities around the world.

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