Customer Data & Analytics Blog

Andrew Malinow, PhD and Mimoza Marko

Recent Posts by Andrew Malinow, PhD and Mimoza Marko:

The Mechanics of Predicting Customer Churn: Part 3

Andrew Malinow, PhD and Mimoza Marko | 3 minute read

The last two posts in this series covered measuring churn, both in businesses with a subscription-based business model and in ones where there is no easy way to define “churn.” This blog post will detail how to tune an existing model to give more accurate results.

When building any Classification or Predictive model, there are always multiple iterations – throughout each, we “tune” the model based on its performance on training data. All of the steps that we take, from segmenting the data, to feature engineering, building the model, and then evaluating the model, are typically repeated several times. For our churn model, it is critical to retrospectively evaluate model performance for each phase of development and identify things that can be modified to improve model accuracy.

Topics: Customer Analytics

The Mechanics of Predicting Customer Churn: Part 2

Andrew Malinow, PhD and Mimoza Marko | 2 minute read

Last week’s blog post focused on how to predict customer churn for businesses that have a subscription model, where the definition of “churn” is straightforward – a subscription canceled equals a customer churned.

This post will provide an overview on how to predict customer churn for businesses that do not rely on subscriptions. The absence of an explicit churn ‘label’ in our data adds an additional level of computational complexity to the analysis – specifically there is need to develop a mechanism to define “churn” (rather than inherit it directly from an existing data element). To this end, we will leverage information about customers’ transactional behaviors to provide us with a definition for churn that we can use for building our model.

Topics: Customer Analytics

The Mechanics of Predicting Customer Churn

Andrew Malinow, PhD and Mimoza Marko | 2 minute read

 

When The Business Follows A Subscription Model

Customer churn is a typical dynamic in any business – for one reason or another, a customer who has previously purchased from a company, no longer purchases. However, to surface potential causes for churn that can inform mitigation activities, we need a more operational definition.

Churn can be defined in several different ways. If a business uses a subscription model (e.g., Netflix, Amazon Prime membership), churn can be defined as those customers who have cancelled their subscription. A subscription cancellation typically exists as an explicit field in a database (cancelled_subscription=True), or it may need to be derived in some way. In either case, there is a specific event, that is either explicitly captured, or can easily be derived from existing data points, that provides the definition for churn.

Topics: Customer Analytics