Fighting Churn Subscription Economy

Churn Data Science Strategy

Churn means that your customers cancel your service, or stop spending money on your site or app. Churn is the most common Data Science problem in the world. That’s because nowadays every company has a churn problem! All companies want to keep their customers coming back. Top 3 Challenges for Data Science with Churn Most data scientists have fit a churn model in a class or bootcamp. But really fighting churn is much harder than…

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Data Science Machine Learning

Explainable Machine Learning Churn Prediction

In my previous post on Machine Learning for Churn prediction, I showed that Machine Learning models (ML also known as AI) are the most accurate. But in the past, machine learning models suffered from being hard to explain. In my book, Fighting Churn With Data, I wrote that you should use Logistic Regression to explain churn. But I also showed that the machine learning model XGBoost gave higher accuracy. Now I recently learned about a…

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Data Science Fighting Churn Machine Learning

Machine Learning Churn Risk Forecasting, Part 2ā€Š

This article discusses using machine learning algorithms to forecast churn risks. Continued from Part 1, in which the XGBoost machine learning model and how it can be used to predict churn is presented. This post demonstrates the code and some results for real data. Below, chapter 9 listing 6 from the book (Fighting Churn With Data) shows XGBoost cross-validation for regression in python. (All the code from Fighting Churn With Data can be found at…

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Data Science Fighting Churn Machine Learning

Forecasting Churn Risk with Machine Learning, Part 1ā€Š

This post discusses forecasting churn risks using machine learning algorithms. In this article, Iā€™m going to introduce the basic ideas of machine learning (ML) and a particular algorithm called XGBoost. If you are already familiar with these topics then skip to my next post which dives into the details of using these methods to forecast churn. Forecasting churn risk with machine learning You can forecast churn with a regression in which predictions are made by…

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Fighting Churn Subscription Economy

Customer Lifetime Value and Churn

Customer lifetime value (CLV) is the foundation of recurring revenue business models: you recoup the costs and profit from a customer over their lifetime using your product, not from a single transaction. CLV is important because you need to make sure that the costs you spend to acquire and maintain your customers are worth it! If you calculate CLV correctly, you can use it strategically to evaluate the ROI of different acquisition and retention tactics.…

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Data Science

Customer churn probability

Calculating churn probability is an important part of fighting churn because of three key use cases: Evaluating which behaviors are most important for engagement Calculating customer lifetime value Segmenting customers for high cost interventions In this post I give an introduction to logistic regression: Logistic Regression is the most common and versatile way to calculate the churn probabilities. I assume you have calculated metrics and created a data set, following the instructions in my previous…

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Fighting Churn

Advanced Metrics for Customer Churn

In this post I’m going to explain why you need to use what I call “advanced” customer metrics in your fight against churn. If you are trained in data science, you would call this feature engineering because we are talking about designing the data (features) for an analysis. I’m going to make some examples from the Versature case study that I mentioned in my previous post on understanding churn with metric cohorts. (Versature is a…

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