What are the best machine learning models for predicting customer churn?

If you have a consumer subscription service, machine learning is an excellent, scalable approach for not only knowing who is likely to churn but also when and ideally what should you do to mitigate consumer churn. Churn is typically seen an event in which a consumer cancels their subscription to your service. However, in many consumer CPG relationships, their is no monthly or annual fee the consumer pays to engage with your brand. Without a definitive action such as revoking payment authorization, churn is a more subtle event measure ex post facto. Where once the consumer purchased your product (or visit your website) with predictable frequency they purchase (or visit) no more. Here too, machine learning can identify potential factors.

Machine learning for churn prediction is crucial in the ever-evolving realm of customer dynamics. Traditional methods often fall short in analyzing complex datasets, making machine learning indispensable. Its ability to process vast data, identify subtle patterns, and provide accurate predictions allows businesses to proactively address potential churn by understanding influential factors. The adaptability of machine learning models ensures organizations stay ahead in a rapidly changing market, continuously learning and evolving based on new data. Ultimately, machine learning empowers businesses with data-driven decision-making, optimizing customer retention strategies, and fostering long-term customer satisfaction.

It might also be worth adding Survival Analysis to the "arsenal" of models. This is a group of statistical methods that study how long it takes for certain instances to occur. The best known ones are Kaplan-Meier survival analysis and Cox regression. It was not specifically designed to investigate churn, but it can be used for this as well. Another very successful family of models is network analysis techniques, where the nature of data and the vertical lends itself to it, e.g. telcos. There, by analyzing the relationships between customers (e.g. different centrality measures), you can identify influencers whose decision to churn might increase the churn risk of many other customers.

As in any modeling effort, a variety of modeling methods are available, the selection of which is dependent on your modeling objective and the type and volume of data available for modeling. Determine which learning approach will be most applicable to your churn modeling approach. Random forests or decision trees leverage information theory to build models and help identify which data will provide the most information gain. These models are less dependent on well defined statistical distributions, but also prone to excellent performance on the training data but degrade significantly when feed test or new data. Regression models help with interpretability, but may be suffer from transforms and expansive feature sets

Churn prediction models come in various types to address the multifaceted nature of customer behavior. Binary classification models offer a clear indication of churn likelihood, while multiclass models provide nuanced predictions across different churn categories. Regression models add a temporal dimension by predicting expected customer lifetime value. This diversity in models underscores the need for a tailored approach based on data nuances and organizational goals, providing a comprehensive toolkit for effective churn prediction strategies.

As initial step toward choosing a machine learning model, carefully consider the ratio of churners to non-churners. Is the number of churners and non-churners relatively close in size (40% churn v 60% non-churn) of significantly different (5% churn v 95% non-churn)? The answer will lead you to focus more on one classification measure over another. Also consider Type I & II errors. What's the worst case if you take action on someone you predict to be churning but actually had not intent to leave your service? Also consider if your churn model could be improved with multiple methods, an ensemble approach.

One of the most important decisions is the target variable. As described in section 2, the business question might translate to a classification or a regression (or yet another) problem. But even within classification, the exact definition of the target variable can make or break the model (this is generally true to predictive modelling, not just churn). For example, we need to decide how long before churn we want to predict. Should the target be 30 days before churn or 90 days before churn? Typically, churn is a gradual process, so it needs to be identified early enough (i.e. a 90 day window may be better than a 30 day window).

Choosing the ideal machine learning model for churn prediction requires a nuanced approach, considering factors like data type, problem complexity, and model performance. Adequate, relevant data is crucial for effective training and testing. The balance between model simplicity and accuracy is key, guided by a clear problem definition. Rigorous evaluation metrics ensure accurate performance measurement, while regular validation and updates accommodate evolving customer behavior. Ongoing monitoring ensures the model aligns with desired business outcomes. The process underscores the need for adaptability and precision in addressing the unique challenges of churn prediction.

Consider some of these best practices when choosing an algorithm: -Logistic Regression is always a great place to start, but it tends to be less accurate with datasets that have a large number of variables, complex / non-linear relationships, or collinear variables. -Random Forest does well on tabular datasets, but can be slow - XGBoost will be less robust in terms of overfitting data and/or handling messy data but it will do better when the dataset is unbalanced -Gradient Boosting is more sensitive to overfitting, but can do better with categorical variables with many levels. -Adaptive Bosting is more sensitive to noise but can perform better with high-quality datasets when overfitting is a concern

Various machine learning models are available for churn prediction, each tailored to address diverse customer behavior. Logistic regression simplifies binary classification, revealing the probability of churn based on customer features. Decision trees offer interpretability through graphical rules, while random forests enhance accuracy through ensemble methods. Support vector machines find optimal boundaries between churned and non-churned customers, and neural networks uncover complex relationships using artificial neurons. The choice depends on data characteristics and the desired balance between interpretability and predictive power, emphasizing the need for a customized approach to churn prediction.

The typical steps of a model building workflow are: - population definition - target definition (These two also include deciding how to assign target dates to the records in the population - i.e. dates compared to which we identify the target.) - data exploration & initial cleaning (the extent of this heavily depends on how much data engineering / warehousing is already there in the org) - feature engineering (data-driven as well as expert-led features) - variable reduction (univariate analysis, bivariate analysis, variable clustering and other collinearity reduction techniques) - pre-modelling data prep (outlier removal, standardization etc) - model building & model selection (incl. hyperparameter tuning) - evaluation & thresholding

Implementing machine learning models for churn prediction involves a methodical approach. Begin by defining the problem and goals, exploring and analyzing data for quality and relationships. Prepare and transform data using techniques like cleaning and scaling. Choose metrics aligning with business objectives and employ methods such as feature engineering. Train, test, and optimize models, utilizing cross-validation and grid search. Post-deployment, monitor performance, gather feedback, and make necessary updates for sustained impact in predicting customer churn.