Are you feeling exhausted and disenchanted after a protracted day at work, spent tirelessly attempting to enhance the precision and effectiveness of your data-driven campaigns and churn prediction accuracy? We understand how you are feeling.

Despite some analytic teams believing that a rule-based marketing approach, using indexes and segmentation criteria, can successfully replace churn prediction models, this will not be the case. Clustering algorithms are not any longer effective, and here’s why:

Clumsy algorithms with low accuracy

As an illustration, choosing two segments and five features to explain a churn-risk customer would generate 32 possibilities, making it difficult to create a linear connection algorithm with high accuracy. In today’s world, every percentage point counts, so counting on intuition and logic will not be sufficient.

It’s so yesterday

Secondly, churn prediction based on clustering is outdated. Since churn is commonly brought on by transitional aspects, yesterday’s reasons and criteria could also be irrelevant today. Then again, predictive models guarantee that rules are stable over time and may maintain efficiency month over month. Consequently, operators can provide tailor-made offers, equivalent to attractive discounts and personalized tariff plans, with the next likelihood of engaging subscribers and retaining customers.

Nothing is clear anymore

Thirdly, predictive models offer cross-functional insights that transcend the reach of clustering approaches. As an illustration, a serious Eastern European operator discovered a major segment of its customer base with high average revenue per user and high data spend, which tended to churn and required specific treatment. Moreover, the model revealed that customers with home web and paperless bills were more more likely to churn than others. These insights weren’t obvious and wouldn’t have been revealed through traditional clustering.

Where the magic is hidden

Did you already know that the variety of banks a subscriber uses can tell us so much about their likelihood to churn? There are various non-obvious features which have a major impact on churn, and their combination could be a game-changer. Predictive models are powerful tools that might be used alone or along side rule-based segmentation as a reinforcing factor. But how do you include all of them? The reply is to make use of models.

A well-designed and trained predictive model, based on a high-quality dataset, can take your project to a latest level. The accuracy of a predictive model is determined by many aspects, and the principal one is the choice and quality of input variables or features. Features are the input variables that the model uses to make predictions, and their selection has a major impact on the accuracy of the model.

Feature engineering fastidiously selects variables and preprocesses them to enhance the accuracy of the model. Its role is to discover probably the most relevant and informative features for the issue being solved, transform and scale them appropriately, and take away any redundant or irrelevant features which will add noise to the model.

The accuracy of a predictive model heavily relies on the choice and quality of input features. It might be a difficult task to discover and prioritize features with high importance. To deal with this issue, we have now created an open-source project where we encourage contributors from different fields to share their findings and contribute to the project. The goal of this project is to create a collaborative platform for feature engineering, where individuals can collectively discover and evaluate probably the most informative features for a given problem.

Our open-source project https://github.com/FeatureHub-AI/FeatureHub