Leveraging ML for Customer Churn Prediction in the Banking Sector
No bank wants its valuable customers to churn. Retaining customers with customer churn prediction is the most cost-effective way to bring in revenue.
Customer churn, also known as customer attrition, is when a regular or subscribed customer stops purchasing a business' products or services in a given period of time.
Customer churn has an astonishingly significant impact on a business's bottom line, in fact, increasing customer retention by just 5% can increase profits by between 25% to 95%. This is because acquiring a new customer is 5-10x greater than retaining an existing one, so from an efficiency standpoint, it pays to ensure customers stay.
Predicting if a customer will churn is like a superpower, allowing a business to strategically target at-risk targets with retention strategies, keeping them doing business and stopping them from switching to alternatives.
However, traditional statistical methods of churn analysis are costly, often requiring a team of analysts and a heavy investment of both time and money. Furthermore, the complexity and quantity of factors result in underwhelming accuracy.
How can you predict customer churn? Machine learning (ML) is a subset of artificial intelligence (AI), that uses algorithms to build data-driven predictive models. This makes it ideal to analyze complex customer and transactional data in order to predict churn.
The AI & Analytics Engine (the Engine) is an ML tool that helps you quickly, easily, and affordably turn data into value - no coding required.
The template is designed for non-contractual, monetary transactions. That is to say, purchases involve spending money and customers do not inform the business if they will stop purchasing at a date, for example, personal banking, e-commerce or retail.
The business can decide at what activity level threshold (total number of transactions or total transaction amount) a customer is considered churned.
In addition to the prediction value, the Engine provides insights to give a deeper understanding. Feature importance values detail the most impactful factors that contribute to churn. For any particular prediction, the prediction explanation tool decodes the impact of each factor for any particular prediction, and the what-if tool provides a way to see how that prediction would change if the contributing factors change.
First things first, sign up for the AI & Analytics Engine free trial. Create a project, this will take you to the project overview page, from the project overview page, select “Use template” and then “Customer Churn Template”. From here, there is a simple three-step process to go through to start predicting churn.
The first step is uploading customer transaction data, as well as customer information (optional), which can be done either through a direct file upload or connecting to a database.
The next step in the process is defining the conditions, based on your domain knowledge, that constitute a churned customer for you. This can be defined either by:
Finally, add some contributing factors. Contributing factors are defined as factors that influence the likelihood of a customer's churn. By default, the Engine pre-selects the transaction amount
, transaction timestamp
columns and define rolling date ranges most recent 30 days
30 days, 30 days ago
and 30 days, 60 days ago
.
The date ranges help the Engine generate additional features (that is columns in a dataset) that improve the speed and quality of your prediction. You can modify or remove these default rolling dates, or add new ones.
After these three easy steps, the Engine will automatically recommend the optimal model for you. Simply
Once your model has finished training, you can start generating predictions from the app’s summary page. The two options are making a one-off prediction if you want it immediately or scheduling periodically if you want them continuously, as new data is entered.
Scheduled predictions require a connection to a database such as SQL, Oracle, or Snowflake, in order to input new transaction data.
Having a model is great, but it’s important to evaluate it to determine how well it’s working. The model leader board ranks the performance of all models built, giving you advanced metrics Which metrics are used to evaluate a binary classification model's performance?, along with visualizations of the precision-recall and ROC curves What are ROC and PR Curves for binary classification.
Equally valuable to the churn predictions are the insights on your data, which the Engine provides within the model insights tab.
These are displayed in three tools:
The feature importance tool tells you exactly how impactful each factor is in contributing to a churned customer in general.
The prediction explanation tells you how much impact each factor for any given prediction.
What-if analysis allows you to change inputs for factors for any given prediction, allowing you to see the new prediction and compare results.
Once you’ve generated your predictions and gathered contextual insights, it's time to consume your predictions. Predictions can be previewed, downloaded, or exported, as a dataset to a project within the Engine, or as a table to a connected database.
Customer churn is important, and accurately predicting who is likely and understanding why it’s happening, is the first step in creating a retention strategy that will spike your profitability. PI.EXCHANGE’s customer churn template allows you to harness the power of machine learning in order to do this in the most efficient way possible.
We have answers.
The template is designed for non-contractual, transactions. Where customers do not inform the business if they will stop purchasing at a date, for example, personal banking, e-commerce, or retail (to name a few). The other type of customer churn is a subscription or contractual churn where there is an understanding of an upcoming transaction ahead of time (don't worry - we are building a template for this too!)
In order to build and train a customer churn model, we need historical data of the customer's behaviour. This dataset should include details of each customer's past transactions, including the date and amount. The Engine will use this information to build a prediction model to create predictions that will indicate the likelihood of churn for each customer.
Yes. You can trial the churn prediction template for two weeks free. This gives you access not just to templates but to the Build from the Scratch tool as well so you can develop other machine learning models (like clustering models for customer segmentation).
No bank wants its valuable customers to churn. Retaining customers with customer churn prediction is the most cost-effective way to bring in revenue.
This use case outlines what customer churn prediction is, why is it important, and how you can implement it with the AI & Analytics Engine.
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