What is it and why is it interesting for your business?
Churn prediction is the process of identifying customers who are likely to discontinue their relationship with a business. It involves analyzing customer behaviour, and other relevant features to predict the likelihood of customer churn. By predicting customer attrition, businesses can take proactive measures to retain valuable customers, such as personalized re-engagement campaigns or improving customer experience.
Churn prediction is often crucial for businesses as the cost of acquiring a single customer is generally greater than the cost of retaining one. This is especially the case in industries with subscription-based business models.
Insights from churn prediction can lead to several benefits, among which:
- Automated detection of at-risk accounts
- Optimized marketing efforts
- Reduced costs by focusing on retention
- Improved revenue forecasts
Combining churn prediction with customer segmentation, helps you to identify and target high, medium & low value customers that are likely to churn. This allows businesses to allocate resources more efficiently to where they generate the most value.
How does it work?
We use a set of customer features to recognize patterns in customer behaviour and predict the likelihood of churn in the next month, quarter, semester.
Some of the most frequently used features are:
- Usage metrics: frequency of product usage and time spend on a platform
- Transaction history: purchase frequency, transaction amounts
- Demographic information: age, gender, location, and income level
- Contract and subscription information: Contract length, subscription type, and renewal dates
Features should always be defined together with business-savvy stakeholders to assemble a set that is capable of predicting churn. Depending on the nature of the dataset and the complexity of the features, the applied algorithm can vary from a logistic regression to a neural network.
Ultimately, we use the model to benchmark a customer's behaviour to historical feature data that was gathered for similar customers, and use this to predict how likely they are to churn.
What do we need to pay attention to?
Model interpretability is crucial when using AI to predict customer churn. It refers to the ability of a business to understand the model and the factors that contribute to churn. Without a thorough understanding of the model outcomes, it becomes challenging to take appropriate actions to prevent attrition.
Example
In industries characterized by stiff competition, such as media and publishing, effective customer retention strategies are indispensable for sustained success. In a climate where advertising revenues face significant challenges, the balance between customer acquisition and retention becomes even more critical, with retention often proving to be up to five times more cost-effective than acquisition efforts.
To tackle this, a comprehensive churn prediction model was developed, utilizing both subscription and behavioral analytics data. The model accurately forecasts churn probabilities over various timeframes, such as 1, 3, and 6 months, enabling companies to proactively address attrition risks.
With solid performance demonstrated across these periods, the model's insights are now being leveraged across advertising and marketing initiatives to optimize customer engagement and loyalty, contributing to the long-term viability of businesses in the sector.
