October 21, 2019 Customer Data & Analytics, Customer Engagement, Loyalty Trends, Personalization

Predictive Churn Modeling: Improve ROI & Reduce Marketing Costs

Group of marketers gathering insight customer data

Most business leaders know that new customer acquisition can be a challenge, with significant costs of acquisition during the process of marketing as well as the costs of effective onboarding. So it should make sense then for the entire organization to work to retain and maximize the value of those customers throughout the customer journey.

There are many factors to consider to improve retention, such as strategies to deepen customer relationships through cross-sell strategies, strategies to improve the customer experience and how deliver greater value through improved content, campaign and offer relevance. But one tool that may be under-utilized in most businesses is Predictive Churn modeling.

What is Predictive Churn Modeling?

A Predictive Churn model leverages machine learning methodologies to evaluate a wide range of metrics related to customer engagement. These variables, or “features” as they are commonly called in data science, are used to train an algorithm to recognize patterns of behavior. That, in turn, allows each customer to receive a score that estimates his or her probability to churn. The feature set can actually be quite complex. It may include variables that are commonly used by loyalty marketers such as Recency, Frequency and Monetary (RFM) metrics, but will also include other ways to measure engagement across all omni-channel touchpoints. These may include metrics related to point earnings or redemption patterns related to a loyalty program, it may include emotional engagement scoring, or even looking at trends over a time series that also considers seasonality.


What Can You Expect from a Predictive Churn Model?

Aimia has performed benchmarks on the business value of predictive churn models, which has shown a significant lift in results over more traditional rule-based strategies within loyalty programs. By using a data-driven approach, customers with the highest likelihood for churn can be easily identified, which allows you to implement more targeted efforts for retention or re-engagement.

In our benchmarks against rule-based approaches, we have seen improvements in campaign ROI of over 11% and a reduction in costs by over 700% through more accurate targeting of offers where they are more likely to generate business impact.

Read out from loyalty data analytics predictive churn model
Predictive Churn modeling reduces costs by allowing marketers to target highest risk customers


Building Your Retention Strategy

Once predictive churn scores are known, you’re empowered to take preventative actions to help protect those relationships, such as a personalized offer. By identifying only the customers with the highest likelihood to churn, you improve the relevancy of your offers, which improves the customer experience.

Complementary strategies can help you boost your ROI even further. A few examples include customer segmentation strategies, clustering to identify similar need and behavior patterns, customer lifetime value (CLTV) predictions and more advanced models such as Next Best Action models. A Next-Best-Action model predicts a corrective action that will have a higher relevance to the customer and will be most likely to grow the business value of that customer relationship. These strategies will be covered separately in future articles in this forum.


Avoiding Common Challenges with Predictive Analytics

Predictive analytics can present some challenges that you must consider. To maintain accuracy, models will require retraining on a periodic basis. Because the business landscape is constantly changing, a predictive model must be continuously monitored to detect a drift in accuracy that will inevitably occur.

It is also unlikely that your dataset will contain all the factors that may influence a customer’s behavior. For example, there may be economic changes, new competitive threats, social media activity, natural disasters or other factors that the model will not detect because the data is not available. Such impacts should be kept top-of-mind when monitoring ongoing results.

Additionally, a predictive model should not be built in the absence of domain expertise. While data is powerful, it must be used in the appropriate context for a given business. Domain expertise will help translate raw data into a meaningful model that truly represents the market dynamics of your specific company and within the context of your industry.

As you can see, there are many factors that go into the development of a successful retention strategy. A predictive churn model can be a powerful addition to the marketers toolkit, allowing the application of more contemporary strategies which even include the application of AI and machine learning. The benefits of these approaches not only reduce costs and boost ROI of marketing efforts, but also help facilitate a digital transformation of your organization to build expertise in leveraging some of the latest data-driven tools.


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