Marketers have long struggled to understand more about and manage customer churn. Find out which customers have churned, the cause for churn, and, most importantly, how to prevent these customers from churning in the first place are invaluable insights, but marketers have always needed to conduct expensive surveys or try different approaches to answer these questions. If you use AI to predict customer churn, however, you can finally gain the insights and answers you need. Better still, AI can help you intervene to prevent these customers from leaving your brand in the first place.

Retain customers with best-in-class experiences

Customer retention is all about creating memorable and seamless customer experiences throughout the entire customer journey. Regardless of your business model or products, everything from the first point at which the customer discovers your brand, through to the first purchase, and extending into customer service and repeat purchases needs to align with the consumer’s expectations. When the customer experience breaks down or results in a negative interaction, the customer is at risk of churning.

Using data to track customer touchpoints

You should be using data to track and analyze every touchpoint in the customer journey, whether online or offline. This data will help you uncover where the customer experience breaks down. While individuals and teams can understand specific touchpoints that result in negative interactions, it becomes difficult for a human to analyze historical data from dozens of different touchpoints, understand which combination of experiences causes customers to churn, and segment customers that are likely to churn in the future. AI, however, helps you assess data points across the entire customer journey and predict with far more certainty than a human whether a customer is likely to churn. For teams that want to consider different combinations of data points, algorithms and AI are necessary.

Realizing the potential of your customer data with AI

Effective marketing is now about making sense of your data and using your learnings to execute the right programs. Many tools help you make sense of your data, but AI is the tool that enables you to apply the learnings before it’s too late. Using AI, you no longer need to be reactive; you can create a segment of customers are most likely to leave your brand or product, enabling you to intervene with new marketing programs or offers proactively.

Predictive Journeys: use AI to predict customer churn

While this probably sounds great in theory, it’s important to explain how you would actually implement this process in your organization. You don’t need a data science team to leverage this technology. Canopy Labs launched Predictive Journeys, which enables you to use AI to predict customer churn automatically.  The platform ingests your customer data, trains the model using your customer data, and predicts outcomes that are specific to your customer base and organization. The trained model can then produce a segment of customers that are most likely to churn. You can also automate communications with this segment using Canopy Labs’ email automation features. You can use AI to predict customer churn without the investment and resources required to build these processes and models yourself.

How does the model work?

Interested in looking under the hood? The Predictive Journeys feature is built using gradient boosted decision trees. This is an algorithm that takes in customer data from various interactions (email clicks, opens, purchases, etc.) and constructs decision trees. The model considers many different decision trees and finds the combination of decision trees that result in the smallest difference from reality.

First, the model is “trained,” which is a process where it ingests customer interaction data with a known outcome (in this case, purchase or no purchase) so that it learns which combinations of decision trees result in the known outcome. Once the model is “trained,” it uses the same combination of decision trees to predict future outcomes.

The algorithm predicts the likelihood of an event, which means that you are predicting which customers are most likely to churn, not necessarily those who will undoubtedly churn.

Business benefits of using AI to predict customer churn

Ultimately, using AI to predict which customers are likely to churn will help you reduce your churn rates. Engaging customers before they leave your brand will lower the number that do actually leave. There are additional benefits too, however. You can ensure that you aren’t leaving money on the table by sending offers or discounts to this group only, instead of grouping in customers that were never at risk. You can also look at the combination of touchpoints that predict churn and attempt to improve your business processes. Finally, you can enhance the customer experience for at-risk customers by proactively engaging them and indicating that they are valued.

There are countless articles and research studies indicating that businesses that retain existing customers are more successful, which gives you a strong incentive to use AI to predict customer churn.  Predictive Journeys is the AI tool that will empower you to understand who is at risk of churning so that you can intervene and retain more customers, enabling you to drive business results.