When it comes to reducing customer churn, knowledge is power especially if it’s in the form of rich behavioral insight. As marketers we do everything we can to understand how customers behave and why they leave, so that we can get prevent it.
But what marketers often get is a churn explanation rather than a true prediction of churn risk. So how do you get in front of the problem? How do you predict who may leave with enough accuracy and sufficient time to intervene in ways that influence their behavior?
For as long as marketers have been trying to address the problem of churn, the traditional approach to churn modeling has been to “score” customers. The problem with churn scoring is that most retention models rate customers with a score that depends on manually creating aggregate attributes in a data warehouse and testing their impact in improving the lift of a static churn model. The process can take several months, from analyzing customer behavior through deploying retention marketing tactics. Furthermore, since marketers typically update customer churn scores on a monthly basis, rapidly emerging signals that indicate a customer may leave are missed. As a result, retention marketing tactics are too late.
Amplero, which recently announced the integration of a new approach to behavioral modeling to fuel its machine learning personalization, provides marketers a smarter way to predict and prevent churn.
What is Machine Learning?
Machine learning is a type of artificial intelligence (AI) that provides systems with the ability to learn without being explicitly programmed. This is typically accomplished through continuously feeding data to and having software alter algorithms based on the results.
Unlike traditional churn modeling techniques, Amplero monitors sequences of customer behavior on a dynamic basis, automatically discovering which customer actions are meaningful. This means that a marketer is no longer reliant on a single, monthly score indicating whether a customer is at risk of leaving the company. Instead, the dynamic behavior of each individual customer is analyzed on a continuous basis, leading to more timely retention marketing.
Key benefits of Amplero’s behavioral modeling approach:
- Increased accuracy. Amplero’s churn modeling is based on analyzing customer behavior over time so it can detect both subtle changes in customer behavior, and understand the impact of very infrequent events. The Amplero model is also unique in that it is updated continuously as there is new behavioral data. Because churn scores never get stale, there is no drop-off in performance over time.
- Predictive vs. reactive. With Amplero, churn modeling is forward looking resulting in the ability to predict churn several weeks in advance. This ability to make predictions over longer timeframes allows marketers to engage customers who are still engaged but are likely to churn in the future with retention messages and offers before they reach the point of no return and leave.
- Automated discovery of signals. Amplero automatically discovers granular, non-obvious signals based on analyzing a customer’s entire behavioral sequence over time. Continuous exploration of data allows for the detection of personalized patterns around purchases, consumption, and other engagement signals. If there are changes to the competitive market that result in changes in customer behavior, the Amplero model will immediately adapt to these changes, discovering new patterns.
- Early Identification, when marketing is still relevant. Because Amplero’s sequential churn model leverages highly granular input data, far less time is required to successfully score a customer, meaning that Amplero’s model can identify churners with much shorter tenure. Results of the propensity modeling are constantly fed into Amplero’s machine learning marketing platform which then discovers and executes the optimal retention marketing actions for each customer and context.
With Amplero marketers can achieve 300% better churn prediction accuracy and up to 400% better retention marketing than when using traditional modeling techniques. Having the ability to make more accurate and timely customer predictions makes all the difference in being able to develop a sustainable capability for reducing churn and boosting customer lifetime value.
For more information or to request a demo, please visit Amplero.