When deploying predictive analytics in your company, a key component to keep in mind is that as your company evolves, your customers may change and the math formula that was used to predict the initial cancellation rate may have accuracy diminish over time. It’s a good idea to A/B test your offers and incentives to verify that they are still effective. You can even perform predictive analytics on offers to predict what offer a customer is most likely to respond positively to.
Most businesses have ever-changing customers over time, which means you are predicting the future of a moving target. Hence, it’s crucial to evaluate that the process is still working and accurate.
Because you are influencing the customer’s behavior, it’s important to identify if you are still getting the results you need through the campaign. A predictive analytics campaign is the same as any campaign with an ROI, it just comes at a different part in the sales funnel - particularly with retention metrics. For example, “Iis the retention campaign keeping more customers and adding to the bottom line?.”
It’s easy for customers to figure out patterns and share that information with others. There are dozens of outlets for customers to communicate holes in your process and share your communications. Imagine an email with a 50% discount code sent to high-risk cancellation accounts., tToday it takes less than 30 seconds to share that discount code on various discount websites or to share it on social media platforms such as Twitter or Facebook.
An algorithm detects a probability of a result, like “the customer has an 80% risk of churn.” Most systems that you integrate with need a binary decision, such as “send the email or don’t” at a certain threshold. This is where either management decisions or artificial intelligence (or both) is required to maximize the result. For example, perhaps you decide that when a customer is identified as having an 80% probability of cancelling their subscription, you want to email a discounted offer to retain them. However, is 80% the optimal predicted threshold? What about those that have a 60% chance of churn - is that high enough risk to offer a discount? Or, looking at it from the other direction, if a customer is at an 80% chance of leaving, is it even worth giving away a discounted service if they’re going to ultimately leave regardless? They key to navigating the gray area is to assign someone to determine these thresholds and have them monitor the results so the algorithm can be refined over time.
Predictive analytics is a very powerful tool that any business can use to make better decisions and get better results for their customer growth or retention campaigns. Used correctly, you can increase your top-line revenues, reduce risk for your organization and ultimately, increase your bottom-line results.