In today’s digital economy, consumers have quick recourse if they’re not happy with a company’s services: turn to social media, move to a competitor or simply disappear without a trace.

When a customer ends a relationship with a company — otherwise known as churn — the potential for increased profits is lost. A report by Bain & Co. stated that the longer a customer is with a company, the more profit they bring in.

So, how can you prevent churn from eating into your profits?

A New Approach

Lara AlbertThere are many models out there to help companies, but they’re riddled with problems, said Lara Albert, vice president of global marketing for Globys, makers of the Amplero personalization platform.

“With traditional models, you employ targeting rules to dictate which offers are given to which customers. It’s a grueling manual process.”

This is just what the platform’s latest release aims to solve, she added.

By integrating continuous monitoring of customer activity with machine learning, Albert claims that Amplero can now predict churn 300 percent more accurately than other solutions, and provide up to 400 percent better marketing retention.

“Now, we can automatically determine the best experience for each customer in any context, based on the highest expected lift for any experience,” said Albert.

Outperforming Today’s Churn Models

Oily DownsOlly Downs, Chief Scientist/CTO for Globys, joined Albert in the conversation by discussing some of the problems with currently available churn reduction solutions, and how Amplero bypasses those issues.


First and foremost on the list, said Downs, is timing.

“Because of the cadence of traditional churn modeling, marketers are being reactive rather than proactive,” he said. “A customer might have already churned by the time the reports are generated, rendering them inaccurate.”

With Amplero, stated a company release, the dynamic behavior of each individual customer is analyzed on a continuous basis. The results of this time-based modeling are constantly fed into Amplero's closed-loop platform, which then uses machine learning to discover and execute optimal retention marketing actions on a per-customer, contextual basis.


Another challenge with traditional churn models is that they typically only provide an aggregated report of customer activity, which doesn’t give a clear picture of whether or not a customer is in danger of churning.

“Traditional models take a row of attributes from the customer, and use the current values of those attributes to predict the probability of a customer churning,” said Downs.

“On the other hand, by continually looking across a sequence of events, scoring and rescoring the customer, marketers can better determine at which point a customer is likely to churn.”

Wasted Resources

Finally, said Downs, because of the imprecision of traditional models, marketers end up wasting resources by extending promotional offers and freebies to customers who don’t intend to churn.

“Sometimes marketers end up messaging a large proportion of customers who aren’t going to churn, which could cannibalize their revenue or invoke hard costs, such as extending two months of game time for free, or sending a new device when they didn’t have to.”

With Amplero’s newest solution, concluded Albert, marketers can experience improved retention rates, while their customers can experience higher value in continuing an ongoing relationship with their brand of choice.

“By more accurately predicting churn, marketers will have enough time to execute messages or offers – perhaps with a sequence – that can be effective in influencing customer behavior,” she said.

When it comes to churn, concluded Albert, what companies need to ask themselves, is this:

“Are you predicting customer churn with a high degree of accuracy, and are you allowing time for marketing execution to take place?”

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Title image by Zack Minor.