Keeping More Customers with Predictive Modeling

Keeping More Customers with Predictive Modeling

Part 1 of a 3-part series on customer retention.

(Updated March 6, 2019)

Marketers have a tough job. There is a plethora of competing priorities, new technologies, mountains of data, competition getting tougher, and consumers are harder to convince.  And your job is never done. You masterfully design and execute direct mail campaigns, craft email campaigns, develop promotions, and manage your budget all while striving to improve Return On Marketing Investment (ROMI).  

You look back at your progress over the last quarter and you’re happy to see that you’ve acquired a number of new customers, but you noticed that Revenue is essentially flat. You dig deeper and find that you’ve lost customers.

Your heart sinks.

Customer churn, otherwise known as attrition, is a challenge that many businesses face. In some cases, it develops into a dire problem—and one of the hardest to remedy. When a company loses customers, it becomes increasingly difficult and expensive to replace those customers with new ones. 

While some businesses operate with numerous one-time customers, many others depend on customers who pay for products and/or services every month or year. These customers provide the foundation for a business’s Annual Recurring Revenue (ARR) and are critical to its success.

Their loss can become detrimental.

On the other hand, improving customer retention makes tremendous business sense.

This should come as no surprise.

However, the size of the impact may shock you.

A 2% increase in customer retention has the same effect as decreasing costs by 10%.[1]

Additionally, Bain & Company found that a 5% increase in customer retention drives profits to increase by greater than 25%.[2]

Combine this with a solid customer acquisition program and you have a recipe for growth.

The Problem: Losing Customers

The reason customers leave could depend on many different factors. Bad customer experience, poor communication, cost sensitivity, perceived value, and internal service gaps are likely causes. Each of these factors can be highly subjective. Moreover, pinpointing and understanding the overall “why” these customers leave can be daunting.

When addressing the problem, developing and implementing programs can help, but oftentimes results can fall short.

Looking at a dashboard of lagging indicators showing your results have fallen short can be frustrating. Even more frustrating is when your leading indicators don’t seem to be helping you enough either.

Companies know high or growing customer attrition leads to slowing growth and decreased profitability. If they can’t get it under control, the business’s future financial stability can spin out of control.

Financial institutions sometimes find themselves in this same predicament– high attrition and no clear view on why it happens and how to fix it. There have been cases where financial institutions are disappointed to find high churn among their new credit card holders. They attracted them by offering a zero percent interest rate for 6 months.

However, their campaign attracted the wrong type of customer.

They attracted those who lacked loyalty to a specific credit card company and who would take their business elsewhere once the promotional period ended.

Another industry that has a similar likelihood of attrition is the telecommunications industry. These companies experience tremendous attrition because their service is perceived as a commodity. Coverage, plans, and packages all relate back to cost and cost-sensitivity becomes a main cause for attrition. Thus, the cheapest plan wins the day.

Keeping More of Your Customers

Understanding your customers' motivations and what drives them to purchase is imperative.

As Peter Drucker once said, “The aim of marketing is to know and understand the customer so well the product or service fits him and sells itself.”

Learning why they leave for the competition is just as important.

Additionally, measuring each customer's level of loyalty will guide you in making decisions on retention. Many retention programs risk failure because they provide little to no insight into your customers.

“The aim of marketing is to know and understand the customer so well the product or service fits him and sells itself.”
— Peter Drucker

Predictive modeling uses data and statistical algorithms to predict certain outcomes.

Historical data, such as direct mail responses or spend are incorporated into the building of predictive models to predict the likelihood of future events. This moves beyond descriptive statistics or analytics. With predictive modeling, we aim to identify patterns of customer behavior and draw actionable insight.

Predictive churn modeling can help improve customer retention by providing a more structured, data-driven approach to understand and retain customers who are likely to depart.

Coupled with the right data, predictive churn modeling, or attrition modeling, enables the sophisticated marketer to gain insight into their current customers and pinpoint what differentiates the ones who leave from the ones who stay. This further empowers marketers to make more cost-effective decisions on their retention promotions and incorporate this knowledge into the acquisition, cross-selling, and upselling initiatives.

Be sure to check out the second blog post on customer retention where we cover several types of retention programs. Then, read the third post on how to improve customer retention with predictive modeling.

If you’re interested in keeping more of your customers, download our special report by clicking below. It explains how predictive churn modeling can help reduce customer attrition.

Or, if you’d like to start a conversation with us right away, contact us today.


[1] Emmett C. Murphy and Mark A. Murphy “Leading on the Edge of Chaos.”

[2] Bain & Company, “Prescription for Cutting Costs,” 2011.