Of all the metrics a marketer can track, retention is easily among the most important. It is far easier and less expensive to retain a customer than it is to acquire one, which makes low churn essential for sustained growth. Churn management is a significant contributor to the long-term health of any organization as it ensures that customers understand the brand’s value.
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Despite this importance, churn management is, in many ways, a reactionary practice — brands often do not start optimizing services and messaging until churn is already a concern. By the time marketers are working to improve these services, customers have already lapsed. One ideal solution is to define at-risk customer segments in advance, so they can receive targeted messaging about your brand’s value before they churn on you.
More from PostFunnel on customer churn:
7 New Strategies to Reduce Customer Churn
Reduce Churn, Even Post-Holiday Season
How to Lose a Customer in 10 Days
Let’s take a closer look at what marketers need to predict a lapsed customer, along with strategies to prevent them from churning:
Collecting and Preparing Data
When predicting churn, it’s essential to understand what drives customers to leave in the first place. Doing so requires pre-existing data that can be analyzed for churn indicators and then applied to your current customers. Unfortunately, merely gathering and preparing this information can be the biggest challenge of the entire process. Emerging brands might not even have enough data to produce reliable results.
Neil Patel outlines four kinds of consumer data that are important to keep in mind:
- Customer features: Personal and demographical information relating to the customer, such as age, sex, or income. Depending on your industry and the scope of collected features, this might include details like income or education level.
- Support features: Characteristics of a customer’s interactions with the brand, such as customer service reviews.
- Usage features: Characteristics of how a customer uses your product or service. These features are often specialized to support a particular industry.
- Contextual features: Any other contextual information that might affect the customer experience.
Each data element will need to come from a specific timeframe, usually a relatively recent 6-month window. If automated software is being used to collect or analyze this data, it may need to be cleaned of extraneous elements.
Predictive Models
Once you’ve gathered data, you’ll need to come up with some kind of predictive model that correlates certain activities with customers who are likely to churn. Given the size of most cleaned customer data sets, this is best accomplished by using third-party machine learning software or a manually coded predictive model.
Third-Party Platform
As machine learning capabilities become more accessible, it’s relatively easy for marketers to invest in platforms like Google Cloud’s ML Engine or BigML to manage models for you. These services require users to upload a cleaned database to a free or premium account so it can be analyzed for correlations.
Manual Calculations
If members on your team have programming expertise, it’s possible to design and run your own predictive model. This Towards Data Science guide provides one example of how to manage a manual predictive model using the Python programming language and associated tools.
Whichever way you choose to manage your predictive model, it’s better to use tools such as these than to manually scan your customer data for trends. These services are far more precise and accurate in their analysis, providing more detailed insights that will drive optimization efforts.
Once You Know Which Customers Are Likely to Leave, Then What?
If everything has gone well in the previous steps, you should have a list of variables for a customer that is likely to churn. Now what?
This is the ideal time to take steps that reduce your churn rate before customers lapse. These strategies will help you get started:
Communicate Results with Your Team
It’s quite likely that any product or service changes made to reduce churn won’t be accomplished by a single individual. You’ll need to communicate these results to your marketing team along with any departments responsible for the features in question. In turn, they likely have insights for why features operate a certain way and actionable insights on how it could be improved.
Compare At-Risk Customers with Your Most Loyal Customers
One of the most effective ways to prevent churn is to treat new or at-risk customers just like your loyal customers. If you compare at-risk profiles with the profiles of customers who are unlikely to churn, marketers can better understand what customers truly value about their brand. If you can figure out what value isn’t translating to the at-risk group, you might be able to replicate the low-churn effect.
Work with Customers On Solutions
In many cases, marketers will understand the signs of an at-risk customer, but not why they chose to churn. When this happens, the easiest thing is to ask the customer directly. Marketers should periodically solicit feedback from at-risk customer segments to better understand their experience. Their responses can then inform optimizations or future development, preventing churn before it takes place.
A certain amount of customer turnover is inevitable, but there are many ways brands can minimize their churn rates. Marketers that take additional steps to be proactive about churn will not only prevent lapsed customers but find new ways to enhance the value of their product or service.