The stress of onboarding new customers and keeping current ones is constantly exacerbated by the looming threat of high customer churn rates.
Lost customers, or customer attrition, aren’t completely avoidable — but how many is too much? When does churn become a problem?
The answer varies based on the unique attributes of your company.
That’s why it’s crucial to compile, analyze, and build customer retention strategies with churn data.
What Can You Do to Lower Your Customer Churn Rate?
You probably already know what churn is, but as a refresher, here is how you calculate your churn rate:
This number acts as the basis for building your customer attrition and retention strategies. Churn rates tell you that retention issues exist, but not exactly which ones.
Customer Churn Analysis
Evaluating the where and why of losing your customers starts with customer churn analysis.
Analyzing customer churn data allows you to identify patterns in customer behavior in relation to your organization’s actions. These patterns act as signals to help you identify customers that may be on their way out the door.
Data types used in customer churn analysis come from customer and account exec feedback which include:
- Amount and content of complaints
- Number of customer support tickets opened
- Invoice payment frequency
- Number of webinars attended
- Amount of nurture emails sent, and how many received engagement
- Frequency of promotions from both your organization and your competitors
- Price changes in plans
With this customer data gathered up and quantified, your company’s customer journey can be mapped out by stage. This map should highlight the most likely causes of your churn.
From there you can predict which customers are most likely at risk to lose your business.
Predicting Churn
To make proactive changes in customer attrition rates, you can use your churn analysis to build a predictive churn model.
This model is made of statistical predictions coming from your churn analysis data. Do customers leave when prices go up? When competitors come out with new products?
Building predictive churn models requires some expertise and mathematical knowledge, but fortunately there are tools to help you with that.
What SaaS Tools are Great for Churn Analysis?
With the support of data scientists or analysts, machine learning tools can compile and analyze customer data listed for analysis. Customer data platforms (CDPs), the most commonly used machine learning tool for customer data, create quantitative patterns for building usable models.
And though machine learning and automated systems are great, humans are still needed to fully understand why other (human) customers discontinue purchases, end contracts, and leave back feedback.
Customer Engagement in Reducing Churn
Fully understanding customer behavior and what parts of it contribute to attrition requires efficient and strategic engagement.
ZoomInfo’s Engage includes features such as email customization and an auto-dialer to increase calls and emails with targeted messaging. By identifying which messages get the most positive attention from your customers, you can make adjustments to boost your customer nurture and retention game.
The Next Steps in Reducing Churn
So what’s next after these models are built and causes to customer attrition are found? The next step is to put together and implement a solid customer retention strategy — ultimately reducing churn.
Whether or not you invest in tools like CDPs, your customer analysis strategy can include any amount of data you want or need.
In the end, addressing and acting on attrition issues creates better customer relationships.