Interpreting Your Data to Reduce Customer Churn

Customer churn is costing you money. Most times it’s costing you a lot of money! Fortunately, increasing your customer retention by only five percent can increase your profits by 25 to 95 percent. So, you might be wondering how you can convince a customer to stick around. Well, it all starts with analyzing your customer relationship management (CRM) data.

CRM systems have transformative potential when it comes to reducing churn. However, very few businesses understand how to maximize their systems to improve customer success. If this sounds like your organization, you should focus on three areas when you track and analyze churn. After you’ve finished reading this article, you will understand how to identify problem areas and monitor change as you implement strategic solutions.

Analyzing Customer Behavior

Have you ever noticed that some of your customers behave the same way—especially if they have similar characteristics? Particular groups of your customers do in fact exhibit common behaviors.

If you can learn the common behaviors associated with particular groups, you can predict how similar customers will behave under similar circumstances. For example, you can estimate what a customer’s reaction will be to a future marketing action or an outreach moment from a team member.

Luckily, building customer behavior models is fairly simple with the help of a CRM tool. The confusing part is often translating the data into all-in-one visualizations. Many companies work with a data visualization, like we do Visual Cue, to better understand their reports.

Image #1 (1)Another aspect of customer behavior analysis that can challenge companies is deciding how specific the models should be. Businesses that already use software designed to track user behavior can generally make their reports very granular. After all, one of the advantages of a CRM software is that it collects and organizes a plethora of specific user data.

Businesses that don’t have a user behavior software in place may need to work with their engineering team to see if they can provide the data or create a tool that will collect it moving forward. Overall, the more specific you can get with customer behavior data, the better. An in-depth report will take time to produce, but it’ll provide you with the best picture of what behaviors lead to customer success.

Considering Customer Age

Another effective way to analyze customer churn is to look at churn by age. Here, age is calculated by how long customers have been with your company. In other words, one age group could be “first month,” and another could be “twelfth month.” After all of your users are sorted by the amount of time they’ve been customers, you can start to analyze customer abandonment rates.

Viewing data in this way will help you grasp customer information in a simple way. It will also allow you to know your customers’ behaviors as they age. Chances are you will notice some similarities that you can try and address.

Companies that have high churn rates within the first few months can and should be working on improving their onboarding process. On the other hand, businesses that notice increases in churn several months in might find that their rates increase when customers need to renew their contracts. If spikes occur during specific time frames for various reasons, and addressing these reasons can make all the difference.

Risk_Pg11Once you implement solutions to problem areas, take the lessons learned there and apply it to another problem area in your business. Step by step you will be able to conquer all the issues your company faces.

Churn By Time Frame

In some ways, analyzing churn rates by time frame is similar to analyzing churn rates by age. The main difference is that time frame data can be harder to track and analyze—especially if you’ve been in business a few years. Again, this is why many companies work with a data visualization tool or software. It’s all too easy to become confused by a chart that has hundreds of lines.

To group customers by time frame, you must first define your parameters. There are several common ways to do this, but one of the most popular is to group customers together my month. Under this parameter, all customers who purchased in January of 2017 would be grouped, all customer who purchased in February of 2017 would form another group, so on and so forth.

When you analyze data based on time frame, you’ll notice two benefits right off the bat. First, you’ll find that your numbers aren’t influenced by customer acquisition. You’ll have clean, clear data that speak only to your customer abandonment rates. The second benefit is that you’ll see clear patterns for various groups.

After you create this report, analyze patterns and look for causes of customer churn. If you find that a lot of customers joined your company in November but abandoned shortly after that, seasonality might be to blame. With this speculation in mind, you can begin to look into what happened in the month November to learn why that time frame group had a high churn rate. As soon as you figure out what led to the increase in abandonment, you can create a plan to improve your retention for the future.

This type of report may also provide you with insights on actions that improved customer retention. For example, if individuals that joined in April have lower attrition rates, chances are you did something right to influence them to stay with your organization.

Once you dig into the month, you can begin to determine the elements that incentivized customers to stay active. Then, you can work on reducing churn by creating similar conditions for your entire customer base.

The Impact of Reducing Customer Churn

There are many ways to make reports, but every single one you create should inform strategic action. Overall, the easiest way to interpret data is a clear and accurate visualization that will help you gain insight into your customer base.

Equipped with strategies to collect and represent your data, you’ll be able create plans designed to influence churn. You might instruct your marketing team to run campaigns that share similarities with past successful campaigns. Maybe you take the time to work with your call center employees and teach them about certain behaviors they can perform to reduce churn.

As you roll out your solutions, make sure you understand how you will measure their impact. A sound monitoring system should be in place you so you can promote the aspects of your customer churn reduction plan that are working.