January 13, 2022
As much as companies prefer to focus on how many customers they have, it’s equally important to pay attention to how many customers you are losing, otherwise known as customer churn. By conducting a churn analysis, you can identify which customers are leaving and why. You can even begin to predict which existing customers are looking likely to churn, and then you can take action to prevent and reduce churn.
Instead of getting frustrated when you lose customers, think of it as a learning experience. You can use churn analysis to find new ways to improve your customer retention and make your business stronger than ever. At first glance, a churn analysis will give you statistics and will identify the percentage of your customers who don’t come back for more as compared to the percentage of repeat customers. When you dig deeper into the results of a churn analysis, you will see trends that will help you address challenges head on and improve your bottom line.
Read on to learn more about churn analysis, including when and how to conduct it.
It’s important to always have a sense of the churn situation in your business. That being said, it’s not necessary - and in fact would be overkill - to conduct a full customer churn analysis every day. Rather, it’s something that should be done on a recurring basis based on your business needs. Many businesses find it most useful to update churn numbers either monthly or quarterly.
However, because churn can be extremely expensive for a company and it can compound and grow quickly, if you start to see any red flags, that is a sign that it’s time to do a churn analysis. For example, if you notice that your churn rate has jumped drastically in one month or you’ve seen a slow increase over a number of months, you should conduct a thorough churn analysis to figure out what is going on and what you can do to improve the situation.
It’s not always a “bad” event that should trigger a churn analysis. This type of analysis can also be very useful to measure the effect of a new onboarding process or the launch of a new product, for example. You may want to conduct a churn analysis after such an event in order to see what the impact - positive or negative - has been on customer retention.
Bottom line, have a set time when you plan to do a periodic churn analysis, but also keep in mind that there are certain triggers that will prompt the need for churn analysis outside of those scheduled times as well.
There are many benefits to your business when you conduct a customer churn analysis, including:
Remember, it costs up to 5 times as much to acquire new customers as it does to keep the ones you already have - so it’s worth the effort to conduct a churn analysis and work to prevent existing customers from leaving.
In order to make sure that your churn analysis is accurate and gives you truly actionable insights, you need to choose the right KPIs to measure. The following KPIs are useful when it comes to conducting a strong customer churn analysis:
When conducting a churn analysis, it’s important to also consider the different types of churn and the reasons behind why customers leave. Armed with this information, you can target your response and make sure you address the specific reasons why customers are choosing to leave.
The most common types of churn are:
When churn happens, there are different reasons that might lead a customer down that route. Let’s take a look at some of the motivators behind several common types of churn:
When a customer actively cancels their use of your product or service, it could be for any number of reasons, including:
If a competitor is offering a better deal or can otherwise convince your customers that they are the optimal solution, you will see a steady stream of customers filing out of your (virtual) door. Always keeping an eye on what your competitors are up to can be the difference between retaining customers and watching them go.
You may see customer churn simply due to the fact that some people won’t realize that they had to actively renew a subscription so they just let it lapse. These are difficult customers to get back, so it’s worth developing relationships and making sure you remind the customers you want to keep that they must renew subscriptions. Make it worthwhile for them so that they know that you care about them and want to keep their business.
Now that you know what churn analysis is and what the benefits are, you may be wondering how to actually perform a customer churn analysis. Luckily, there are plenty of tools out there that can help you, and conducting a churn analysis is not as difficult as it may sound. There are lots of different models and ways that you can go about it, but here’s one way that we suggest:
Get yourself a tool that lets you gather and then see all of the metrics of your choice in one place. This is where you will collect the data about your customers’ behavior and will be able to drill down and see exactly what is going on at any given time. A good system will let you choose which metrics you want to see on your dashboard - such as cancelled subscriptions - and will also include automated alerts so that you’ll know when any red flags arise that require investigation.
Trying to look at each customer individually is time consuming and won’t necessarily give you any added-value over analyzing groups - or segments - of customers who behave in similar ways or share common traits. Most analysis tools will allow you to segment your users or customers based on income, industry or other specific demographics such as geographic location.
Once you’ve gathered the data and analyzed it, you will reveal the answers as to when and why churn is happening in your business. You’ll see the percentages of active vs. passive churn, as well as where in the product lifecyle most of the churn is happening. The end result is the answers to the questions of which customers are churning and why. You can bolster the results you get from your analytics tool by sending out surveys and simply asking customers what made them leave.
You can play around with the data you’ve collected in many different ways in order to find the answers to all of your burning questions about when and why churn is happening. You can drill down to get as much detail as possible and really understand the reasons behind customer behavior. You can then use this information to create a plan to encourage your best customers to stay.
While there are many tools that can automatically calculate churn for you, some people may prefer to do it manually. It is possible - and easy for those who are comfortable with software tools - to use programming tools like Python and R to conduct a thorough churn analysis. A more simple analysis can even be done using Excel.
Python is a programming tool that uses data science and machine learning to help make predictions. It’s possible to use a data set to analyze customer behavior and attributes and predict the likelihood of churn. In Python, you can choose the features within the data set that are relevant to whether or not a customer will churn. For example, if a bank is analyzing which customers may churn, they may look at things like credit score, location, bank balance, age, and length of time the customer has been with the bank. An algorithm is applied to the data and then predicts, based on the chosen features, which customers are most likely to churn.
This analysis can then be used to take preventative measures and prevent the churn from happening.
Similar to Python, R is also a programming language - it is designed for computing statistics and can be a powerful tool for churn analysis. It operates in a very similar way to Python in terms of choosing a data set and then applying a statistical model to it in order to predict which customers are most likely to churn.
Excel is a simple yet powerful tool that can also be used to conduct a churn analysis. For those who don’t know how or don’t want to use a more complex tool like Python or R, Excel is a good option. All of the relevant data should be added to an Excel spreadsheet and then you can insert a pivot table and use the “churn rate calculation” in order to identify variables that correlate with churn. Variables with a high correlation indicate the types of customers who are likely to churn.
Whatever software or program you use to conduct your churn analysis, it’s ultimately based on the churn rate formula, which is as follows:
Churn rate = number of lost customers/ending total of customers
The best way to understand this is via an example:
Company X is a subscription business that began the year with 100,000 customers and ended the year with 120,000 customers. On the surface, that shows a 20% growth rate which is a good thing.
But, if you dig below the surface you will learn that the company lost 40,000 existing customers during that period and the growth was due to increased spending on acquiring new customers. In this example, churn would be calculated as follows:
40,000 lost customers/120,000 ending total of customers = 33% churn rate
That’s a high churn rate - working on lowering that will result in even better growth for the company.