All companies have a few things in common. B2B, B2C, SaaS, and e-commerce, it doesn’t matter.
- No companies want to spend too much for acquisition, yet they want high acquisition rates.
- And no company wants high churn rates.
But how do you actually detect and prevent churn?
Since getting new customers costs a lot more than keeping the ones you already have, it’s important for businesses to use data to predict churn.
In this guide, we go deep into the world of churn prediction and show you the best tools, strategies, and techniques for doing it.
Without further ado, let’s start with:
- Churn is one of a your biggest enemies in increasing your customer time value and ARR.
- The easiest way to reduce revenue churn rate and increase retention in SaaS is to set up a churn prediction system using a Customer 360 tool that provides insights.
- Customer churn prediction is analyzing the customer’s likeliness to churn based on past data.
- To predict churn, you need to gather data, segment your users, create a churn prediction system using machine learning or AI, and put all the data in to gather insights.
- If you use a customer 360 or track your customer health score with another tool, you will have a headstart, as the metrics required for a CHS are usually very similar to those needed to detect churn.
- Predictive customer scoring tools, like UserMotion, can help you calculate your customer health score, predict and prevent churn with insights and alerts, and notify you about upsell opportunities.
What is Customer Churn in SaaS?
Customer churn is losing a paying customer because they are no longer interested in your solution. This “churning” user doesn’t have to be a person. It can be a booking, an account, a deal, and so on.
In SaaS, preventing churn becomes much more important than other business types because the primary source of revenue is usually the Monthly Recurring Revenue (MRR). Retaining customers, or in other words, the customers that don’t churn and keep paying every month, are the backbone of all SaaS businesses.
Preventing churn is more important than acquiring new customers because customer acquisition is almost five to seven times more costly than retaining existing ones.
What is Customer Churn Prediction?
Customer churn prediction is analyzing the customer’s likeliness to churn based on past data. In order to accurately predict customer churn, you can get help from a Machine Learning (ML) tool, or ask your data scientist to build a decision tree model powered with AI.
Or, instead, you can use your Customer 360 tool to do all the hard work for you.
In order to accurately feed the churn prevention mechanism of your choice, you will need data.
After all, it’s all about the numbers and the math. Let’s see what kind of data you need:
How to Predict Customer Churn?
I already mentioned that you need a mechanism and data. Without one, the other cannot work. That’s because it’s almost impossible to keep a human eye on every single customer and see if they are taking the right steps or not.
So, here are the details of the two sets of information that you need:
- Important Metrics
- Customer Health Score consists of a few separate metrics itself. Thus, it is the easiest and most accurate way to determine whether a customer is on the verge of churning, or like you just as you want them to.
- Satisfaction Score is a part of the customer health score and can signal upsell opportunities and churn risk almost as accurately as the Customer Health Score.
- Historical Data about your users is crucial in detecting changes. Past NPS scores, satisfaction ratings, changes in activity time and log-in frequency can help you (or your mechanism) determine that “this user needs help”.
- Predictive Mechanisms
- If you work with a Data Scientist, ask them to build you a simple mechanism that can detect predefined changes and signals in users’ behavior.
- Or, more simply, invest in an ML tool that will analyze the data and metrics that you provide it with in order to get a faster result. Training the machine learning system will be faster than building a working system for yourself.
- Or, skip all the hassle, invest in a Customer 360 tool, and let the tool’s customer health scoring (CHS) system alert you via Slack or email when needed. And you don’t even have to feed it with data since the whole point of having a Customer 360 is integrating all actions in one place, and letting the tool do most of the things for you.
The 7 Steps to Predict Customer Churn
I believe that by now, you have an overall idea about how churn prevention works and what you need for it.
But just like everything else, we need details and steps to ensure all our actions pay off and nothing goes to waste.
So, here is your 7-step, step-by-step walkthrough on how you can prevent churn in your business and maximize retention.
1. Data Collection
You need lots of data. By saying lots, I don’t mean the number of metrics. You need lots of historical data to determine why previous customers have churned and determine whether there is a pattern.
- Do they churn more if they don’t use a certain feature?
- Do they churn more if they log in under a certain number of times each month?
- Are the low-touch customers more likely to churn?
- Does a number of support tickets affect their likeliness to churn?
- Has your company had a time when churn was highest/lowest? What changed since then, and at what point did you see the most drastic difference in churn numbers?
2. Segmenting Customers
Now that you have your data, you can focus on the users and start segmenting them.
Since different users with different needs and usage patterns don’t churn because of the same reason, you need to put them in separate segments.
For instance, segment them based on:
- Demographics: their location, region, company size, and year signed up to your company.
- Behavior and usage: Do they use a certain feature more/less, how often do they log in, did they complete the onboarding?
- Contract terms: What pricing plan are they on, did they sign up for a monthly/quarterly/yearly deal?
3. Customer Health Score
Now that you have your data and your segments, your Customer 360 tool can create a customer health score dashboard for you.
The tool will use all this data to create different health scores for different metrics, different segments, and different features. If you need an overall look over all customers and all features, then you can also create a global health score and have a holistic view of everything.
4. Predictive Algorithms
Most customer 360 platforms nowadays have a built-in churn prevention algorithm that automatically analyzes all the data above and gives you an accurate prediction.
If your tool isn’t capable of doing this, don’t worry, since Usermotion is an easy alternative that will not only accurately detect churn risk, but also alert you on the platforms of your choice so that you won’t even have to check yourself every single time.
5. Churn Signals
Back to our topic, if your customer 360 (or the detection algorithm of your choice) comes upon:
- A decrease in log-in frequency,
- An abandoned onboarding process,
- A decrease in a specific health score,
- Repetitive similar support tickets,
- Or even no support tickets at all,
It will identify it as a “high churn risk” and compile all its data for you to have easy access.
Of course, the examples I provided above are only examples, and the signals for your company can vary. Just set up your parameters in your tool, and you are good to go.
6. Get Notified Immediately if a Signal Appears
By combining historical data with metrics and the customer 360 tool’s algorithms, you get little baby notifications.
Those baby notifications are cute, and let you know that it is time for you to check on them.
They are cute because they help you make your business more profitable by focusing on the right customers at the right time.
7. Save the Revenue
What you did by following these steps is create a self-sufficient data machine that detects churn even before the customer knows they want to churn.
And now, your data is being analyzed and categorized by your customer 360, which also notifies you when you need to take action and only when you need to take action. Enjoy sparing your valuable time for the actual business instead of dwelling on data and trying to find “reasons for churn” while you can predict and prevent it beforehand.
Customer Churn Prediction for SaaS Businesses
So, what do you do with the data you gathered and the tools you have set up?
You level up your customer success game. Here is how:
The first step on your journey, as we have discussed, is spotting potential churners through customer 360, AI, and ML algorithms. Based on the results you get, you can start setting up a strategy.
It is time to work with the data that you have. You should now start crafting strategies and playbooks to engage and retain customers exhibiting churn signs. Those strategies can include:
- Engagement posts,
- A call with a customer success manager,
- An in-app alert, encouraging them to use that one key feature they haven’t used yet,
- A live-chat message within the tool, lets them know there is a related help article about where they are stuck.
- Post-Churn Analysis:
If customers do leave, analyzing their behavior to draw meaningful business insights and refining future strategies will be as effective as preventing churn itself. Try to go back to your segments and see which ones need more care. Is there something you can improve with your onboarding? Is there something about a certain feature that makes people feel like you are not the one?
- Continuous Predictive Model Refinement:
Use new data and feedback to improve predictive models and scores/weights over and over again. It is not only your product that needs to improve, it’s your methods and metrics as well.
Setting up a customer churn prediction system will save you hours each week, but you still have to improve and maintain it as things change with your product or your customers.
So, how can you make all these processes even easier?
There are 3 methods:
1. Customer 360 in Customer Churn Prediction
A customer 360 basically automates all aspects of churn management, starting with:
Gathering Data Points:
A Customer 360 such as Usermotion will automatically gather most of the data through integrations from your analytics tools. This way, you won’t have to jump in between tools or sheets. The tool will also help you determine which metrics could matter the most to your business, so you don’t have to be an expert on the matter.
Interpreting the Data:
It is not easy to understand the narrative behind numbers and patterns. The Customer 360 will analyze the data and put it in simple terms and graphs so that everyone on your team can understand the data.
From Data to Action:
As I mentioned before, a customer 360 helps you determine churn before it happens and leads you on how to act on it. By utilizing the tips and insights that the Customer 360 tool provides, you can spend less time on ideation and focus more on execution to maximize your retention rates.
2. Customer Health Scores in Customer Churn Prediction
Parameters of Health Scores:
The metrics that help you (and your tool) create a Customer Health Score analysis are more or less the same ones that will help you determine churn risks. Those are more or less the ones below:
- Product usage,
- Customer fit
- Account activity
- Feature usage per month
- Growth of account
- Renewals vs. payment issues
- Last seen period
- Support tickets
- Product feedback metrics (such as NPS)
This is why setting up a Customer Health Score for your business can give you a headstart on churn prevention.
Health scores serve as early warning systems. To prevent churn, what you want to do is put together certain data that might indicate churn risk, categorize them, and then visualize all the data.
The process is exactly the same as the Customer Health Score, and it can also give you insights on upsell opportunities. That’s why using CHS for your business will prove more effective than most metrics.
Adapting to Dynamic Health Scores:
Since you will be setting up health scores for different segments, features, and parameters; I’m sure you understand that the scores and parameters are subject to change over time. Along with the parameters, health scores change, and businesses need to adapt in real-time.
That’s why using a customer 360 for CHS is crucial, as the system can adapt to changes, and you won’t have to set up all the changes manually.
3. Machine Learning and AI in Churn Prediction
The Power of Prediction:
As it is in all kinds of fields that are involved with data, manual work over hundreds of cells and numbers takes time. Instead, you can get help from a program or tool that uses ML (machine learning) or AI in order to speed up all your processes. The same goes for Churn Prediction.
Here is how:
Training the Models:
Those tools or programs need to be trained to understand how they can help you best. You need to have quality historical data on patterns and metrics and feed the program with that data to help it understand how your tool works and what it needs.
Once you finish the training and setup, the rest will be automated, and you will have lots of time to focus on other important aspects of your work.
Pinpointing churn and preventing it completely is almost impossible, as your product is not meant for everyone.
However, determining the main churn reasons and eliminating those for your ideal customer segments is not so difficult.
Simply adopt a Customer 360 tool, and follow the tips.
How do you predict customer churn risk?
To predict churn, you need to gather data, segment your users, create a churn prediction system using machine learning or AI, and put all the data in to gather insights.
How to reduce churn rate and increase retention SaaS?
The easiest way to reduce churn and increase retention in SaaS is to set up a churn prediction system using a Customer 360 tool that also provides you with insights.
What is the predict model of churn?
The customer churn prediction model is a predictive algorithm that calculates the likelihood of a customer churning. It uses customer health scores and various data points, such as product usage and behavior, to make its predictions.