How to Improve your SaaS Business with Customer Churn Prediction
Meta description: Learn how to use churn prediction based on customer data to improve your SaaS business and maintain the long-term success of your company.
Customer churn is inevitable for any SaaS business. There can be many different reasons why consumers choose to cancel an account, and therefore it is vital to get an understanding of which of your customers is most likely to churn.
By examining your consumers’ behavior, habits, and characteristics, you and your team can learn what exactly drove churning users away from your firm.
If you make sure to constantly and proactively analyze why former buyers never return you will be able to improve your churn rate. Learning how you can reduce customer churn for your business, will subsequently improve your retention rate and thereby ensure continuous growth and profitability.
Customer churn rate is the percentage of users that leave your firm during a given month, quarter, or year. Some examples of customer churn for SaaS businesses could involve non-renewal of a contract or service agreement, the closure of an account, cancelation of a subscription, or using another service provider.
And why is that important to focus on?
Because if the customer churn rate for your SaaS company keeps getting higher and higher it can eventually prevent the overall growth of your business. Therefore, you want to be focusing on what you can do to reduce the overall customer churn in your business.
Churn prediction consists of identifying customers who are likely to cancel a subscription at your firm. Statistics have shown that the average subscription-based firm has a 66% renewal rate, which means a 34% churn rate (2017, Forrester survey). So you see why this is worth given some attention.
When choosing a strategy, any business needs to focus on where they can get the biggest return on investment. Research has proved that it can cost five times more to acquire new customers compared to retaining existing users. So option three seems like the best strategy to concentrate on.
There are several things you can do to improve customer retention overall. Yet, individualized customer retention is often too time-consuming and costly. However, if you could predict in advance, which users are at risk of leaving, you could focus your customer retention efforts solely toward at-risk customers.
Customer churn prediction and its impact on your brand’s retention efforts
Before we get started on how to predict customer churn it is important to understand how customer churn prediction strategies can influence short-term marketing goals and long-term brand profitability.
Data has proven that those businesses that fail to forecast when certain customers will churn or solely focus on reinforcing their retention rates will keep seeing their users leaving for competitors.
For example, it has been found that firms lose about $1.6 trillion annually from customers who churn due to poor customer experiences in the past. Furthermore, 59% of consumers will stop buying from businesses after several bad customer experiences, and 17% will do so after just one bad experience (PricewaterhouseCoopers report).
Therefore, you need to learn how you can address your firm’s churn rate and keep your users coming back. When doing so, you need to recognize customer intent, track customer experience, and identify customer trends through regular churn analysis.
When prediction churn, you will be able to identify customers who are likely to cancel a subscription to your service based on how they use your service.
Churn prediction is based on machine learning, where future prediction is based on data from past events or experiences. So when predicting whether a user is going to leave within X months, the user is compared with examples of customers who stayed or left within X months.
In order to perform these comparisons, users should be represented based on information about what affected them to churn or not.
Each part of the information used to represent customers is called a “feature”. In relation to customer churn, there are four types of features:
1. Customer features: Basic customer information such as age, house value, college education, and income.
2. Usage features: Characterizations of the customer’s usage of your service.
3. Support features: Characterizations of the user’s communications with customer support including topics of questions asked number of interactions, satisfaction ratings, etc.
4. Contextual features: Other contextual information about the user.
You will see that customer and support features are quite generic, whereas usage and contextual features are specific to the service you are offering.
Depending on your SaaS business and your specific offerings will influence the time frame you should be using for your data. In a firm that sells monthly plans, it normally makes sense to examine who is at risk of canceling now, based on last month’s usage. In this case, you should only be computing usage feature values based on the previous month.
In other cases, it might make more sense to examine usage over 2, 3 or 6 previous months in order to capture information that has an impact on whether a customer churns or not. Then you would have to include average usage feature values over this duration.
When you have decided on how to represent your customers, you need to collect historical data of up to X months in the past. The purpose is to create a dataset of examples that consist of “inputs” (users) and associated “outputs” (e.g. churn or no-churn).
To do so, you should create a script that: 1) connects to your customer database to obtain the required information to estimate feature values for each user, and 2) put these values into a CSV file. In this CSV file, each row should represent a customer and each column a feature. Then you have a CSV file that contains your customer dataset.
Step 2: Create a churn prediction model
Besides gathering your data, you also need to start asking the right questions to uncover “triggers” that lead to customer churn. You should be asking as many questions as possible, in order to test, retest, and qualify your hypotheses and data before you start to implement a model.
Below you will find examples of three questions you can use, to get an understanding of how to start your data investigation process.
2.1. Q1: What is the % of correlation between available data points and churn?
This sort of question should give you answer such as; “71% of all churning users had filed an online complaint”, or “57% of lost users have never downloaded the free software we offer”.
By gathering such answers, you should be able to see a pattern of correlation in particular from data points to declining users.
Furthermore, you have to ask the same questions to your current customers. In that way, you will know that you aren’t just making assumptions about something being a direct cause of users churning. Chances are that the same percentage of your current users share the same data values.
2.2. Q2: At what point in the product lifecycle did your consumers leave?
The next questions should address at what stage during the product lifecycle your customers left.
Your questions could including examples such as “did your users leave when the subscription expired and then they did not renew it?” “Or was it related to a holiday or other external events?” “How long did your users stay before churning?” “Did they stop to use your service before they officially left?”
Understanding the answers to all of these questions is vital to learn the critical points in the consumer journey and where you need to focus your attention.
2.3. Q3: What was the login pattern? Or how many times did they use “X”?
By examining your consumers’ login patterns and frequency of usage, you will collect more detailed data over time. During this process, you are searching for something unique about the pattern and quantity that isn’t related to a current subscriber’s pattern and quantity.
In several instances, you will start to see a few high correlation attributes. Finding those unique correlation triggers is essential to the final step in the model creation.
2.4 Putting it to Together
When you have asked all the right questions and obtained all the relevant data, you will start to see some interesting patterns of churn.
You might find some very high correlated data points that are not as differentiated as you would have hoped. We advise you to find a least three high correlation attributes.
If you can’t – keep searching!
In those cases, you are presumably not asking the right questions or you do not have the right data points in your database just yet.
Once you have all the right churn triggers classified, you are ready to start designing the final churn model for your business.
Step 3: Start Testing Preventative Measures
At this stage, you should have a system in place that can distribute specific churn scores to your consumers. Then the next step is to start testing and learning into your response program.
The predictive scores you will receive from the model will be assigned to your users on an individual level. Consequently, you need to build a methodical approach where you can test the most efficient response system.
Key elements you need to test includes:
● What to say: What information or offers do you think can prevent potential churning customers from leaving?
● When to say it (and how frequently): When should you give important information? Should it be just after purchase, right before churning or at intervals during the consumer journey?
● Who does it impact the most: Which churn score intervals are most responsive to the messages/offers you provide?
The great thing about this type of testing is that you will end up with a clear indicator of success: an enhanced churn rate!
5) Churn prevention doesn’t need to be hard
However, customer churn prediction isn’t all you have to do. Once you have created your churn predictive model, the next step is to proactively affect those customers who are likely to churn.
One of the most common and simple reasons why consumers are churning is that they are not being helped to get value out of your product or service. In those cases, you should be communicating directly with the customer to turn them from at-risk to satisfied users.
For example, you can send out an email to customers that are not getting the full value from your products. It is also a good idea to email users, whose subscriptions are running out, leaving them at risk of delinquent churn.
Make incentives that will make your consumers feel valued and wanted. This can be done by offering promos and discounts or offering loyalty programs.
Such incentives will most likely help persuade them to stick around, especially if your market competition is strong.
It is particularly important to make relevant incentives towards customers who may have unsatisfied needs. Otherwise, they might be churning immediately. Incentives can help you buy time while you solve performance issues or expand your service.
Some customers will churn no matter what you do and no matter how successful your churn prediction is. But you need to keep going. Look at your churn numbers and find out what went wrong with those users.
The best strategy for churn analysis is to map out your customer’s journey with your product. This allows you to compare the customer’s journey with your churn data, and observe where the risk of churn is highest. Then you can start to focus your effort on the most crucial points.
Overall, make sure to carefully evaluate the factors behind your churn, and make this data work for you. A practical approach to churn prediction will allow you to understand the reasons why consumers might churn and respond to them.
So we urge you to embrace a predictive solution to handle your churn. That will give you clarity and ultimately help you decrease churn in your SaaS company.