When you know that your revenue is based on a recurring contract – be it monthly or annual, you might as well be aware that every client can put a dent on your cash flow. And that is why high retention score is of huge pertinence for you.
Wouldn’t it be nice if there could at least be an easy way to predict when and how are your clients going to cancel? Well, that is what churn prediction is all about.
Creating a churn prediction model helps you revamp to radical changes that eventually cut down churn rates. You need to understand how this churn impacts your revenue targets. Only then, will you be able to make predictions on how to manage these issues in the near future.
Let us begin by understanding the types of churn we see in the industry. On one hand, we have a voluntary churn, where the clients choose to cancel their service. There could be several reasons behind it – they could have received a better service provider, the subscription rates could be comparatively low or perhaps a dissatisfied customer experience.
As Judith Soloman rightly puts it, “What we call churn in coverage is a big problem – i.e. people losing and regaining coverage over relatively short periods of time, a great deal of churn occurs at the time renewals of coverage are due but non-payment of premiums can also cause churn.”
While on the other side is the involuntary churn. That happens when a client’s account is canceled when they were not aware or did not intend to do the same. This could happen due to a failed payment system, an expired credit or debit card, or maybe exceeding the limit of available transactions.
Needless to say, customized client retention is vehemently tough considering the time and efforts needed to be spent behind a single client. With that, the costs would be high and could also outweigh the extra revenue.
Nonetheless, if one could predict before-hand which customers are on the verge of unsubscribing, you could cut down the churn rate by directing them solely towards these clients. Hence, we have curated the following points that can help you do churn prediction.
Customer Profiles to build a Churn Model
The first step in customer churn prediction is accessing your customer profiles. Every client data point will be useful to create a targeted churn model. Gather as much information you can from the profile. Bits like the employment status, size of team, designation, and more.
Next, start analyzing the spot patterns in the churned out customers, linked to their demographics. Remember, different client types will churn in different manners. Having done that, gather information on more private matters like the billing and purchase history.
You will get a clear-cut picture in your head when you have access to vital information like the date when a client signed up, date when they unsubscribed, and their entire payment history. That is how you will ascertain more about account churn in particular.
For a SaaS company, delve into features like the number of times the client logged in, total time spent on the app, or the actions performed on the app.
Next what you need to do is the extraction of data. Connect your database to bring in exact information required to compute values for each client.
Compare the analysis whether it was a voluntary or an involuntary churn. Also, note that the reasons for a month-old client to a year-old client are way different.
Prediction via Web Interface
Next, you can rely on prediction tools such as Google Cloud ML Engine through a web interface. Once you have enabled this, the service will automatically make predictions. Or you could take up a mathematical modeling formula, namely the logistic regression. You can use all the customer information, prior churn data, purchase history, and turn it into statistical prediction.
With the end of that, you will be in a position to see the probability of certain account churns. Now you need to formulate a plan, based on these findings to target each of your clients.
Keep track of how it impacts your churn rate over the coming months. Use a decisive retention strategy too for getting the best of customer success.
Time for the Drum-rolling Results
Now that you have a model to provide you all the requisite information. Now, all that you wish to do is to make predictions on all the customers and see who and who is not at the risk of churning. Also, note that the total time and the churn information will not reside in the database.
With the results, you will not be able to see the predicted results. This will help you collect your data and make vital decisions, needed for the future of your business.
Using the aforementioned tools will demystify the modeling and the retention process. It will give you enough time that you can spend on keeping your clients satisfied and will eventually grow your business.
A lot of SaaS companies take to churn prediction to stay alert in the industry. It is useful in weeding out both the types of churn discussed in the article. The fewer customers you lose to churn, the more is the revenue that you will be able to capture from them.
Aside from that, do not wait till the end of time to use a churn model to predict churn. From the beginning itself, strive to provide a seamless experience to all your clients. Be there for them when they need you. 22
If they are facing some trouble, fix it for them as quickly as possible. They must know that you are there to help them out no matter what. Following the aforementioned practices will generate a huge reduction in account churn, which is much need for any given SaaS.
You might also like:
- The SaaS Churn Handbook – Everything you need to know for understanding and preventing customer churn.
- To understand how SmartKarrot helps top SaaS companies predict and prevent churn, Request a Demo.
Simran hails from the content marketing backdrop with extensive knowledge in blogs, articles, and technical whitepapers in the non-fictional domain. She uses her ‘gift of the gab’ to explore new possibilities on her way and to make an exquisite impact on her readers. In her spare time, she likes to read journals on artificial intelligence or play with her cute kittens.
Published May 28, 2020, Updated December 30, 2020