Updated: Oct 3
Customer retention is one of the primary growth metrics for subscription-based businesses. Competition is extremely tough in the SaaS market with customers free to choose from a significant number of providers in order to achieve their business outcomes.
Churn rate is a critical pillar of customer satisfaction. The higher this rate, the worse you will be doing as a business to keep customers happy. It is a good indicator of growth potential and there’s endless amounts of data you can derive from the churn rate. When it comes to making use of this, it doesn’t get much better than machine learning and customer churn prediction. In this article we’ll dive into what exactly that is and how you can employ it in your SaaS business.
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WHAT IS CHURN RATE?
Churn rate is a measure of the number of customers who leave a company over a given time period. This term can also be used to describe the total revenue lost as a result of these departures.
A company’s changing churn rate can provide crucial insight into its inner workings, and can help you understand the health of your business. It seems obvious to say that the higher the churn rate, the worse it’ll likely be for your organization - it is a clear indicator that you are losing more customers and, in turn, should be taking steps to try and reduce this rate.
WHY IS PREDICTING CHURN RATE IMPORTANT?
So, with the impact of churn rate being clear, a company will require strategies in order to reduce it. Predicting likely churn is a good way to create proactive, combined marketing & customer success campaigns targeted at the customers you want to keep but that are showing signs of leaving.
Thanks to big data, forecasting customer churn is possible. During churn prediction, you’re also:
Identifying at-risk customers
Identifying customer pain points
Identifying strategy/methods to lower churn and increase customer retention
HOW TO PREDICT CHURN
Many SaaS organizations incorrectly believe that customer churn is the direct result of customer dissatisfaction, which is not necessarily the case. It is in face possible for customers to be unhappy with their experience but stay with the service regardless.
This is because of the ROI.
In short, your customers subscribe to your service because they want to see positive results. No matter how great an experience is, this won’t change much by way of customer retention if they fail to see results from your service.
On the other end of the spectrum, customers who experience a great service but don't see ROI will likely end up churning. Thankfully there are still ways to catch it before it happens. A big indicator of unexpected churn is a period of inactivity after the initial sign-up. The best way to tackle this is to constantly reinforce the ROI your customers are getting. Sharing these numbers with them will be a big influence on retention.
Talk to your customers
In its simplest form, preventing customer churn comes down to communicating with your customers. This is something machine learning cannot replicate, and getting your customers on the phone to have an honest conversation can often be the most effective way to learn about and solve their problems.
This is not taking away from all of the other methods of communication which are also very effective at preventing churn, however. Emails, webinars, and social media are all valuable ways to communicate with your customers and determine whether churn is beckoning in the future.
Monitor your competition
Customers can sometimes be happy one day, unhappy the next. Most are extremely satisfied with you when they sign up, but time passes and that may diminish. This is often revealed by the request of a new feature or discount. Be cautious - this could be the first sign of future churn.
To quell this before it happens, keep one eye on your industry, more specifically on any and all competition within your market. Market dominance a year ago can often be redundant today, as there could be a wide range of new features or enhancements developed that now dwarf your own.
The best way to prevent this is to create a process that allows you to monitor all feature requests and share them with your product team. By giving your customers a say in the product development process, you can keep on top of their needs and greatly reduce the risk of losing them to your competitors.
Determine product issues
Product issues are one of the top reasons for customer churn, and although a certain number of support tickets show customer health, since they’re interacting with your product, this only applies to an extent. If your customers are facing problems on a regular basis, it’s only a matter of time before they take their business elsewhere.
The best way to prevent this is to set up a line of communication between your support and product teams.
When it comes to customer experience, your support team must be able to tell the difference between a healthy amount of support tickets and an unhealthy amount. Once the latter threshold has been reached, your product team needs to be informed so they know how to fix it if a problem arises. They can then use this information to identify and resolve recurring product issues.
Utilize Machine Learning
Machine learning can be described as a branch of artificial intelligence, which looks at the capability of a machine to imitate intelligent human behavior. It allows software applications to become more accurate at predicting outcomes without being programmed to do so. Machine learning algorithms utilize historical data as input to predict output values.
Machine learning is important as it gives businesses a clear view of trends in customer behavior and operational patterns, and further supports new product development. Many leading companies today, such as Google and Facebook make machine learning a central part of their strategy. It has become a vital competitive differentiator for many different companies.
HOW CAN THESE METHODS HELP PREDICT CHURN RATE?
Detects customers who are at risk of churning
When it comes to identifying customers who have the potential to churn, machine learning algorithms can become crucial here. They can reveal many of the shared behavior patterns of customers that have already left the business. Machine learning and data analysis algorithms can then check the behavior of current customers against these patterns and identify any potential churners.
Many subscription-based firms benefit from using machine learning for predictive analytics to discover which current customers aren’t fully satisfied with their services and aim to solve these issues before it’s too late.
But it doesn’t just stop at maximizing retention. Software that uses these techniques can be useful tools for customer success & delivery managers to help them define which customers they should contact. This way, employees can ensure they are speaking to the right customer at the right time.
Predict the level of churn rate to accurately forecast and predict
Businesses that consistently monitor how consumers engage with products, encourage customers to share feedback, and swiftly solve their issues have much greater opportunities to maintain good customer relationships.
If a business has been gathering customer data for a prolonged period of time, they can much more accurately identify patterns of behavior in potential churners, target these customers at risk, and take the necessary steps to win back their trust.
This proactive approach to customer churn management is helped by the use of predictive analytics. This is an analytics type which enables businesses to forecast the probability of future outcomes or values by analyzing current and historical data. The big statistical technique at use here is machine learning.
HOW TO TURN PREDICTIONS INTO ACTIONS
So now we know how machine learning can help predict your customer churn rate, but how do we actually put it into practice? What data is needed? And what are the steps involved in implementation?
When it comes to machine learning, data science specialists need data to work with to begin with. Researchers must define what data they need to collect, which is dependent on the goal. Selected data is then prepared, processed, and transformed into a state which is suitable for building machine learning models.
Finding the best methods for training machines, fine-tuning the models, and choosing the best performers makes up a large portion of the work. Once the model that makes predictions with the highest accuracy is selected, it can then be put into production.
The scope of work data scientists undertake to build machine learning systems which forecast customer attrition to great success could look like the following:
Understanding the problem and end goal
Modeling and testing
Set your goals
The first step in performing churn analysis using predictive modeling techniques is to specify a churn metric. This can be a binary classification problem or something more complex. This definition can vary from business to business depending on differing requirements and problem sources.
Once the churn metric has been defined, the next step is to clean the data. This can include dealing with any missing data, or targeting any outlying bits of data and removing them accordingly. Data cleaning can go a long way towards reducing waste within the business, increasing productivity and improving decision making.
Machine learning and data analysis methods, specifically classification, have been widely used for predictive analytics because of their ability to manage complex relationships in data.
The aim of classification is to determine which category a data point (customer) belongs to. When it comes to problems with classification, data scientists typically use historical data
With defined target variables or labels, such as churner/non-churner. These are answers that need to be predicted in order to train an algorithm.
With classification, companies can answer the following questions with greater accuracy:
Will this customer churn?
Will a customer renew their subscription?
Will a customer change their pricing plan?
Are there any signs of abnormal customer behavior?
Customer churn prediction can be formulated as a regression task. This is a statistical technique that estimates the relationship between a target variable and other data values that influence the target variable.
If this is too hard, regression always results in a numerical figure, whereas classification instead suggests a category. Regression analysis enables businesses to estimate the number of different data variables that influence a target variable. Regression allows businesses to forecast the specific time period a customer may be likely to churn or get a probability estimate of churn per customer.
Develop your customer success approach
Designed to combat churn directly, customer success in its basic form is the series of tactics you can use to look after your customers from the point they make first contact with the business, to regular reviews to make sure they’re still happy with the service years after implementation.
This is most effective when you have a dedicated customer success team to coordinate and maintain efforts effectively. Some customer success teams will focus their efforts on customer retention and renewals, while others may lean towards up-selling and cross-selling. The one thing all of these teams share is a proactive relationship with their customers.
In essence, your customer success team’s main priority is to help customers achieve continuous, long-term value from your service.
Harnessing the tools and using them effectively to predict customer churn requires a lot of patience, effort, and resources, but boy does it pay off. Bain & Company stipulate that it costs between six and seven times more to acquire a new customer than it does to retain an existing one. Many of the elements we’ve mentioned, like data analysis, machine learning, and a competent customer success team can do a lot of the heavy lifting for you, allowing you to get to the route of your churn problem quicker and plan accordingly to reduce this.
Want to understand more about how you can productize your service offerings to reduce churn? Our new playbook, How to Productize Services Delivery, is the free, detailed guide on what you need to do. This playbook covers:
Frameworks to design, build and sell service packages
Sales enablement toolkits and pricing strategies
The Precursive productization toolkit
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