Customer churn is a black cloud ominously hovering above every business. Whether you sell a product or service, whether you sell it to end-consumer or other businesses, whether you charge per purchase or use a subscription model, the threat is indiscriminate and universally devastating.
No wonder! All studies suggest that when customers churn, the bottom line suffers.
Marketing Metrics reports that the probability of an existing customer making a purchase is up to 14 times greater than the probability of a new customer making one. Other research suggests existing customers are fifty percent more likely to try your new offering and, as if that wasn’t enough, spend thirty-one percent more compared to new customers. Furthermore, according to the Harvard Business School report, boosting customer retention by only five percent can increase your profits by twenty-five to ninety-five percent.
Also, let’s not forget, Hubspot’s report shows that the cost of acquiring new customers increased by nearly fifty percent in the past five years.
Though, the saddest part is eighty-five percent of customers churn because of poor service that could have been prevented.
At Landbot, we are no strangers to the customer churn battlefield.
Through trial and error, our customer success and support team developed (and continues developing) weapons and tactics to gain an advantage, get better at how to streamline customer support, and reduce customer churn to a minimum.
And so, I decided to share a bit of our knowledge in the hope it will help you reduce churn and get it under control.
What is Customer Churn Rate?
Customer churn rate represents the percentage of clients who stop using a product or service over a given period. The causes of a possible high churn rate are plentiful, and only an analytical approach can help understand its dynamics and prevent it.
In my experience, an accurate scientific process is key to identifying its causes. This approach also offers you a practical set of possible solutions to limit its devastating impact on your business. These types of anti-churn activities often fall under the broader tactics for retaining customers.
In the sphere of predictive analysis, churn analysis has, little by little, acquired a strategic value. That’s thanks to its close link with the degree of customer satisfaction which directly links to loyalty or abandonment.
So, if we all agree that this is one of the most delicate aspects of the success of a business, we can also all agree that data analysis is our most valuable ally in this war.
What’s Churn Analysis?
Churn analysis is all about examining customer defection and their tendency to abandon a product or service. It helps predict and prevent the effects of the churn rate — the actual defection rate taking place in the analyzed period.
Before going into the specifics of churn analysis, I feel it’s crucial to underline that it’s an analytical discipline. It uses historical data to implement strategies based on objective analysis and forecasts of the dynamics relating to each customer’s behavior.
Typically, the data analyzed can be categorized as biographical or behavioral. Their correlation determines the client's interaction with your business. For example:
- When did they become a client?
- When was the last time they used your service?
- What did they do with it?
These are just some of the questions you should ask yourself every day to make your strategies efficient.
If implemented individually, the churn analysis can return a partial result. But, if used with the cohort analysis, it will give you an instrumental view of the entire Customer Lifetime Value (CLV).
Why is Churn Analysis so Important?
Churn analysis works, above all, to reduce the probability of customer abandonment to a minimum, keeping the churn rate as low as possible.
The main reason for its application is the desire to prevent such dynamics by setting up alarms and intervening before the situation worsens. Its objective is to understand which type of customer or segment is most affected by the dynamics of defection so you can identify corrective actions to take.
To give you an example, at Landbot, we used every churn and contraction above 150€ monthly recurring revenue (MRR) as a trigger to identify the cause behind it and reactivate when possible. (🧐 There's always more on how to generate revenue for your business from a customer support perspective). The idea was to reduce the time window between churn and reactivation (especially in case of failed payments) by getting in touch with the churn client as soon as possible. The strategy allowed us to control the churn rate throughout the whole year and with only two months above eight and a half percent:
Only an efficient examination of these phenomena can enable the development of appropriate customer retention strategies aimed, as the name suggests, to keep the acquired customer base as solid as possible.
To respond efficiently to these needs, churn analysis uses methods and techniques typical of predictive analysis.
Over the past few decades, the growing availability and amount of data increased companies' demand for new analytical methods. Predictive modeling and, specifically, churn modeling represent one of the most encouraging practical outcomes.
What’s the ultimate goal of churn prediction and analysis?
It’s to support the decision-maker in their choices.
Through a series of forecasts that can be updated in real-time!
The decision-maker can accurately assess the possible scenarios and therefore make the best decision.
For example, in line with predictive analysis, we set out to identify and predict high expansions to prevent contractions. In other words, we noticed customers who expanded quickly and crossed their chat/message limits incurring costs they did not expect churned. The goal of our proactive framework is to contain these big expansions by upselling a better, committed-through-time and higher-volume-based plan to the client BEFORE they cross their limit and churn. This tactic allowed us to prevent big contractions volume with only two peaks in 12 months (because of specific cases):
Another possible example could be evaluating the number of users who do not renew their subscription to a service. They don’t abandon it but settle for a less expensive or free basic solution. Through analyzing individual behavior, you might notice surprising aspects you would never have thought of beforehand. The findings can lead you to correct a redundant commercial offer in favor of more consistent solutions with the services expected by our customer base.
At the same time, I feel it’s vital to stress the role of a human analyst. The role becomes increasingly important as it ensures the forecasting models don’t stray too far from realistic scenarios in pursuit of credible assessments to support the final decisions.
What Are the Advantages of Churn Analysis?
Suppose the data history analysis allows us to have a clear picture of the situation and what led customers to abandon a given offer. In that case, the predictive analysis allows us to develop a further awareness of the problem.
This will, in fact, allow us to take managerial decisions to act in advance on the critical factors that cause the defection and actually reverse the effects.
Thanks to churn analysis, it's, therefore, possible to transform a severe threat into a series of opportunities, specifically:
- Product/service improvement: If users abandon your service in favor of a competitor, it means that it works better. Suppose you want to remain competitive in the market. In that case, you have to take note of it, analyze what your competitors are doing better and answer with a more effective and innovative offer. Similarly, if users abandon one of your solutions because they are tired of not receiving support or finding themselves always facing the same "bugs,” you have to promptly intervene if you do not want to lose them.
- Improvement of marketing/sales/customer care strategy: In many cases, it’s not uncommon to see very valid products that suffer from penalizing dynamics in terms of sales. This can happen for many reasons ranging from a too-high price, competitors' more aggressive strategies, gaps in after-sales support, and the lack of attention to the customer’s individual needs. In this regard, customer data is an invaluable asset to perform an efficient churn analysis and therefore identify any critical points in your business strategy. And finally, to implement corrective actions before it's too late.
Another relevant aspect in the predictive analysis is the interpretation of the churn, which doesn’t have to necessarily correspond to a negative effect.
To simplify, you can distinguish the “good” churn from the “bad” churn.
A targeted analysis could reveal how, following some product variations, there is a high abandonment rate by an unprofitable category of customers. In this case, even in the face of a negative trend, the direction taken by your company could be the right one.
Going deeper into the analysis, you could evaluate which part of your portfolio is not particularly profitable and decide to eliminate some products, investing more in those solutions that are strategically and remuneratively more effective for your business and better performing for the most valuable customers segment.
Regardless of the applications, the main advantage of a predictive examination is to progressively increase the level of awareness of the problem so as to implement concrete strategies that — besides the single area of activity — go in the direction of the general improvement of your business.
No Magic Formula, Just Hard Work
There are no magic formulas or approaches that guarantee a priori the success of an anti-churn strategy.
Instead, we can look for a series of good practices that are possible to implement using appropriate tools:
- Encouraging new customer onboarding: Provide training courses that can put new users at ease with the product/service. Make an effort to illustrate its potential and how it can provide value to your clients;
- Listening to customers: Investing adequately in marketing, sales, and customer care departments to support users with customized products and the level of services based on specific needs;
- Anticipating clients’ requests: Try to continuously monitor your customers level of satisfaction with your product/service. It will give you valuable insight into what you are doing well and on what you need to rethink;
- Investing in the quality of the product/service: A consideration that at first glance might seem even trivial, but it's the most crucial aspect to satisfy customers in a context in which competition could prove to be very aggressive, overall in the IT sector;
- Identifying and taking extra care of the most profitable customers: Encourage exclusive conditions and rewarding strategies for the most loyal and collaborative users.
What’s the biggest learning here?
Being able to exactly identify customers on the edge of churning is fundamental. However, it's potentially worthless unless it can inspire managerial actions.