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For Data Kraken CEO Gope Walker, churn prediction analytics can’t prevent companies from losing customers unless the insights are understood and embedded in every customer interaction from the get-go.
It’s an inescapable fact that customers sometimes leave in any business or industry. What’s more, as your business grows and scales up, so does the likelihood of your churn rate.
Think about it this way: the more customers you have signing up, the more customers you also have that may leave down the line.
This doesn’t just have an impact on business profits. Finding and onboarding new customers costs time and effort. Indeed, retaining existing customers is a much cheaper way of doing business. But that’s easier said than done.
A quick search online and one of the most used tactics for retaining customers is churn prediction. But having the right knowledge doesn’t necessarily mean you’re going to use it in the right way.
Here, we explore why churn prediction is all well and good, but it won’t automatically lead to churn prevention.
What is churn prediction?
Put simply, churn prediction is a term to describe techniques that allow business leaders and internal teams to understand customer behaviours and attribute, specifically linked to the timing and risk of them leaving your business. It’s the process of analysing and tracking certain behaviours and using this data to predict customers leaving before they do.
This includes assessing how long someone has been a customer, how active they are, how much they have been paying or spending each month and whether they typically use new features. Put simply, artificial intelligence (AI) churn prediction can predict how likely a customer is to cancel their subscription or commitment to your business in the next month or so.
The idea is that if business leaders and the appropriate internal teams can identify potential customers before they leave, they can use this information to encourage them to stay. But having the data isn’t enough. Instead, teams need to know how to use the data effectively and to understand the data because there is always a chance it could be wrong or misinterpreted.
Where does churn prediction go wrong?
Lots of businesses implement a churn prediction model and think the hard work is done. However, this is just the beginning.
Using data analysis, you know that a customer is highly likely to leave within 28 days. So, now you need to try to keep them. You could call them and offer a better contract, or you could give them some free months. Alternatively, you might offer them vouchers or even send them a gift in the post with a note saying you’ll try harder.
These are just a handful of examples but not all of them will work for your business. For churn prediction to really work and ultimately lead to churn prevention, it requires starting from the moment you sign up customers. This is because the most successful businesses on the planet take the time to know and understand their customers from the day they sign up. They nurture the relationship from day one and prioritise learning what their customers like and don’t like – and never take them for granted.
If you’re concerned that your customers may choose another tech business and you don’t know what to do about it, it’s probably too late to act.
Another issue with churn prediction is that by starting the retention campaign on those customers who are identified as having the potential to churn, you may also prompt some customers who wouldn’t have churned to cancel. For example, sending an email offering a discounted month to someone who was on the fence about leaving could result in them clicking cancel.
How can you use churn prediction?
For churn prediction to really work, it needs to start from the onboarding process. One of the easier ways you can do this is by asking simple questions during this stage.
Whether you onboard customers over the phone, in person or via a virtual system, ask them why they are joining your company. After all, if you have an understanding and awareness of why people are joining, you may be able to gather insights into why they may want to leave in the future as well as what and if there is anything you can do about it.
It doesn’t stop there though. You need to continually check in with your customers after they have joined, striving to learn as much as possible about them, their desires and ultimately their turn-offs. This will all help to shape their relationship with you. Do everything right and they’ll be less inclined to leave.
So, how can businesses prevent customers from leaving?
The answer is simple: aside from providing unrivalled services and products, tech businesses should know everything there is to know about their customers’ behaviours, desires and pain points. Then, they can put steps in place to fix any potential issues and prevent them from leaving.
This requires quality data which can be achieved through innovative analytics, CRM, segmentation and demographic profiling.
With these in place, business leaders then require teams to understand and analyse the statistics to level up their churn prediction and, indeed, turn it into churn prevention.
What’s more, while it might be too late to stop those customers who want to leave now from doing so, implementing churn prediction modelling into your business is a great way to protect new customers going forward.
In short, churn prediction can be an effective churn prevention tactic, but it requires the right approach from day one of a customer’s interest in your company as well as the right processes and data-driven insights to allow companies to manage their business as effectively and efficiently as possible.
By Gope Walker
Gope Walker is the founder and CEO of Data Kraken, a company that works with global clients to provide data-drive insights for effective and efficient business management.
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