“Research shows that the cost of acquiring a new customer can be higher than the cost of retaining a customer by almost 1000%.”
That’s a pretty staggering statistic. Therefore, customer retention should be one of the most critical goals for businesses across industries. Retained customers are also much more engaged and open to cross-selling and up-selling.
In the past, customer churn was usually just treated as a metric of interest since businesses were able to do precious little to actually affect it. Churn is one of the more frustrating business metrics since by its very definition it is reactive rather than proactive – observed and measured after the loss of customers is done and dusted.
But things are different now. Companies today can capture data points across multiple attributes of customer engagement. Sophisticated AI and analytics techniques can then help leverage this rich data to address churn in a much more effective and proactive manner.
“Increasing customer retention rates by just 5% could increase profits by 25% to 95%.”
What is churn?
Let’s start with the basics first. Customer churn is simply defined as the number or percentage of customers lost within a specific period of time. In other words, the metric tracks how successful or not you have been at keeping your customers engaged.
What is ‘reactive’ vs. ‘prospective’ churn?
Customer churn or attrition can happen in two distinct ways and it’s important to understand the difference to effectively manage churn of both kinds.
Customers can sometimes react to specific negative events or experiences that trigger their move away from your business — this is usually known as ‘Reactive’ churn. For example, an ecommerce customer might get put off by delayed delivery or by receiving a damaged item, and thereafter not return to the same platform.
Alternately, sometimes the disengagement is gradual and not necessarily driven by any specific trigger. This is ‘Prospective’ or ‘Silent’ churn. This type of churn is much more difficult to identify and manage for obvious reasons.
How can data and an AI driven approach help you in managing churn?
The key to churn management lies in being able to identify the early warning signals from customers most likely to leave. If you know early enough that a specific customer is likely to leave your business, you can take proactive steps to prevent it from happening. And this is where data and AI can play a transformational role.
At UNCOMPLICATE.AI we look at three fundamental approaches for addressing churn. Based on the specific context at a client’s organization, availability of data, and other critical factors, we determine which approach, or combination of approaches, would give the best results.
In a nutshell, our three strategic approaches are the following:
1. Identifying the causal relationship between known negative triggers and their effects
Analyzing historical data around negative customer experiences and how different customer types responded to them helps develop a strong model for predicting reactive churn. This can then be used to track similar triggers being experienced by current customers to determine how they are likely to react. Once a customer is identified as a likely churner, he/she can be treated appropriately to ensure they are retained. Do note that triggers can be both internal and external – and it’s critical to provide for both in your churn model!
2. Using past churners to understand fading behaviors
As we mentioned earlier, churn is not always based on negative triggers. The good news is that historical data can also be used to predict non-trigger driven churn. The churn model can map current customers, based on a 360-degree assessment of them, with historical customers that have churned, to help identify high risk customers. To put it another way, if they display behaviors similar to customers who left then they are likely to go down a similar path. Examples of such behaviors could be reduction in frequency of buying, reduction in number of items bought per transaction, reduction in loyalty points redemption etc.
3. Isolating high-risk clusters
The third approach we use is to build a model to segment customers into different behavioral characteristics. This enables identification of the segments or clusters which have a high risk of attrition. Let’s look at an example from stock brokerage industry to help understand this – the segmentation decision-tree might reveal that customers that are less than 30 years old and made fewer than 3 trades in the past six months have a churn rate eleven times higher than the average. Or that the customers that joined less than a year ago and have invested decreasing sums over the last three months, have a churn rate seven times higher than the average. These groups can then be isolated as high-risk groups and customers belonging to them can be managed proactively.
Customer churn is a complex, yet extremely critical business aspect that no organization can afford to ignore. If you’d like to know more about how churn may be affecting your business or how you can address the issue of churn, reach out to us at hello@uncomplicate.ai for a complimentary assessment.
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