You wake up one morning and decide to glance through those KPIs. Your website traffic? Looks great! Conversion rates? Even better! Maybe even that ever-important profit margin is looking better than ever! But then you take a peek at churn rates and retention rates and your heart sinks into your stomach a bit. What’s going on?
Retention rates and churn rates are amongst the most important KPIs you track as a business, and one of the hardest to predict. You can see in real-time just how many people visit your website or buy your product – but how can you predict how many of them will stay with you in the future?
Enter churn analytics, which is here to help you understand how your customers behave, and how they’ll behave in the future so you can optimize your marketing and sales processes to retain your customer base.
Using analytics for customer segmentation helps you learn what’s true about your users and allows you to keep giving them the thing they love most about your product. Without segmentation, you can’t know how to continue to develop your product, or specifically why a given product was successful or unsuccessful (especially given that there is always the chance that it was purely random chance rather than a narrative around a particularly good/bad design). When you segment customers, you personalize the user experience so that each user has the specific experience they’re looking for.
Data can help you track user engagement – often using a user segmentation platform. It can help you identify segments using a set of business priorities quicker than any other form of segmentation, highlighting how things happen in an app or on a site and suggesting metrics to follow up with. Analytics helps generate reports, and it also helps to formulate changes based on the segmentation it’s produced.
It’s not that difficult to see how many people are visiting a given website, or how they sign up, or when exactly (but maybe not why!) they purchase your product. How many will do so in the future is another matter.
Analytics has ideas for how your customers think and act, allowing you to optimize how you’ll market and sell a particular product to engage and retain your customers.
Serious data analysis is required here, particularly from data professionals. It can often happen that you have data that, say, after dealing with customer service your churn rate is going up. This might lead you to believe that your customer service is the issue and lead you to invest heavily in that.
It’s possible you were right even! But more often than not, customer dissatisfaction builds up over time, and a call to a customer service representative is often the last step in that process. Maybe your customer service COULD be improved, but actually it was just the final straw in a customer journey that wasn’t optimized from the get-go. Data analytics can pinpoint where churn begins rather than where it ends.
Related to point 3, after you’ve pinpointed at what step in a customer’s journey churn is likely to begin, you can then identify which kinds of customers are more likely to react poorly to which kind of interaction along their journey. Sometimes this means the point at which engagement starts to wane, from which point you can then use data to suggest a way forward (say, investing in engagement with at-risk customers at precisely the moment they are most likely to begin losing interest).
Similarly, big data helps you grow your customer base beyond just the original target audience. Customers are obviously the most important part of any business, and data allows you to see what they want and where they’re trending. The more data collected, the more trends and patterns that can be identified. Companies today need to connect with their customers – over the web, mobile apps, social media, email, phone or any other method – and listen to their needs and interests. That data can then be used to identify different sets of data for different audiences, and then to align content to each set.
The classic example here, of course, are e-commerce websites, which recommend products and services to users based on their past behaviour. The second a user goes to a product search page, they are shown an array of products which represent the website’s “collaborative filtering” based on the customer’s behaviour. This behaviour can be anything from previously purchased items or items the customer has interacted with in the past, to incredibly elaborate data-driven measures that determine what customers might buy at certain stages of their lives or careers. Famously, the shopping market Target even determined when women might be pregnant before anyone else in their family knew based on their shopping behaviour. The algorithms used in e-commerce can be very complex, using data related to time, location, demographic distribution, and a whole lot of other metrics including open rate, click rate, and opt-out rates.
Churn analytics is a relatively burgeoning field of analytics, but it helps you identify how churn works. Churn rates in general are not new, but figuring out exactly WHY churn is occurring is a complicated and often-times opaque process. Was it really one thing that did it? Was it death by a thousand cuts? Analytics breaks down every minute detail of who a customer is and how they behave, allowing you to notice nuanced changes that go so far beyond just “using product vs. not using product” that means you can tailor your product to the journey that customer wants to experience. That way you can optimize your processes and end that churn and burn that’s been the bane of your company’s existence for so long!
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