Combat Customer Churn with Predictive AnalyticsReading time: 5 minutes
No matter what industry you’re in, losing customers is bad for business.
The good news? Retaining customers is much more affordable than acquiring new ones. And increasing customer retention by just 5% has been shown to increase profits 25-95%! That’s because it costs five times more to earn a new customer than to keep a customer in-house.
So how can you reduce churn and retain more customers? The key is to identify why your customers leave, then take action to keep their business. And if you can forecast which customers are likely to leave (and why), retaining them will be a whole lot easier.
That’s where customer churn prediction comes in! Here’s how it works:
1. Set Benchmarks with Descriptive Churn Analysis
The first step toward reducing your customer churn rate is understanding the situation as it currently stands. This means gathering as much data on customers as possible.
Data can tell you where customer churn is hurting your business. Data tells you when churn occurs, and which types of customers typically leave. Depending on your business model, data also helps you see where you should improve to meet more customers’ needs.
This level of analysis is known as a “descriptive” analysis, which measures past data and shows your current churn rate. Descriptive analysis helps you set benchmarks, create concrete goals, and plan measurable strategies.
This analysis can prompt immediate changes in how you work with customers, which is helpful all on its own. But it also sets the baseline for the next step in the process.
2. Use Models to Make Customer Churn Predictions
Once you understand your current churn rate and where it’s impacting your business most, you can move into the next phase: forecasting how many of your customers are likely to leave, and just as significantly, why.
Patterns and trends that lead customers to leave your business are often invisible to the naked eye but seem bright as day when viewed through the lens of data.
Using the data you gathered for your descriptive analysis, you can build a predictive model to flag customers who take actions or fit profiles that often lead to churn. Predictive analysis builds on descriptive analysis, using past data to calculate what is likely to occur in the future. Rapid Insight’s modeling tool, Predict, makes building a predictive model easy for anyone. Predict automines your data, identifies the statistically significant variables, and creates the strongest possible model from your dataset.
The insights you glean from predictive analysis may confirm your existing suspicions about why you’re losing customers, and that has value of its own—operating on hunches works about as often as it fails. Objectively confirming your gut feelings offers assurance that you’re on the right track and justifies expenditures of time or money.
At the same time, your analysis will likely uncover unexpected correlations between customer behavior and churn. You may be overlooking areas where a few changes in procedures could go a long way toward keeping customers loyal.
Predictive analytics also allows you to identify which specific business sectors are likely to experience high attrition rates, offering you the chance to focus efforts in the right place.
3. Intervene to Prevent Customer Churn
Now that you know where your efforts will make the biggest impact, it’s time to get proactive about retaining your customers!
The right actions to take depend on your industry and business, but here are a few ideas that any company can implement:
Create a customer communications calendar
If you aren’t tracking the last time a customer visited a store or made a purchase on your website, start now! If the customer goes dormant and doesn’t interact with your site for a period of time, send them a ‘wake-up’ email or offer them a discount to incentivize another purchase. Predictive analytics can help you identify the ideal time to wait before sending this outreach.
Send a periodic email newsletter to keep your business top-of-mind
Share updates about your industry and business that your customers will find interesting. If you have an extensive list of customers that you can break down into subsets, consider segmenting your list and personalizing outreach.
Create a loyalty program
Customers love rewards. Find genuinely surprising ways to reward customers that only you can offer. If your predictive analysis uncovers that rewards incentivize subsets of your customer base to re-engage with your organization, send surprise rewards and draw those customers back in.
Offer excellent customer support
At Rapid Insight, we’ve found this to be a key differentiator. We offer all of our users free, unlimited support from our team of data analysts. Time and time again, our customers tell us how much they appreciate and value that support. Customers often outright say it’s a significant part of the reason they continue to work with us!
Survey current (and former) customers for feedback
Gathering survey data is often the best way to check your customers’ pulse. Offer a reward for participation, such as a gift card to a coffee shop or Amazon. Tracking information on current and past customers also provides data you can use in your customer churn models to make predictions about future customer behavior.
The key to all of the suggestions above is demonstrating that your customer has value to you by offering value in return.
There’s no guarantee that any given strategy will work for your customers. That’s why it’s critical to continue monitoring data as you implement intervention strategies. After all, that’s the only way to know if your efforts lead to success.
How Rapid Insight Can Help
With our intuitive, powerful data-prep tool, Construct, you can compile and prepare your business’s data to create a clean dataset for on-demand analyses. These analyses can help you analyze past trends and identify why customers left in the past.
But you can deepen those insights with our predictive analytics software, Predict, which helps you anticipate the likelihood of customer churn in the future. Once you understand what makes customers likely to leave, you can plan effective strategies to keep those customers loyal to your business or organization. Better yet, you can measure the success of each intervention, helping you refine your retention strategies even further.
It’s time to take your business expertise and customer knowledge and apply data analytics software to minimize customer churn. The first step is understanding your customers’ value. Our predictive analytics software will help you take it from there.
Click the button below for a personalized demo of our tools. Start making your own customer churn predictions today!