Customer retention, often measured by “churn” rate (the percentage of existing customers who leave in a specified period of time), is the most important success factor/KPI for any business. When customers stay, your business can build long-term profitability through repeat purchases, as well as cross-selling and up-selling opportunities. When you retain customers and optimize their lifetime value, you also create brand ambassadors who give you priceless word-of-mouth marketing and referrals. “Churn,” on the other hand, is a revenue killer.
As a recent Forbes article explains, “it can cost five times more to attract a new customer, than it does to retain an existing one. Increasing customer retention rates by 5% increases profits by 25% to 95%.” Not to belabor the point, but your business simply must retain existing customers in order to grow. This post will explain how customer intelligence via analytics can help you reduce churn.
Customer analytics for the customer journey
Customer analytics can drive retention and engagement throughout the entire customer journey, from generating top-of-funnel leads to the mid-funnel nurturing of leads to bottom-of-the-funnel conversion and closing, turning leads into revenue. Knowing more about your customers, which customer analytics enables, is the best way to engage your customers with relevant messaging that keeps them moving through the funnel to conversion (and greater lifetime value).
For example, you can improve lead generation with bots backed by machine learning who perform quick engagements with your customers, answering basic questions and routing them to relevant products, services, and salespeople. Contrast this real-time, bot engagement with a website form that a prospect might fill out, only to wait weeks for a busy salesperson to answer. Which of those two scenarios drives more engagement, faster?
Customer analytics can help “solve” customer churn
The best way to leverage customer analytics is to map out your entire customer journey and identify places where analytics can help (places where your funnel is “leaky”). In its simplest form, the process would work as follows:
- Identify a particular problem that customer analytics can address.
- Ensure that your data is ready to be used, that it’s actionable and can be deployed on the particular problem.
- Deploy machine learning to address the problem and learn from the data you’re collecting, so your approach becomes “smarter” over time in addressing the problem.
Let’s identify customer “churn” as a big problem-to-be-solved (see step 1), which it certainly is. In step 2, you would need to collect and then leverage relevant data to better understand where and why customers are leaking out of your funnel. Then you’d build a model based upon this data, deploying machine learning, in order to help you predict the moments and the reasons your customers leave. So instead of passively watching customers leak from your funnel, you would know why they leave and be able to proactively engage them to prevent churn.
Machine learning basically uses math, statistics and probability to find connections among variables in your data, helping you optimize important outcomes such as retention. These machine learning models get even smarter at making predictions by constantly integrating new data. The result? You get data-driven insights that lead to marketing actions that retain your customers.
In another example of driving retention and ROI, you might apply customer analytics to better understand your customer’s past purchasing or browsing history, and then build a predictive model that could anticipate the next product a customer (or customer segment) might be interested in buying. This predictive model, using machine learning, would then help you identify the right customers (and the right times) for up-selling or cross-selling opportunities.
To do all this and more, you need the capacity to build models based upon quality data and deploy machine learning so these models get smarter over time, driving relevance and stronger customer engagement. You need a data and analytics platform that allows you to make your data actionable (the old saying remains true, “garbage in, garbage out”). By leveraging an automated customer data platform with machine learning analytical capabilities, you can leverage your data to reduce churn and boost ROI.