The 3 most important data metrics for retaining customers
Nearly everything in modern business is measurable, but often companies are relying on legacy data that can obscure the truth about what is happening in the business.
Some time ago, broad quantitative metrics were enough to shape the strategy of the business and were often focused on acquiring new customers. But with time, global markets have become increasingly competitive and acquiring new clients has become harder and more expensive. Thus, businesses have transformed their operational models to building continuous relationships with customers.
Priorities have shifted from getting new clients to keeping existing customers satisfied and growing “share of wallet” - a way of measuring how much a customer will spend in a business category and the percentage of that spend that a business is capturing. Capturing only data for broad indexes (for example, conversion rate and customer acquisition cost) doesn’t provide enough insight on your company’s long-term health and can create growth strategies that are underfunded and slow a company’s ability to unlock its potential and maximize market share.
Some 28% of businesses actually disappear due to inappropriately distributing their finances? In an era where every customer is valuable, it’s important to more deeply understand the most profitable and high-growth customer segments (and just as important, the underperforming segments) and how to ensure they are satisfied enough to be more profitable, repeat customers.
To achieve this kind of focus, there are three key data metrics that will help level-up sales, marketing, and service teams’ performance and processes.
Customer lifetime value
Customer lifetime value (CLV) is one of the most critical key metrics because it measures not only the net-present value of a customer (spend minus acquisition cost) but it predicts what the customer will be worth over a period of time. While it’s impossible to foresee exactly how long a relationship will last, businesses can use a variety of methodologies for calculating CLV - including the average lifetime of similar customers or use a predictive algorithm to consider a variety of factors to predictive lifetime value. One of the most critical qualities of the metric is that it measures that different customers bring different value to a company over time.
Compared to conversion rate or average order value/average contract value, which tend to focus more on the value of single transactions, CLV emphasizes that long-term relationships are worth more and thus, a company can invest more in acquiring and/or retaining them.
Customer satisfaction rates have often been measured, but with minimal effect on the business outside the confines of the contact center. This is typical because customer satisfaction rates are often limited to point-in-time measurement, and don’t look at changes in satisfaction over time. Additionally, customer satisfaction is often used to measure customer service performance and not as an indication of how well the business manages customers in various segments.
For example, it’s possible that the change to customer satisfaction rate might be minimal (from 4.5 to 4.3) but the change might be due to a full point decrease in a critical customer segment. It’s also potentially true that looking at broad averages hide the fact that customer satisfaction among the businesses most valued long-term customers has been slowly declining. Customer satisfaction is more of a predictive metric of success than say, retention rate, but without measuring over time or using advanced segmentation customer satisfaction fails to be actionable beyond the contact center and even then it’s of marginal value.
The downside of using retention rate as a key or supplemental measure of customer satisfaction is that it’s highly reactive and leaves the business limited time to save a customer or turn them back to a highly satisfied customer. Similarly, measuring customer satisfaction rate only near the time of transaction has the same limiting effect. Regularly measuring customer satisfaction enables a business to see trends over time, especially within a segment and deploy a variety of tactics to improve performance within that customer segment.
An additional benefit to optimizing the way a business measures customer satisfaction is the positive influence it has on Customer Lifetime Value. In subscription businesses like SaaS, this has created an opportunity for more proactive “customer success” teams that are focused on proactively engaging customers over time to address potential customer satisfaction issues before they happen.
Product adoption rate is a less well-known metric, but one of growing importance for companies that sell a product/service that requires service renewal or that is priced based on value consumption. For example, if your cable company sees that you are not actually using your cable service it can predict that you are more likely to cancel service or downgrade. Same can be said for all the new subscription businesses like curated shopping for clothing and food. The less you consume, the less value you derive and thus, the less likely you are to continue to pay for the service.
Adoption rate creates a metric to understand the possible value that can be consumed and gives it a numerical value, then compares your usage against this number. Then, a business can segment their customer base into power users, average users, and at-risk customers and can create targeted strategies for influencing change.
In contrast to measuring the renewal rate, which focuses on the business’ performance at renewing customers, measuring adoption rate focuses on predictive data that can enable the business to take action early to change the long-term results.
As business models evolve, the metrics that help businesses thrive evolve and change as well. The wrong metrics can mislead decision makers and cause poor decision making and bring unintended harm to the business. By focusing on metrics that emphasize long-term performance, and measure change over time or predictive results, companies have the ability to act early and alter the results of underperforming teams/products/services while increasing investment in high-performance tactics.
Businesses that act on these metrics and use a more advanced segmentation strategy can find opportunities that might not have otherwise been visible and replicate that success. With so much data from every source of the business, metrics like these can really change your business.