The Net Promoter Score (NPS) is largely popular due to its successful implementation by companies such as American Express, Apple, and Southwest Airlines.
While these scores provide insights on customer satisfaction, they are limited in their ability to prevent churn. A real example provides a case in point, but first a little more information about the NPS.
The NPS is a customer loyalty metric developed by Fred Reichheld, Bain & Company, and Satmetrix. The 2003 Harvard Business Review article, “One Number You Need to Grow,” introduced the concept. NPS measures the relative amount of customers who are promoters of a particular brand or company (i.e., fans) versus those who are detractors by measuring the difference between the two. NPS can be as low as −100 percent (everybody is a detractor) or as high as +100 percent (everybody is a promoter). By knowing this score and tracking changes, each department from marketing to products to sales to support can understand how well their products and services create customer satisfaction.
The Good: The NPS is a valuable metric for gauging sentiment of the customer base — we use it at Scout Analytics. Because the score is derived from a survey, NPS provides sentiment via a sampling of an organization’s customer base, typically with a 10 percent to 15 percent response rate.
The Bad: While sampling provides a good representation of the whole base, it does not provide specific insight into every individual customer. In fact, 85 percent or more customers are not represented by NPS.
The Ugly: For customer success organizations wanting to leverage NPS to prevent churn, the lack of coverage (i.e., 85 percent of customers without an NPS) is a real issue. Investments in time, money, and other resources to implement an NPS will quickly bump into the laws of diminishing returns in terms of predicting and preventing churn.
An Actual Case in Point
This case illustrates not only the coverage issue, but also a second concern regarding NPS accuracy in predicting churn. A midsize SaaS firm in human resources management was using the NPS as a predictive factor to determine at-risk customers. When an audit was completed to determine predictive accuracy, the company was surprised to find that more than one-third of its so-called “promoters” churned. Likewise, only 25 percent of their detractors churned, including those that received no intervention.
Peeling back the details on its NPS revealed the mismatch between NPS and renewals. The NPS survey had been sent to all customer contacts (i.e., users and buyers of the SaaS service). While the users generally had more promoters, the buyers had more detractors. In other words, the users liked using the service while the buyers were not happy with the return on investment. In fact, the composite NPS (i.e., NPS from all users and buyers) was +9.4 percent, but among buyers only it was -20 percent — a low score. And because the buyers, not the users, are responsible for the renewal decision, the use of composite NPS provided inaccurate predictions. The composite NPS predicted churn correctly only 23 percent of the time. Given its response rate was 12.3 percent, its customer success teams had low customer coverage and predictive accuracy.
How Do You Avoid the Pitfall?
Understanding customer coverage and predictive accuracy before using a metric for churn prediction is important. If the metric used has low customer coverage and predictive accuracy, then customer success teams will constantly be in fire drills. If the metric has high coverage and low accuracy, then the customer success team will have many false alarms. Likewise, high accuracy and low coverage will be surprised by unanticipated churn. Only with high accuracy and high coverage can you get to efficient and effective churn prevention.
The following infographic summarizes the challenges of NPS for predicting and preventing churn for this specific SaaS company.
NPS remains a valuable and useful metric for companies to know and grow; however, it rarely provides sufficient actionable data for customer success teams either because of coverage or accuracy. Rather, customer success teams should view NPS as a supporting metric. Our company uses NPS as a critical component of understanding how we can improve. That said, we focus on usage and ROI metrics for managing customer success.
Usage data is the new fulcrum for managing profitability of customer relationships. It might seem like an obvious statement, but the only way to gauge customer success is based on whether customers actually receive value from use of your product. And without visibility into critical milestones of the customer lifecycle, such as on-boarding and adoption, unanticipated churn and customer retention fire drills become problems too great to ignore.
What Is Usage Data?
Usage data can be defined simply as “data about usage.” It is the record of customer activity occurring at any given time. For example, usage data can include a page view, a file download, a transaction performed, or an article read. Before so many products and services turned to digital, usage data was not a factor in determining how much value a customer was getting out of a subscription-based service. But with the shift to digital, massive volumes of usage data are now being generated daily, and service providers now have the opportunity to measure and monitor content consumption,use of features, and frequency of use. The companies that connect usage data to customer success management, product management, marketing, and sales are the ones achieving profitability.
Usage data can answer such questions as: Have active users gone dormant? Is a customer’s usage above or below the average? Is a customer using all their subscriptions? Has the customer usage levels dropped off?
Usage data leads to metrics with high coverage and high accuracy for predicting and preventing churn, and is the key to having an efficient and effective customer success team.
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