Use customer health data to grow and forecast NRR

An old maxim among courtroom litigators states that you should only ask a question of a witness when you already know how they will answer. Otherwise, you might be in for an unpleasant surprise. For this reason, effective prosecutors and defense attorneys engage in various pre-trial activities, including “witness prep,” to help them take control of the narrative.

As many SaaS companies look to increase net revenue retention (NRR) to compensate for weak or declining sales, they may want to adopt and adapt this maxim to say: “Before we ask existing customers to renew or expand their subscriptions, we will pursue customer success (CS) strategies and activities (‘customer prep’) that help us avoid unpleasant surprises and increase the number of successful outcomes.”

Now comes the tricky part. What kinds of customer health data should you collect and analyze to help you avoid unpleasant surprises? And which strategies and activities should your sales and post-sales teams pursue in response to this data?

A DEAR solution

Historically, many CS leaders have relied on anecdotal evidence and presumed “best practices” in the hope of boosting NRR. Even when this approach seemed to work, customer success managers (CSMs) often lacked the empirical evidence to firmly connect the success with their team’s good work.

To overcome such strategic “squishiness,” we spearheaded the development of a more scientific, data-driven customer health scoring and retention modeling methodology. Known as DEAR (deployment, engagement, adoption, ROI), this framework aims to help CS teams deliver exceptional customer experiences and drive existing customers to their desired outcomes. In addition to a customer experience score, DEAR also provides a customer outcomes score, an objective indicator of whether the customer is seeing value and ROI on their investment.

Below is a breakdown of DEAR’s four components.

Note that in order to efficiently leverage this information, you’ll need the right technology (ideally, customer management software) and behavioral data (ideally, telemetry about how your customers are using the product).

Deployment

Is the customer activated? Are they set up to effectively use what they bought? Poor deployment is often a strong indicator of the risk of partial churn or downsell.