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.

Here, you’ll need accurate entitlement data (what they are licensed for) versus what they have activated (e.g., assigned licenses for). To make this actionable, you’ll want to feed this data into a “deployment” health score as part of the customer outcomes health score, which is often built into customer management software.

This will allow your teams to receive alerts or take action if deployment isn’t reaching the desired threshold, as this is the first warning sign of potential shelfware.

Engagement

Is the customer engaged? Are you multithreaded to the right stakeholders? In other words, are you talking to all the right peope at the right cadence — the people with influence over the outcomes of the partnership?

To answer these questions, you’ll need to tag/identify the key people at your customer and establish SLAs for engagement. By leveraging a CSP, you can create logic based on the activities that are happening to feed the engagement health score. In turn, this will signal how strong engagement is at that customer (i.e., are we engaging with the key people in the desired timeframe?) and trigger alerts/playbooks when things go off track.

Adoption

How broadly and deeply is the customer using your product? Here, breadth refers to how often, or how many, users regularly log in and display healthy usage. Depth is about adoption quality: the regular use of sticky features that indicate engagement in meaningful end-to-end workflows.

Telemetry data is becoming mission-critical in SaaS for understanding how customers are using products and where you can improve to drive better outcomes. We recommend looking at both depth of adoption and breadth of adoption.

Using a product adoption tool to glean this data, you can then feed your adoption health scores with associated playbooks based on the signals.

ROI

Is the customer achieving genuine value based on the outcomes you’ve identified and the work you’ve done?

Value realization is a common way to frame this based on your customers’ desired business outcomes. Create a mutual success plan with your customer to identify their desired business outcomes and the key initiatives you will work on to achieve the agreed-on success criteria. From there, this “success plan,” often thought of as a “value plan,” automatically drives an ROI health measure based on the completion of the “verified outcome.”

From workflows to leading indicators to lagging outcomes

Essentially, the DEAR customer outcomes score enables you to connect workflows to leading indicators and lagging outcomes. It lets you measure every activity your team is performing in terms of how it’s impacting your business today and how it’s likely to impact your business tomorrow.

I firmly believe that DEAR should be the north star of every customer success organization. With this framework, you can connect, with a high degree of statistical certainty, the lagging outcomes of retention and net retention back to the high-value activities that your team is performing (or not performing).

Here at Gainsight, DEAR allows me to identify the leading indicators tied to our CSM organization’s core workflows. For example, to drive engagement with various stakeholders, we conduct regular stakeholder alignment calls and executive business reviews. To facilitate adoption, our CSMs follow different playbooks and strategies. To boost ROI, we collaborate with our customers to create mutual success plans oriented around the outcomes they wish to achieve. Leveraging our outcomes framework, we then track when those outcomes have been achieved.

Help CSMs understand what to prioritize

DEAR helps CSMs understand which activities to prioritize and lets the company accurately gauge the impact of each activity on retention and expansion.

For example, at regular intervals, you might conduct a regression analysis on the individual components of your DEAR framework — product adoption scores to customers who have renewed in the past 12 months.

What you’ll likely see is that when CSMs are doing XYZ activities, they generate more “green” adoption scores, which correlate with higher renewal and expansion rates. By contrast, when CSMs are doing ABC activities, they generate more “red” adoption scores, which correlate with lower renewal and expansion rates.

Armed with this information, you can now say with a high degree of confidence that XYZ activities are generating XX% more green scores, which translates into a XX% higher renewal/expansion rate, which translates into an XX% increase in NRR. No more guesswork and no more assumptions. The DEAR data directly connects outcomes to your strategies and activities.

You might then decide to conduct a “red adoption campaign.” You might say, “Okay, we’ve identified the customers with red adoption scores. Let’s run an XYZ campaign to see if we can move some of them into the green before renewal. If we can turn X number of red customers green, DEAR indicates that their odds of renewal will increase by XX%, which, in turn, will increase our overall net retention by XX%.”

This type of conversation really excites and empowers your executives and CS teams. They now understand how leading-indicator-driven activities connect to lagging outcomes for both customers and your company. The metrics-driven DEAR framework will also help your executives and your board view your team through a strategic lens, winning you more credit for your good work and justifying the resources you need to continue doing that work.

An uncanny forecasting tool

Since implementing DEAR at Gainsight, the data we’ve generated has proven to be an uncanny forecasting tool and the most predictable way of forecasting revenue retention. In fact, we’ve used the overall DEAR customer outcomes score to build our gross revenue retention (GRR) plan each fiscal year, given the strong correlation and predictability of retention.

If you’re looking for a data-driven way to build confidence in your modeling with your executive team and board, this is it. Year after year, our actual renewal rates have aligned almost perfectly with the customer health scores generated by DEAR.

I’m willing to bet that by equipping your sales and post-sales teams with DEAR, you’ll significantly decrease the number of unpleasant surprises awaiting you in the court of customer retention, and significantly increase the number of pleasant, albeit unsurprising, outcomes.