Originally published on inSided.com on June 2, 2022.
Digital Customer Success is for everyone.
This is the message that has echoed across LinkedIn and in the wider CS community for some time.
We caught up with Brian LaFaille, former Global Head of Customer Success Strategic Programs at Looker, to get his take on segmentation and how they score customers at Looker.
Here’s what he had to say.
Starting Out with Digital Customer Success
When CS teams start out, they often align with the sales organization to have clear accountability across sales and CS. The natural next step is to segment your customers based on ARR. At Looker, this is the approach we took in the very beginning. And it made sense. The sentiment was that we needed to protect the customers spending the most and ensure we grow them. As a result, these customers all got a CSM. And it’s not a bad approach. It’s straightforward, instantly quantifiable, and you know the dollar amount as soon as the deal closes. Plus, you can plan for it and look at a forecasted pipeline. But in the name of durability, it’s not a sustainable approach. We noticed over time that a couple of things broke down for us when we started to use that approach at scale.
Where this approach falls short is that you can’t identify high-growth accounts. In the early days of Looker, we signed IBM. If we had only looked at the ARR, no one would have thought twice about growing that account. They had signed a tiny pilot worth $12,000. Based on that, they would never have gotten a CSM. In reality, IBM is a huge, publicly-traded company with hundreds of thousands of employees worldwide. It was instances like that when we realized, look, if we just look at ARR, IBM would never get a CSM. They would just go into a digitally-led program. But If this goes really well, there’s a massive opportunity for us to expand this account.
Building a Digital CS Model to Identify Customers
After this, we came up with a model that looked at both the ARR and the number of employees. In this case, the number of employees represented the growth opportunity. Next, we attached this simple framework to a point system. For every $10,000 of ARR, the company gets one point. For every 1,000 employees, the company gets one point. Using this scaling system of points, we were able to assign CSMs based on both ARR and the expansion opportunity. The number of employees may not be the right metric for every company, but it worked for us at Looker.
Once we had this point system in place, we grouped all of our customers, scored them, and bucketed them. Then we said, “the top 1-2% that are scoring hundreds of points, they’re natural candidates for CSMs.” But the ones in the middle and the lower tier are the ones that either have a low ARR or they have a decent ARR, but the number of employees at the company is fairly low.
By having a scoring mechanism where we could standardize and group customers into high-touch or digital-led buckets, Looker started to introduce more programmatic elements into segmentation. Plus, when discussing the new approach with Sales and Professional Services they were completely aligned. Their response was pretty much, “Um yeah, I want a CSM on that IBM deal.” And that’s something that was missing before.
Aligning Teams Around Digital CS Customers
Introducing the point system, and publishing that with their Sales, Professional Services, and Support counterparts, helped every team to be aligned and have a baseline to use to score customers. Customers that score above X are good candidates for a CSM. Candidates that score under a certain level potentially will go into a digital-led program. I also want to stress that I’m saying potential candidates. Why? Because this is just a starting point. If there’s a customer that scores below the threshold, but the sales rep or the Sales Manager comes to us with some added context, for example, this company is going to acquire this other company in the next six months. Then that’s a good opportunity. Let’s keep a CSM on them and we can evaluate later.
The point system helps guide the conversation in a more data-driven way. And that’s what we’re trying to do when it comes to scaling, is how can we assign quantitative measures to some of the scaling elements.
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