Product Usage: An Undervalued Buying Signal

Lessons in driving sales growth through customer usage data
by
John Tan

SaaS has enabled product usage data to become a key buying signal. Previously in the on-prem world, this wasn’t possible. Nowadays, everybody is adopting product-led growth with their SaaS offerings.

Marketing and growth teams are always experimenting with new signals. But what about AEs? Is it worth spinning up initiatives to arm front-line reps with product usage data?

Looking At Actual Behavior

There is a large and growing group of power users who pay attention to usage data. At Workbase, we’ve talked to hundreds of teams in addition to implementing systems in past lives. It is very hard to draw generalizations, but we’re energized by the number of reps who increasingly pay attention to usage telemetry. Without quoting anybody specifically, but commonly recurring words to describe usage data include “invaluable”, “critical” and “undoubtedly”.

The use cases are as you’d expect:

  • Uncovering new opportunities: out of 1,000 freemium workspaces, which are 50 new ones I should go upsell?
  • Driving a POC conversion: over the past month, who has logged into the application and what benefits have they achieved?
  • Identifying expansion signals: which companies are using feature X, and therefore they should be candidates for buying feature Y

Obviously, this only applies to businesses where ACVs are high enough (say $10K+) and there is enough usage data being collected. Reps who hunt net-new enterprise logos won’t have usage data to look at. We’ve also noticed that the sweet spot for setting up a system like this is when you have 5–10 reps and a sales ops team.

Looking at actual behavior, AEs tend to seek out product usage data by opening some sort of dashboard: across a limited sample size of SaaS reps (n=~40), the average was about 1x/week. The 75% percentile was 2–3x/week, and the 25% percentile was 0.25x/week.

The frequency of usage varied based on how important usage is to revenue growth; the quality of tooling in place; and a culture that values data-driven decision-making.

The Big Pushback

Enthusiasm for leveraging usage data is clearly accelerating, but there are still problems to address. The big pushback on this proposition is that there are already so many sales processes, tools, and methodologies. Any new signal like product usage would be competing for space on the SFDC account page and for mindshare in the rep’s daily routine.

Like any data initiative, there are some common failure points we’ve heard with this data:

(1) Not actionable or insightful: the data doesn’t have context or granularity. For example, a SFDC field that says L30D usage is 100 is not actionable. Who’s using, why, and how?

(2) Too hard to use: for example, many BI tools require reps to pull together multiple reports for a single use case. This is a lot of work that reps won’t end up wanting to do.

(3) Limited underlying value: certain businesses don’t grow with usage, even if they are SaaS. In that case, the value of product telemetry would be limited.

Practical Drivers Of Success: A Top 4 List

Assuming the underlying value exists, good execution is critical to broad internal adoption. It’s very easy to make the interface either too simple or too complex. If it’s too simple, then the data isn’t useful. High-level metrics without context makes it hard to action on the data. If it’s too complex, then the average rep won’t want to grok it. Reps aren’t hired to be data analysts. There’s a fine line to balance to ensure good adoption.

We’ve compiled a list of best-practices we’ve learned from our own experiences and talking to RevOps, BizOps, and Analytics teams to minimize low adoption risk.

(1) Right level of granularity: telling a value prop story requires detail. There needs to be a ‘who’, ‘what’, ‘when’, ‘where’ and ‘why’. Giving reps enough granularity to tell this story is key; it makes them sound prepared. Oftentimes, this involved diving one level deeper than a single aggregated account field in Salesforce.

(2) Prioritized lists: having a list of key leads, custom objects, opps, etc. makes data actionable. That being said, what shows up on the list (the filter), how the list is ordered (the sort), and what additional context to provide (the columns) are 80% of the value-add. Ideally, this work is automated and ‘pushed’ to the rep to minimize mental burden.

(3) Pre-made talking points: data is most valuable when put in context. This could be historical trending, comparisons against peer groups, or custom metrics that shed more light on value being created or where the customer needs to go. The phrase ‘the data is not actionable’ comes up frequently, and part of the problem is a lack of context on how to turn raw metrics into an action story.

(4) Visual cues: a picture is worth a thousand words — or at least a well-structured layout is. Having 50 fields on a Salesforce account page is impossible to digest. Reps often complain they get lost in a sea of fields. If we think about delivering a ‘product’ to reps, then the UX is critical. Using design principles like size, space, color, and lines to articulate the message would make data a lot more engaging.

Based on our experience, there is high correlation between how much attention is paid to the rep experience and how much adoption and recognition such initiatives get. RevOps, BizOps, and Analytics teams get creative on how to deliver this, whether through BI or custom eng work in Salesforce or a standalone internal app.

Clearly, there is room for tooling in the space to evolve and deliver easier-to-use solutions. But we’re excited about the increasing resources dedicated to these revenue initiatives.