A little ways back Databricks’ VP of Sales Heather Akuiyibo joined SaaStr to share unexpected things that work well at Databricks’ GTM organization … as well as some things that haven’t worked as well.

 

4 Unexpected Learnings from Databricks’ Sales Growth Machine

1. Calendar scraping reveals top performers spend disproportionate time on new prospects – Databricks uses calendar data to track how their best AEs allocate time, discovering that overachievers focus on prospect development over existing accounts.
2. Word clouds drive culture ownership – Rather than dictating culture from the top, Databricks creates alignment through visual exercises that give AEs direct ownership in defining team identity.
3. Celebrations are shifting from contract signatures to consumption metrics – Their team is evolving beyond celebrating closed deals to automated alerts for customer usage milestones, fundamentally changing what success looks like.
4. Scorecards evolve with company maturity – What made a great hire four years ago isn’t the same today; Databricks reduced emphasis on big data selling experience as the company grew, showing how hiring criteria must adapt.

Deep Dive: Unexpected Learnings

Time Management as Competitive Advantage

Most sales organizations guess how reps should spend their time. Databricks took a data-driven approach by analyzing calendar data from their top performers.

“The best-performing account executives disproportionately spend their time on new prospects,” Cuibo reveals. This insight runs counter to conventional wisdom that maintaining existing relationships should be the priority.

For a company targeting 100% year-over-year growth, this focus makes perfect sense—but it required data to validate. Their analysis prescribes a specific formula: 30% of time with customers, 15% with partners, and the remainder split between prospecting and internal activities.

What’s revolutionary is how they operationalize this insight. Rather than vague guidance, each AE receives specific time allocation targets and regular calendar reviews to ensure alignment with these benchmarks. The company refreshes these targets every six months as the business evolves, creating a dynamic blueprint for time management.

Culture Building Through Visual Collaboration

Databricks’ approach to culture represents a significant departure from top-down value statements that rarely translate to daily behaviors.

“We asked our cross-functional partners to describe our team in three words,” Cuibo explains. “Then we asked our own team members what culture they wanted to create.”

This two-pronged approach revealed fascinating insights. External partners used words like “dedicated,” “teamwork,” and “scrappy,” while team members articulated aspirational qualities. By visualizing both perspectives in word clouds, the team could immediately see alignment gaps and opportunities.

The genius lies in what came next: account executives were given ownership to define and shape the culture they wanted to embody. This created personal investment and accountability that no executive mandate could achieve. The word cloud became both a mirror reflecting current perceptions and a compass guiding future behaviors.

“Culture is tough to pin down,” admits Cuibo, “but this exercise made it tangible and actionable.”

From Closing to Consuming: Redefining Success

Perhaps the most significant evolution in Databricks’ approach is their shift from celebrating signed contracts to celebrating platform usage and consumption.

Traditional sales organizations stop at the signature, considering their job complete when ink hits paper. Databricks recognized this created a dangerous disconnect between sales success and customer success.

“We’re introducing automated alerts that notify the team when customers reach important usage milestones,” Cuibo explains. These alerts go to the same Slack channel where deal closings are celebrated, creating a natural extension of the success narrative.

This subtle but powerful shift accomplishes several objectives simultaneously:

1. It reinforces the company’s customer obsession value
2. It aligns the sales team with customer success outcomes
3. It creates natural accountability for selling viable use cases
4. It provides early indicators of renewal likelihood

“These alerts have sparked more interest in what’s happening inside our customer base,” notes Cuibo. The sales team now thinks beyond the close, fundamentally changing how they approach customer conversations and solution design.

Adaptive Hiring Criteria for Different Growth Stages

Databricks’ scorecard approach to hiring revealed another counterintuitive insight: hiring criteria must evolve as the company matures.

“We’ve reduced the emphasis on big data selling experience as the company has grown,” Cuibo shares. In the early days, domain expertise was critical as the company established market credibility. Today, with an established brand and product, other characteristics have become more predictive of success.

Their data-driven approach allows them to correlate candidate characteristics with eventual performance, creating a feedback loop that continuously refines their hiring model. The weightings assigned to various traits—from industry expertise to learning agility—shift over time.

This adaptability prevents the common trap of hiring for yesterday’s challenges rather than tomorrow’s opportunities. It also explains how Databricks has maintained performance while scaling at 100% annually—they’re constantly redefining what “great” looks like for their evolving organization

What Hasn’t Worked: Three Growth Challenges

Even with Databricks’ impressive track record, not every initiative has been a home run. Here are three approaches that required significant adjustment:

1. One-Size-Fits-All Enablement – Early attempts to standardize training across all AEs regardless of experience level led to disengagement from top performers. The team has since moved to tiered enablement tracks, allowing veteran AEs to focus on advanced techniques while newer team members master fundamentals.
2. Over-Reliance on Activity Metrics – Initially, the team tracked dozens of activity metrics that created data overload without insight. “We had to simplify our dashboards dramatically,” Cuibo admits. “Too many metrics meant no clear priorities.” They’ve since focused on a vital few leading indicators with proven correlation to outcomes.
3. Territory Transitions – The hypergrowth required constant territory realignments, which created friction with both customers and AEs. “We underestimated the relationship disruption these changes caused,” notes Cuibo. They’ve since implemented more gradual transition plans with longer overlaps and clearer communication frameworks to mitigate disruption.