Clay 3x'd growth, cut SLA by 80%, and increased sales velocity with Snowflake and Hex

The challenge: A $100M trajectory with a one-person data team

Clay is in the business of hypergrowth. Their data enrichment platform helps sales and growth teams get in front of the right customers at the right time with automated research workflows.

On their journey to $100M ARR, Clay's own appetite for data surged: the company needed a better way of understanding user activations and sales needed visibility to see how individuals were pacing against quota.

Although the data team was greenlit to grow, hiring alone wouldn’t be enough. The data team still had to deliver a myriad of ad hoc and exploratory requests. Clay searched for a platform that let the data team work fast on complex problems like building a company-wide activation metric — but that also made it easy for non-technical stakeholders to get answers on their own and share what they found.

Solution: Keeping the pace of hypergrowth with Hex and Snowflake

Hearing that Hex was the go-to tool in the market for analytics, Joshua Hanson, Clay’s founding Data Scientist, jumped into a self-serve trial and was able to pull data from Snowflake within minutes. Unlike a local notebook environment, there was no wrestling with connectors and no painful local setup.

Once inside, Hex's intuitive UI let Josh and his team dive straight into analysis. With SQL and Python living side by side, they could connect queries, datasets, and models in a single workflow — no context-switching, no stitching tools together. Everything lived in one cloud-based environment, so their work wasn't just exportable, it was shareable and understandable to anyone across the org.

But what sealed it was the bigger picture: Hex was powerful enough to bring Clay's scattered data sources together in one place, giving Josh the foundation to help teams across the company build clearer, sharper views of their strategy. The data team was sold.

Cutting customer reply times by 80% with a support simulation built in Hex

As Clay's customer base grew, the support team started to drown in help tickets. Response times climbed, service level agreements were at risk, and the natural assumption was that the team needed more people. Josh built a series of simulation models in Hex to find out.

By bringing together ticket submission and resolutions times, as well as staffing schedules, Josh discovered that the problem wasn't headcount, it was timing. Support tickets peaked when the fewest agents were available and quieter hours were overstaffed. Clay didn't need more people. They needed to rebalance their current staff to accommodate peak ticket submission hours.

Josh used Hex to model different staffing scenarios — adjusting variables, rerunning simulations, and pressure-testing assumptions with each pass. The result was a new coverage plan that matched staffing to actual demand. After the rebalance, Clay’s P90 time to first reply dropped by 80%.

The model became the backbone of how Clay plans support capacity today — and saved the company from unnecessary hiring in the process.

3x user activation rates with Hex

Despite growing fast, the company had no definitive understanding of when a new user becomes an engaged one. The growth team was flying without a compass.

Clay's growth team is responsible for one of the most strategically complex jobs in the company: partnering with customers to map their growth priorities and identify the right use cases for Clay. To do that well, they needed to understand their product's activation patterns first. Without a shared definition of what "activated" actually meant, there was no reliable way to know where to focus.

One of Josh's key early projects in Hex was answering that question: what does activation actually look like? He needed to craft a metric the entire company could rally around, but building one is far from simple. It started with a few hypotheses about which early user behaviors may have predicted whether someone sticks around, and then requires rounds of modeling and experimentation to prove it. To do this, Josh needed to move fluidly between SQL, Python, and visualization.

Josh started by digging into dozens of user behaviors in the first 28 days, looking for early signals that someone would stick around six months later. He translated hypotheses into variables, fed them into regression models, and layered in controls until the signal was clear and the activation metric was defined.

With the metric in hand, Josh partnered with Clay's growth team to build a full activation funnel. The team ran experiments in Eppo, a feature experimentation platform, and brought the results back to Hex for deeper exploration — pulling apart what was working, what wasn't, and why. That cycle of experiment, analyze, iterate became the growth team's rhythm for the next year.

“Clay's activation metric was born in Hex. I built it there, the growth team rallied around it, and it's still our north star today.” — Joshua Hanson, Data Team Manager

Hex became the growth team's home base for understanding what was actually driving Clay's engine and the results followed. The growth team grew their activation rates by 300% and increased the trial-to-conversion rate by 200%.

91% faster insights drove an increase in sales velocity

Hex's impact at Clay isn't limited to the data team. Sales is using it to hit their numbers, too.

With aggressive quotas and a fast-growing team, missing the signals on an at-risk deal isn't just inconvenient, it's a missed quarter. Ashley Artrip, an Enterprise Sales Leader at Clay, needed to know exactly where to focus. However, the data to get there was scattered across Notion, Salesforce, Gong, and Clari. By the time someone stitched it all together, the window to act had often already closed.

That’s when Josh introduced her to Hex.

Within an hour, Ashley had built three dashboards with the Hex Agent by prompting the agent to analyze and visualize her team’s pipeline data. Each dashboard gave her a different lens on her team's performance. She can now see predicted pacing alongside actual pacing for every rep, based on historical win rates, deal stages, and how long deals have been sitting at each milestone. The value is in what the surface numbers don't show. Reps who appear to be ahead of quota might actually be trending behind once close rates and deal velocity are factored in. Now Ashley can see that early — and knows exactly where to show up and support her team before a deal slips. With dashboards in Hex, Ashley’s team finished above quota, contributing to Clay’s best quarter to date. Ashley’s speed to insights decreased by 91% from one hour to five minutes. Now she can refocus her time on coaching her team instead of building reports twice a week.

"Hex’s agent helped me build reporting to know exactly where to plug in for my team. A rep can look on track on the surface, but Hex shows me the full picture — win rates, deal velocity, stage timing — so I can step in at the right moment. That's how we're exceeding quota." — Ashley Artrip, Enterprise Sales Leader

Coaching her team is only half the job. The other half is answering to leadership — and Hex handled that too. Ashley used Hex Threads via the Slack integration to answer questions for sales leadership without waiting on the data team. When Clay's VP of Sales asked which reps had historically moved deals into stage 2 sales cycles and what average deal sizes by segment were, Ashley was confident enough to share the results because Hex showed her an audit trail of how the answers were derived and the underlying Snowflake table that it came from. The Head of RevOps confirmed that her numbers were correct.

"I'm not a data scientist and I don't need to be. I used Hex to build a predictive pacing model that tells me exactly where my team needs my support." — Ashley Artrip, Enterprise GTM Engineering Leader, Clay

The Impact: 3x growth, 80% faster support, and a sales team that sees around corners with Hex

What started as a platform for Clay's data team has become the backbone of how the company makes decisions — from growth experimentation to support operations to sales forecasting.

With Hex, Clay has delivered:

• A 300% increase in user activation rates after crafting the definition of an active user built in Hex
• A 200% increase in trial-to-paid conversion driven by rapid experimentation cycles analyzed in Hex
• An 80% drop in P90 time-to-first reply after building a capacity model built in Hex
• 91% increase in speed to insights for sales metrics
• Sales performed above quota with visibility to pacing and customer utilization, contributing to Clay’s best quarter to date
• 3 sales performance dashboards built in under an hour — replacing a patchwork of four different tools
• Ad hoc sales data questions answered in under 30 minutes by a sales leader, without a single request to the data team

"Hex is where we go to understand the deep nuances of what's happening — and then act on them before anyone else sees it coming," Joshua Hanson, Data Team Manager

As Clay heads into its next leg of growth, the data appetite across the org shows no signs of slowing. More employees are scheduling time to learn Hex after seeing how quickly it turns questions into answers — and new use cases keep surfacing.