How Pump scaled from $1M to $25M+ in 18 months using Clay to orchestrate GTM

Systematized ICP definition to ramp 30+ sales rep in less than 2 weeks

When you hire quota-carrying sales reps into a massive TAM, their productivity clock starts immediately. But if they don't know who to target, they'll spend their first 60-90 days learning through expensive trial and error. For Pump, scaling from 0 to 30+ sales reps, this was mathematically unsustainable. If each rep wasted 8 weeks on targeting experimentation, that's 240 unproductive hours per hire lost to learning what the company should already know.

The GTM team used Clay to codify their ICP criteria: tech companies with 5-200 engineers using AWS, GCP, or Azure. They built comprehensive target lists enriched for decision-maker contacts, then assigned them across three sales pods. New reps received pre-built territory lists showing exactly who to pursue, complete with stakeholder contacts and context. Instead of spending weeks figuring out "who are Pump's customers," onboarding became "here are your 500 priority accounts. Start selling."

New reps went from taking 2-3 months to reach productivity to hitting their stride in just 2 weeks. The company scaled from zero to 30+ sales team members without sacrificing individual productivity.

"We've completely eliminated the problem of reps wasting time on account research. With Clay, each rep gets an up-to-date list with their target accounts, key contacts, and account context."
Stuart Lundberg, Head of Operations

Now, sales leadership have perfect visibility into rep coverage and ICP definition. The GTM engineering team avoids constant ad-hoc list requests from reps building their own territories. And critically, this foundation set up their next evolution: enriching those accounts with technographic data to personalize every outbound touch.

Account enrichment gives outbound +30% conversion lift

With target lists defined, Stuart now focused his attention on improving outbound conversion rates. He had the data to show that generic outbound wasn't delivering the rates their growth targets required. When you're selling cloud cost optimization, your prospect's cloud provider is the entire context for whether your message makes sense. A DevOps engineer using AWS thinks in EC2 instances and RDS databases. Send them an email mentioning Google Cloud Run and you've immediately signaled you don't understand their environment.

Pump used Clay to enrich every prospect for cloud providers, then built dynamic messaging templates that referenced provider-specific services. AWS customers received outreach mentioning EC2, RDS, and S3. Google Cloud customers saw references to Cloud Run and GCP terminology. 

The team then stacked additional context layers: hiring signals like new engineering managers or 2x team growth triggered timely outreach, location data routed prospects to regional reps, and AI-generated copy combined all these signals into natural-sounding messages.

Response rates increased 25% after implementing cloud provider personalization alone. Overall outbound lead to SQL conversion improved 20-30%. Signal-based campaigns targeting companies with cloud engineering hires achieved 4%+ reply rates versus sub-2% industry baselines. Location-specific case studies paired with technical personalization peaked at 5.82% reply rates.

"The technographic data was the mission-critical unlock to make outbound at scale even pragmatic," Stuart said. "We cultivated very tailored copy based on cloud providers and when engineering and finance teams were growing, which led to a 25% better response rate. The biggest gains came from being able to rapidly test and iterate. Clay allowed us to quickly create really precise and large lists. We moved from generic outbound to highly relevant outreach, which directly translated into more qualified conversations and more SQLs."

Automated workflow recovers 26% of meeting no-shows

With personalized outbound converting at higher rates, Stuart was able to go down the sales funnel to improve meeting no-show follow up. One of their top AEs had developed a manual recovery process, taking lead context and company details into ChatGPT to craft personalized follow-up emails. It worked well, generating high reschedule rates, but it was time-consuming, fragmented, and only one person was doing it.

Stuart saw an opportunity to replicate what their best reps do manually and automate it for everyone.

The GTM team reverse-engineered the workflow and automated it in Clay. After a no-show occurs, Clay enriches the lead's company, pulls recent hiring activity and growth signals, then generates a personalized follow-up email explaining why now is the right time to reconsider Pump. BDRs simultaneously receive a prioritized call list with full context on each no-show, replacing generic "they didn't show up" notifications with enriched profiles explaining why each prospect matters.

With this automated workflow, they saw a no-show recovery rate of 23-26% with zero marginal time investment per lead. The process that one rep spent 20 minutes executing manually now happens automatically at scale.

"Clay has helped us scale the impact of RevOps alongside our revenue."
Stuart Lundberg, Head of Operations

This pattern of capturing what top performers do manually and systematizing it proved that tribal knowledge doesn't have to stay in one person’s head. Clay provided the infrastructure to codify best practices and distribute them across the team.