"Every GTM team has different questions they need answered. Clay has the data and flexibility to answer all of them from the same infrastructure." — Owen Chander, GTM Engineer
AlertMedia provides physical risk intelligence and incident response solutions that help organizations protect employees, facilities, and assets worldwide.
Risk Intelligence
Austin, TX
AlertMedia sells risk intelligence and incident response solutions that help more than 3,500 organizations around the world protect their employees, facilities, and assets. The organizations most likely to buy are the ones with distributed workforces and assets, which means AlertMedia's GTM engine benefits from knowing exactly how many locations a prospect operates, where those locations are, and what industry they belong to.
The problem was that AlertMedia's data infrastructure couldn't support the scoring models, routing logic, and rep prioritization the team was trying to build on top of it. Account scoring required inputs that existing vendors got wrong. Inbound leads hit Salesforce with missing fields that cascaded into routing errors. Competitive intelligence from sales conversations sat locked in call transcripts with no structured way to extract it. Owen Chandler, AlertMedia's GTM Engineer, was brought on to close those gaps.
What Owen built with Clay is a data layer underneath AlertMedia's entire GTM motion: from how accounts are scored and prioritized, to how reps prepare for competitive deals, to how marketing captures and activates expressed buyer interest.
Scoring accounts on data that influences deal size
AlertMedia's account scoring model weights a specific set of signals: how many physical locations a company operates, where those locations are, and what industry they belong to. Get those inputs right and the model surfaces the accounts where AlertMedia's product delivers the most value. Get them wrong and reps spend time on accounts that look like a fit on paper but fall apart in practice.
Unfortunately, previous data vendors consistently got these inputs wrong. Industry classifications defaulted to broad categories that didn't reflect what companies actually did. International location counts, one of the strongest predictors of deal size, were unreliable. Every scoring error compounded downstream into misrouted leads, mispriced deals, and wasted time.
Clay served as the enrichment layer to enable the account scoring model to work as designed. Owen built Clay workflows that recategorize industries using Claygent, populate international location counts by waterfalling multiple enrichment providers, and fill gaps in legal names and LinkedIn profiles. The enrichments run on an ongoing basis, writing back to Salesforce so the model's inputs stay accurate as companies grow, restructure, and expand into new markets.
The impact of accurate data shows up in deal outcomes. Top-tier enterprise accounts close at a 22% higher rate and carry a 50% larger deal size than accounts that were improperly scored. "Our account scoring model tells reps where to spend their time. If the inputs are wrong, the whole model is wrong," Owen says. "Clay is how we keep it precise."
Turning sales conversations into structured deal qualification
AlertMedia's enterprise motion runs on MEDDPICC. Every meaningful sales conversation is supposed to update qualification fields on the opportunity: Metrics, Economic Buyer, Decision Criteria, Paper Process, Champion, Competition. When those fields stay current, forecasting holds up and managers can coach on the actual state of a deal.
Keeping them current was the hard part. Reps updated fields inconsistently between calls, and managers couldn't backstop the work. With two sales enablement people covering a growing global sales organization, manual call review topped out at less than 10% of total conversations. The intelligence was sitting in the transcripts. Getting it onto the opportunity record at the pace deals actually moved was the bottleneck.
Owen built a Clay workflow that pulls every recorded call from Gong into a Clay table. Task-specific Claygent prompts parse each transcript against the MEDDPICC framework, pulling structured values for each dimension along with supporting evidence from the call. Clay writes the extracted fields back to the Salesforce opportunity automatically. Reps review and confirm instead of typing qualification updates from scratch.
The same call-intelligence layer powers more than MEDDPICC. It generates over 1,800 AI account summaries on demand from inside Salesforce, used by reps for outbound personalization and territory plans, and scores 700+ calls a month against AlertMedia's selling motion with structured feedback posted to reps and managers in Slack.
The qualification work alone saves the sales org over 200 hours a month. Managers stopped spending coaching time on data hygiene because the qualification picture is already populated by the time they sit down with a rep. "We now have infrastructure to answer anything we need from our calls," Owen says. "It only takes about 30 minutes to set up a new workflow."
Getting qualified leads to the right rep
When a lead fills out a form on AlertMedia's website, their job title determines everything that happens next: scoring tier, follow-up sequence, and whether a rep gets a high-priority alert or the lead enters automated nurture. Before Clay, 27% of inbound leads arrived without a title at all. Those leads scored as zeros and dropped into the lowest-quality follow-up path, regardless of who they actually were.
Clay now enriches every inbound lead within the existing five-minute SLA window, before the rep notification fires. For titleless leads, Clay finds and validates a title so the lead scores and routes correctly. 15% of inbound leads are matched to existing accounts in Salesforce and routed directly to the assigned rep instead of sitting in a general queue.
The same gap existed with webinars. After each event, marketing ops would export attendee lists, scrub and score them, and hand them to reps. The process took two to three days, and even then, reps followed up with only about half of attendees. Marketing saw leads left on the table. Sales saw leads that weren't qualified enough to prioritize.
Owen built a Clay workflow that scores every webinar attendee into hot, warm, and cold tiers automatically. Cold leads are sequenced on behalf of reps. Hot and warm leads trigger rep alerts with context on the attendee and their account. Coverage jumped from 50% to over 90%, turnaround compressed from days to hours, and the system removed the ambiguity about who was responsible for follow-up.
"When someone fills out a form, they should reach a rep who already has context on their account," Owen says. "That's true whether it's an inbound lead, a webinar attendee, or a new thread on an active deal."
How one GTM Engineer built a compounding data layer for the whole revenue org
What Owen built at AlertMedia is a system where each workflow feeds the others. Each lead gets enriched and scored correctly, routed to the right rep, who opens the account record and sees deal intelligence from prior conversations already populated. The rep starts with context that would have taken hours to assemble manually, if it was assembled at all.
AlertMedia runs a biweekly Clay working group that includes the CMO, VP of Sales, and VP of RevOps. The workflows Owen builds touch every part of the GTM org. Sales gets better accounts and competitive context. Marketing gets faster activation and cleaner attribution. RevOps gets a data layer they can trust. The working group has shifted from triaging a backlog to planning what to build next, including living intent scoring that layers real-time signals to give reps a fluid weekly ranking of their best accounts.
"Every team has different questions they want answered, and Clay is flexible enough to answer all of them from the same infrastructure," Owen says. "That's what makes it compound. You're not buying five tools. You're building one system that gets smarter with every workflow."
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