ServicesThree pillars, one system

OutboundPipeline from cold↗ABMNamed accounts, warm market↗RevOpsData, routing, reporting↗

OUTBOUND

Pipeline you can plan around

Low volume, high intent, inboxes that actually land.

GTM BrainFOR OUTBOUND

It gets sharper with every reply it reads.

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B2B CONSULTANCY · DATA & TARGETING

Built a verified, niche-specific database with 6,287 in-target companies and 14,046 qualified contacts for ABM and outbound

6,287verified in-target companies

14,046qualified contacts

TECH STACK USED

Who is the client?

Cloudset is a Zendesk implementation and app partner specializing in making complex technical processes easy to understand and simple to execute.

Since 2009, they have delivered best practice Zendesk implementation services that help customer support teams optimize their use of the platform.

They also develop add on applications for Zendesk that enable more advanced and tailored support operations.

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How the client met us

In July 2024, Cloudset’s founder Graham discovered Matteo’s profile on La Growth Machine’s expert advisor page.

After booking a call, Graham decided to work together to build the high accuracy database he had been trying to create for years.

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Context

Cloudset’s primary goal was to build a highly accurate database of enterprise companies that used a specific customer support platform.

Graham wanted to identify every company with 200 plus employees across the US, UK, and major European countries that used the platform, then map the right decision makers inside those accounts.

Cloudset had already tested common technographic data sources such as BuiltWith and Apollo, but consistently found the data outdated and incomplete. Companies listed as platform users often were not, and many real users were missing.

This created a direct ABM problem. Out of target accounts increased inefficiency and increased the risk of contacting the wrong companies.

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What was the problem?

Lack of sales technology expertiseThe team lacked a reliable method to verify technographic platform usage beyond standard tools and did not have the AI and automation capability to build a higher accuracy system.

Inaccuracy of standard databasesExisting sources such as Apollo, BuiltWith, and Sales Navigator produced incomplete and outdated results, which prevented Cloudset from building a trustworthy list of platform users.

Need for an ABM grade, compliance minded databaseThe goal required strict ICP filtering, verifiable evidence of platform usage, confidence scoring, and clean CRM enrichment so outbound could be targeted and responsible.

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The approach we took

1. Strategy and ICP analysis

Defined strict ICP criteria including geography across the US, UK, and major European countries and company size of 200 plus employees

Set clear goals before execution Use AI techniques to improve technographic accuracy

Merge multiple sources to maximize discovery of platform users

Implement confidence scoring based on verification strength

Confirm platform presence using scrapers, data providers, and AI

Enrich the CRM with contact data and automate SDR tasks

2. Data enrichment and ABM targeting

Aggregated a master company list in Clay by merging multiple sources including BuiltWith, Apollo, and Sales Navigator

Supplemented the list with additional discovery signals Companies following the platform on LinkedIn

Companies hiring for platform specific roles

Applied initial company filtering using ICP rules Geography filter

200 plus employee filter

Built a verification waterfall to confirm real platform usage Verified platform specific subdomains using JavaScript and Zenrows scraping

Scraped Google to identify valid platform URLs

Deployed a Clay AI Agent to run autonomous searches when needed

Implemented confidence scoring based on verification results Companies confirmed by multiple methods were scored higher than single method confirmations

3. Channel and infrastructure setup

Designed the database to support outbound activation, including segmentation and decision maker mapping for ABM execution

Built the system inside Clay to support repeatability and future updates without rebuilding from scratch

4. Structured testing and scaling

Ran the verification waterfall at scale across the full merged company list

Added a two phase decision maker discovery process to maximize coverage Phase 1 sourced contacts from Apollo and Sales Navigator and deduplicated all records in Clay

Created three seniority tiers and excluded irrelevant profiles using keyword exclusions such as intern, sales, and success

Phase 2 initiated a secondary waterfall for accounts missing contacts, using Apollo plus Clay AI Agents to search specific roles

Enriched qualified contacts with verified work emails and added phone numbers for Tier 1 and Tier 2 contacts

5. CRM connection and pipeline tracking

Synced enriched contacts into HubSpot through Lemlist

Used Make.com to automatically create call tasks in the CRM so SDRs had structured next actions tied to the enriched database

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Results

Built 6,287 in target accounts

Produced 14,046 qualified contacts

Saved 300 plus hours of client time by building a database the client had been trying to create for years

"A forward-thinking approach, and deep understanding of AI-driven outbound strategies"

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