“Matteo at TAM Acceleration has done a fantastic job for us, both technically in knowing his way around Clay and all the other micro web services used, but also in his tenacity to be ever pursuing ideas on how to achieve and improve the system and data accuracy. It feels like we did some pioneering work here”
Graham Robson – Founder @ Cloudset
Who is Cloudset?
One of Zendesk’s first implementation and apps partners
Cloudset specializes in making complex processes and technical functions easy to understand and simple to achieve. They started back in 2009, providing Zendesk best practice implementation services.
Cloudset optimizes the Customer Support teams’ usage of their Zendesk. They use Zendesk to develop add-on apps that enable sophisticated support solutions.
The Context
Cloudset’s Founder Graham had one big goal: Having his dream qualified, accurate Database of companies using a specific customer support platform. All above 200+ employees, across US, UK, and the main countries in Europe.
In the past, Graham tried multiple technographic sources, from Builtwith to Apollo, always finding outdated, incomplete data. Some of the companies that should allegedly use the support platform were in reality not using it, as well as many companies using the tool were missed.
In Graham’s words: “The main motivation was ABM influenced. It was not only inefficient to include out of target customers, it’s a contribution towards not spamming people. We have done our research and we have now a legitimate interest”.
Business Development Blockers
Lack of Sales Tech Know How
The team missed the latest sales AI techniques.
The team had no way to ensure the accuracy of the Technographic data, as well as finding tech-related companies beyond the ones found in Apollo or Builtwith.
Typical Databases were not enough
Cloudset tried all possible databases you could think of for technographic data:
Apollo
Builtwith
Sales Navigator
All of them provided inaccurate or incomplete information, that left the Founder Graham unhappy and frustrated with the results.
How Cloudset met TAM Acceleration
Cloudset’s Founder Graham found TAM Founder Matteo’s profile featured as an Expert in La Growth Machine’s Advisor page, in July 2024.
After booking a call with Matteo, Graham decided TAM Acceleration was the right partner to come up with the Database he had always dreamt of.
Initial Goals
Use the latest AI Techniques to ensure accuracy of the Customer Support Platform
Merge multiple Databases together, to find as many Enterprise as possible that were using the the Platform
Have a Scoring system in place, based on the Technographic data accuracy
Use multiple advanced scrapers, providers, and AI to confirm the presence of the Platform
Update the CRM Hubspot with contacts data, as well as Task for managing the SDRs activities
How did we proceed?
1. We applied a variation of our proven 5 step TAM Process
TAM Analysis: Analysis of your TAM, ICP and Channel
Highly Qualified Database Creation: Build your segmented, highly qualified lead list using Clay and AI Agents
The Atlas Infrastructure: Create the necessary email and Tech Infrastructure to avoid spam
The Aries Funnel: Find the Funnel and Message that brings you the highest response rate
Scaling the Machine: Let’s scale your results by automating, connecting all the dots, and reverse engineering the system
How did we proceed then?
1. Companies Built Up
We started by working on volumes, sending all the companies we scraped to a Clay.com table.
We merged together all companies coming from different Databases, filtered by technographic usage:
Builtwith
Apollo
Sales Navigator
In addition, we added:
Companies following the customer support platform on its LinkedIn page
Companies hiring for the Platform roles
2. Companies’ Filtering Process
On each single Database previously collected, we applied a first filtering based on:
Geographical area
Employees
3. Companies Qualification Process
All companies that pass the first filtering process are added to a Main Database. Here, each company that is potentially a platform user goes through a waterfall process to verify and confirm the presence of the technographic data.
In order it’s applied:
Platform subdomain existence verification, through JavaScript and Zenrows scraper
Platform URL creation and scraping process through Google, to find out if existent or not
Clay AI Agent to perform an autonomous search
If one of the 3 steps returns a positive result, the company is classified as a Platform User.
A scoring applies to score companies that returned more than 1 valid criteria.
Behind the curtain – Process visual
Behind the curtain – Process visual
4. Contacts’ Finding (Phase 1)
Once we knew what companies used the Customer Support Platform, we looked for the decision-makers at those companies.
To find the decision makers, we applied a double filtering: First, finding decision makers on 2 Databases: Apollo and Sales Navigator
We imported all of them in Clay To ensure optimal coverage
We excluded all duplicate contacts
Then an exclusion and tiering process:
Creating 3 different Tiers, where Tier 1 had the main ICP, and Tier 2 and Tier 3 had secondary decision-makers
Excluded keywords that were out of scope (intern, success, sales, ..)
5. Contacts’ Finding (Phase 2)
For all the companies for which we could not find any contact attached, we applied a second procedure of contacts’ finding.
From the main Database, we intersected the information with the contacts’ table to see when a relevant contact was found for the company, and when not.
If it was found, no further action was taken
If it was not found, a successive contact waterfall procedure followed:
With Apollo and specific keywords, built-in Clay
If no contact was found in the first step, a following Clay AI Agent (Claygent) looked for specific roles and contact information within the company
6. Email Finding and Validation
For all the companies that passed the test and were not “Unqualified”, we found and enriched their work email. For the contacts in Tier 1 and 2, we searched for the phone number too.
7. CRM Enrichment and Task Generation
All contacts were in the end saved in the CRM Hubspot, passing through Lemlist. Tasks to call the contact were generated through Make.com.
In Summary over the first 4 months we have:
Generated an extremely targeted Database, that the client was trying to build for years
Qualified and Disqualified companies and contacts based on strict ICP criteria
Saved hours and hours of the client time in sourcing these leads
“Matteo at TAM Acceleration has done a fantastic job for us, both technically in knowing his way around Clay and all the other micro web services used, but also in his tenacity to be ever pursuing ideas on how to achieve and improve the system and data accuracy. It feels like we did some pioneering work here”
