How Coverflex uses Clay to automate signal-based outreach for 3M+ companies, adding 200+ monthly demos

Coverflex’s challenge: Ineffective enrichment and signal tracking with legacy tools

Pedro's team initially relied on a variety of tools for data enrichment and intent signals. For data enrichment, they relied on separated data providers like Datagma, Dropcontact, and others. For intent signals, they used a mix of Ultrarev, CaptainData, Predictleads and many other tools, but these were each becoming too expensive to manage in aggregate.

As his team aimed to manage and process data on millions of companies across three countries, managing a mess of tooling could no longer keep pace with their data demands. Missing data and poor signal quality meant failure to generate the ARR Pedro's team needed, so he needed a more capable and scalable solution. Now, in Clay, Coverflex can access and orchestrate all their providers all in one place.

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The solution: Building a custom enrichment workflow with Clay, N8N, and Postgres

After exploring several tools, Pedro chose Clay for its flexibility and powerful enrichment capabilities alongside N8N and Postgres.

To address his challenges, Pedro built an automated prospecting tool using Clay for signals and enrichment, N8N for workflow automation, and Postgres for database management.

How it works:

1. Signal and enrichment with Clay: Pedro uses Clay to enrich company data and gather buying signals like headcount changes, job listings, and job promotions so he can prioritize accounts and auto-delete unqualified prospects. Bonus: Clay’s API capabilities allow seamless integration with the rest of their system without manual intervention for custom coded signals that Pedro’s team built in-house.
2. Workflow automation with N8N: Pedro utilizes N8N to connect various data sources across his stack and automate complex workflows. This setup allowed his team to manage millions of records efficiently, triggering automated processes based on real-time signals from Clay.
3. Data management with Postgres: To handle their massive volume of data, Pedro’s team stores the data in Postgres. This approach enabled the team to refresh data regularly and maintain a dynamic database of over 3 million companies and 1 million prospects.

This combination allowed the team to build a robust internal lead-generation machine:

“With Clay, N8N, and Postgres, we’ve created an internal Zoominfo that generates more than 200 demos a month,” says Pedro.

Automating data enrichment, signal collection, and personalization for a huge TAM

Coverflex starts with a TAM of over 3 million companies across Portugal, Spain, and Italy, and uses Clay’s AI research agent to help them narrow and qualify that list.

Every month, Coverflex runs all these companies through Clay to check for key buying signals like headcount changes, website visits, job postings, LinkedIn engagement, and more.

For relevant companies, Clay identifies key decision-makers (HR, Finance, CEO) and enriches their profiles with contact details such as email addresses, LinkedIn profile URLs, mobiles, recent LinkedIn posts, and recent job changes or promotions. If the contact already exists, Clay enriches and updates their profile, then alerts the sales team; otherwise, it creates a new lead.

Once they identify and enrich qualified accounts with Clay, they deploy a multi-touch outreach campaign that includes:

• Automated, personalized email sequences through Lemlist
• Custom presentations and product tours tailored to the prospect’s specific needs
• Automated, personalized hand-written letter via mail
• Custom landing pages featuring personalized calculators and demo videos

Pedro’s workflows ensure that outreach only happens when specific signals are identified, allowing Coverflex to prioritize and manage their efforts efficiently. To find accounts, they enrich basic info from Linkedin and websites to find contact info (email / phone), track headcount changes, and track new jobs postings. They also cross check this with local databases through API's to complete with more info (Headquarters location, VAT etc).

“Clay has become our primary source of enrichment. We built an automated flow that identifies signals, enriches data, and pushes leads to our sales team only when it’s most relevant. Instead of flooding HubSpot with data, we only push leads once they’ve shown strong signals. This helps our sales team stay focused and reduces noise”. Pedro explained.

Automatically targeting ads and personalizing messages based on relevant interactions

Coverflex personalizes LinkedIn ads based on buyer signals received from Clay. For example, if they know a company is increasing headcount, they’ll show people from that company ads about how Coverflex can help companies that are rapidly hiring.

Pedro’s team also helps MDRs personalize messages to target personas based on their interactions with relevant LinkedIn posts. To automate personalized messages to post likers, the MDR team chooses a post that talks about a relevant theme (e.g., employee benefits). They import the post to Clay to find people in their target persona who interacted with it and create a Hubspot record for them. Once they’ve honed in on the right target persona / decision makers at relevant companies that interacted with the post, they use AI to build a personalized message.

For example:

1. Justin, an HR leader from Nike, commented on a post about employee benefits. Since he’s Coverflex’s target persona, they will send a message like "I saw that you commented on Mishti’s post about [X theme].”
2. Timberlake, a store employee at Nike, liked a post. Since he’s not Coverflex’s target persona, Coverflex will use clay to find his manager and send something like "I saw that Timberlake from your company liked a post about benefits—maybe he’s interested in having them in your company. Do you want to chat about adding benefits at Nike?"

"I’m a partner to the sales and marketing teams and can use Clay to build workflows that can do anything they need, whether that’s finding decision-makers, buying signals, enriching prospects for contact info, or something else,” said Pedro.

Automated prospecting for MDR and BDR teams

Pedro’s automation stack empowers non-technical teams like MDRs and BDRs to prospect more efficiently than ever. Pedro created a simplified interface within Clay where sales reps can upload CSV files with target companies. The workflow then handles the rest, enriching contacts, finding decision-makers, and pushing data directly into Hubspot.

“Our MDRs aren’t technical, so I’ve built workflows that do all the heavy lifting,” Pedro explained. “They simply import a list, select their target criteria, and Clay does the rest—from enrichment to generating personalized outreach materials.”

This approach allows Pedro’s team to focus on strategic decision-making rather than manual data entry, enhancing productivity and reducing the time spent on administrative tasks.

With these automations, Coverflex's sales and marketing teams can now operate at 5x their previous capacity, enabling rapid growth across its target markets.