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Hey, GTM Engineers!

Hope you all had a great Thanksgiving. Don’t forget about our ‘RevOps Oven’ holiday special! It could be a perfect gift for that someone special on your GTM team. You won’t be able to find one in stores…

GTM Engineering is becoming a movement and I’m predicting 2025 as the year for GTM Engineering breaks out.

To help champion this, I created the r/gtmengineering subreddit (32 members so far). There’s already a great community post on Claygent models vs. GTP 4o Mini. Here’s an excerpt and its followed by a conclusion & recommendation:

In addition to this, my goal is also repost any GTM Engineering related content on the claymation LinkedIn page.

Back to regularly scheduled programming….

Today’s post will show you how to build a lookalike engine.

This is an easy way to automate finding lookalike accounts that look like your best customers. Especially helpful for early stage companies who want to replicate early customer wins.

This is an overview of the 3 step setup:

1. Upload CSV list of customers to Clay

2. Create Ocean.io enrichment

3. Write data (found lookalikes) to new table

1. Upload CSV of Customers

OK. Yea, this is super basic, but check out that new trick… type “clay.new” into your browser to spin up a new table:

2. Launch Ocean.io Enrichment

What is Ocean.io?

Ocean.io is an enrichment provider that makes it super easy to find lookalikes of your best customers. They use AI to categorize millions of companies so that you can find matches based on basic characteristics.

Getting Ocean.io setup

This is as easy as adding any enrichment column.

You’re probably wondering why I added in “Veterinary” into the industry keywords.

Prior to this, I had done a customer analysis on firmographic characteristics of the company set. You can do this quickly by downloading a table of all your enrichments as a CSV and upload it to ChatGPT to analyze. I simply asked it to do a statistical analysis on the top 3 common characteristics.

This is what it came back with: