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:
GTM Engineering Model Analysis on Reddit
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:
Upload CSV list of customers to Clay
Create Ocean.io enrichment
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:
Clay Tip: “clay.new” = 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.
Adding Ocean.io as an Enrichment in Clay
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:
The prompt was very simple:
This is a list of customers and each column provides some additional information on that company. Do a statistical analysis to find 3 commonalities of these companies. Provide a summarized report that defines who you'd use the 3 commonalities to find similar accounts to sell into.
I’m pretty excited to explore other analysis capabilities. Especially to correlate ACV and Sales Cycle to really optimize accounts that have the highest potential to convert at the highest price the fastest.
3. Write Ocean.io Output to a New Table
“Write to Other Table” is a pretty nifty table transformation tool provided by Clay.
You want to use this when you:
Have a list of data in a cell
Want to extract that data and turn it into rows on a table
The result of this is taking the 5-10 lookalikes derived from each company and ‘writing them over’ to a new table like so:
How to Use ‘Write to Other Table’ in Clay
The result is this:
Write to Other Table Formatting
and that’s a wrap!
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