Hey everyone Another practical use case from our Clay experiments. Here, we identified the right apparel and fashion brands to target by estimating their e-commerce volume using website traffic. Team usually invest in just one data tool but Clay’s waterfall enrichment allows you to pull traffic data across multiple sources:
Low-credit tools first
Higher-confidence tools next
If one source didn’t have the data, the next one kicked in automatically. We added conditions to save credits:
Only companies above a certain size
Only target cities
Then we marked companies as valid only if traffic exceeded a certain monthly visits. Out of 10 companies, 2 made the cut—exactly the ones worth reaching out to. Everything you would see in the video — multiple sources, logic, and filtering — runs inside Clay. Below is a quick guide and here is the link to the video: https://youtu.be/In_Ik6JrZs0
https://youtu.be/14P_x9jJJEc Created a quick video on building sales signals using Clay Most people wait for obvious sales signals: RFPs, hiring posts, demo requests. The reality? 90% of B2B sales happens before any of that exists. In this video, we show how to engineer sales signals using Clay by making calculated assumptions from data: • Turning indirect data into ICP & GTM fit signals • Using enrichment + AI agents instead of gut feel • Approaching accounts with probability, not guesswork If outbound feels random, this will change how you think about sales signals.
