Improving Efficiency in Building and Refining Target Lists

Hi I am looking for a more efficient way to build target lists, ie if I build a list of Accounts or Contacts and then want to refine it, it just ads net new contacts, but how do I re run the collection so it removes the unqualified results.. right now I am copying the table adding new exclusion or inclusion filters and then running again, but it does not seem practical or efficient.

6 comments

Channeled
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To efficiently refine your target lists in Clay without copying tables, you can use our "Filter List of Objects" function. Here's how: 1. Navigate to the enrichment panel and find the "Tools" section. 2. Look for the "Filter List of Objects" option. 3. Select the array of objects (your target list) you want to refine. 4. Choose the field you want to filter by (e.g., job title, company size). 5. Set your filter mode and specify the values to include or exclude. 6. Run the action to apply the filter. This process will create a new filtered array, removing unqualified results without duplicating your table. You can also use the "write to table" feature to update your existing list with the refined results.
Sources: Find People [Clay 101] - Clay University Filter List of Objects with Clay Normalize and Deduplicate a List with Clay
Dan V.
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It does not seem as efficient, ie removing all rows that no longer fit?
Dan V.
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Yeah it’s not efficient - I have to copy and re filter to remove which does not make sense
Dan V.
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?
Nico M.
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Dan V. i would add a column using claygent connected to your OpenAI api key. Build a prompt to qualify if a person fits your ICP or not and have an output field with a binary yes/no response. DM if you want some help with this
Owen C.
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Hey Dan, thanks for reaching out! Nicholas hit the nail on the head here. Determining whether a lead is qualified can be quite subjective, but the goal is to establish an objective answer—whether that's a simple yes/no or a scale (e.g., 1–10, with 10 being a perfect fit and 1 being a poor fit). From there, we can work backward to develop a prompt or apply further enrichments to gather data that helps us reach a clear, objective conclusion. Once we have a clear conclusion (in the form of a checkbox column, a number range or score), we can then begin filtering out leads that don't meet our criteria within the same table. Let me know if you'd like to dive deeper into this! :)