Sort inbound leads by how well they fit your criteria

A fit-labelled lead list with a reason per lead and a borderline queue for human decisions.

Choose one of three approaches. The steps for that option appear below.

Choose an option above. On a phone, swipe the tabs left or right to see all options →

ChatGPT: labels leads and drafts the summary

You check sources, rules and decisions. ChatGPT drafts only from the checked material.

  1. You

    Prepare the checked inputs

  2. You

    Prepare numbered criteria list

  3. ChatGPT

    Label leads against written criteria

  4. You

    Check labels and record verdicts

  5. ChatGPT

    Draft the screening summary

  6. You

    Approve or hold

  1. Prepare the checked inputs

    Use: You

    Have ready: the inbound lead export and the agreed fit criteria.

  2. Prepare the numbered criteria list

    Use: You

    Have ready: agreed fit criteria. Paste the written fit criteria as a numbered list and remove anything not written by sales leadership

    Check

    Check the list matches the leadership page word for word and nothing new was added.

  3. Label leads against the written criteria

    Use: ChatGPT

    Ask ChatGPT to label every lead with fit, numbered-criterion reason and borderline flag.

    Details

    What to paste into the tool

    Task: Labelled table with reason and borderline column. Here is a lead export and numbered fit criteria. For each row give Fit, No fit or Borderline, and a reason that names the criterion number. Use only these criteria. Do not invent criteria. If the row lacks data for a criterion, mark Borderline and say which data is missing.

  4. Check labels and record verdicts

    Use: You

    Check each band against the criteria and fill a reviewer verdict column.

    Check

    Check a sample from Fit, No fit and Borderline bands; fix or relabel any row that breaks the written criteria.

  5. Draft the screening summary

    Use: ChatGPT

    Ask ChatGPT for counts per label, rejected categories and open questions from the checked table.

    Details

    What to paste into the tool

    Task: Draft screening summary. From this reviewed table, write a short summary: counts per label, the reasons given for No fit grouped by criterion, and open questions. Report only what is in the table.

  6. Approve or hold

    Use: You

    Decide the borderline queue and sign off the list and summary.

    Check

    Recount the summary numbers from the sheet. The human decision is final. No lead is contacted, rejected or disqualified in any system by this workflow.

Before you paste into any AI tool, remove any confidential information. Put it back at the end.

Tools in this workflow

Deciding between ChatGPT and Claude for this? See the comparison.

Final check

Every option keeps the reason next to each lead, uses only the written criteria, and ends with the sales lead deciding the borderline queue.

Check the result against your original inputs. Approve it before sharing or acting.

What still needs checking
  • Lead rows are personal data. Plan terms for storing or training on uploaded personal data were not read for any tool here. Row-count limits for a full export were not found on the source pages.

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