LEAD GENERATION

Detect repeat doc visitors and email account owners a daily warm-account digest

Aggregates documentation download history nightly, flags accounts whose visitors crossed a repeat-engagement threshold in the last week.

CategoryLead Generation
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily morning schedule fires
  • ActionQuery trailing-7-day download events from PostgresPostgreSQLPostgres
  • LogicFilter to accounts past repeat-engagement threshold
  • LogicRank warming accounts and assign owner
  • OutputEmail each owner a ranked warm-account digestGmailGmail
  • ActionRecord digest send timestamp in PostgresPostgreSQLPostgres

What it does

Looks beyond single downloads to spot accounts that are quietly heating up. Each night it scans the rolling download log, counts distinct assets and visits per company over the trailing week, and surfaces the accounts crossing a repeat-engagement bar — then sends owners a ranked email so they prioritize the right outreach.

When to use it

Your buying signal is a pattern, not one click — a champion pulling multiple guides over several days. Ideal for PLG and developer-led motions where one download is noise but three in a week is a meeting.

How it works

  1. 1A scheduled trigger runs each morning before standup.
  2. 2Query the Postgres download-events table for the trailing 7 days, grouped by company.
  3. 3Filter to accounts past the repeat-visit threshold (distinct assets and total downloads).
  4. 4Rank the qualifying accounts by a weighted engagement score and attach the owning rep.
  5. 5Email each owner their personal ranked digest of warming accounts with last-touched assets.
  6. 6Stamp the digest send back to Postgres so the next run only re-flags genuinely new movement.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect GmailRead, draft, send, label.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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