Examples
What this actually looks like, by industry
Patterns, not client work: we don't publish case studies (see About), so every example here is illustrative, the kind of thing this looks like rather than something built for a named company.
A trial goes quiet for four days and nobody's watching until the churn report.
Ten to fifteen percent of trials show real usage and never get a human touch before they expire.
The moment
A trial signs up, uses the product for four days, then goes quiet. The AE finds out when the trial expires, from a churn report, not from anything that happened in between. A Slack alert on signup existed once; it got noisy and was muted within a week.
Product-usage signal routed into a ranked worklist. When usage stalls for 48 hours, an agent drafts outreach from the actual usage pattern, not a template, and it lands in the AE's queue for a human to send.
Where AI earns its place
Drafting the outreach from the usage pattern: unstructured signal, no rule can parse it cleanly.
Where it does not
Deciding which stalled trials are worth an AE's time. That's a scoring rule against plan size and usage depth, not a model's judgement.
After: A stalled trial produces a drafted, specific outreach in the AE's queue within a day, instead of a name on a churn report a week later.
HubSpot or Salesforce, a product analytics tool, Slack
The rest of the range
- ICP definition for a new tier or vertical
- Outbound sequence to a lookalike account list
- Lead-routing workflow between marketing and sales
- Renewal-risk alert built from product and CRM data
- Managed outbound through a launch quarter
Illustrative pattern, not a client result. No names, no metrics we did not measure.
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