MARKET RESEARCH

Alert on rating-drop spikes with root-cause review themes

Watches your app's incoming reviews on a tight cadence, detects sudden spikes in negative sentiment after a release.

CategoryMarket Research
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFrequent schedule fires
  • ActionFetch reviews since last runApify
  • LogicDetect negative-velocity spike vs baseline
  • ActionCluster spike reviews into root-cause themesHugging FaceHugging Face
  • OutputOpen PagerDuty incident with top themePagerDutyPagerDuty

What it does

This polls your app's newest reviews frequently, compares negative-review velocity against a rolling baseline, and when a spike crosses your threshold it classifies the offending reviews into emerging complaint themes and pages the on-call owner with the likely root cause.

When to use it

Right after shipping a release, or continuously for a high-traffic app where a regression can crater your rating before anyone notices. It turns review noise into a single actionable page.

How it works

  1. 1A schedule fires every few hours.
  2. 2Apify fetches reviews posted since the last run across your store listings.
  3. 3A logic step computes negative-review velocity and compares it to a rolling baseline; if it is within normal range the run ends quietly.
  4. 4On a spike, a Hugging Face model clusters the new negative reviews into emerging complaint themes with example quotes.
  5. 5A PagerDuty incident is opened for the on-call owner, titled with the dominant theme and carrying the supporting evidence so triage starts with a hypothesis, not a blank page.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  3. 3
    Connect PagerDutyIncidents, on-call, escalations.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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