MARKET RESEARCH
Emerging-complaint spike alert from BigQuery review data to Slack
Runs every few hours over the review-theme table in BigQuery, detects clusters whose volume is spiking versus their baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires every few hours
- ActionQuery recent vs baseline theme volumeBigQuery
- LogicFlag spikes and dedupe today's alerts
- ActionPick representative recent quote per themeHugging Face
- OutputPost spike alert to on-call Slack channelSlack
What it does
Watches your accumulated review-theme data for sudden surges. When a feature-request or complaint cluster jumps well above its recent baseline — usually after a release or an outage — it alerts the team immediately instead of waiting for the weekly digest.
When to use it
Turn this on around launches, pricing changes, or any period where a regression could spike a complaint theme overnight. It's the early-warning companion to slower weekly and nightly review jobs.
How it works
- 1A schedule fires every few hours.
- 2A BigQuery query computes each theme's volume in the recent window against its trailing baseline.
- 3Logic flags any theme exceeding the spike threshold and dedupes against themes already alerted today.
- 4For each flagged theme, a HuggingFace step selects the most representative recent quote.
- 5A formatted alert with theme, growth multiple, and quote is posted to the on-call Slack channel.
- 6The alert ledger is updated so the same spike isn't re-paged.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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