MARKET RESEARCH

Alert the research team when a survey theme spikes above baseline

On a daily schedule, this workflow classifies the day's new survey comments, compares each theme's volume against its rolling baseline, and pings Slack when any theme spikes.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires
  • ActionRead yesterday's responses from AirtableAirtableAirtable
  • ActionZero-shot classify comments into themesHugging FaceHugging Face
  • LogicCompare theme share to baseline, test for spike
  • OutputPost spike alert with examples to SlackSlack

What it does

Each day it classifies the previous day's survey verbatims into theme clusters with a HuggingFace zero-shot model, then checks whether any theme's share of comments has jumped meaningfully above its trailing baseline. If a cluster spikes, it sends an alert with the theme, the size of the jump, and a few example comments.

When to use it

Use it when waiting for the quarterly board is too slow to catch a sudden problem, like a botched release driving a surge of reliability complaints. It turns survey free-text into a near-real-time early-warning signal.

How it works

  1. 1A daily scheduled trigger fires.
  2. 2The workflow reads yesterday's new responses from Airtable.
  3. 3Each comment is zero-shot classified against the theme labels in HuggingFace.
  4. 4Logic compares today's per-theme share to the trailing baseline and tests for a spike.
  5. 5If a theme breaches threshold, an alert with examples is posted to Slack; otherwise the run ends quietly.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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