MARKET RESEARCH

Warehouse feature sentiment and build a Notion regression tracker

Periodically loads classified review sentiment into a data warehouse for analysis and writes any newly detected feature regressions as cards in a Notion tracker for product…

CategoryMarket Research
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerRecurring schedule
  • ActionScrape latest app reviewsApify
  • ActionClassify feature and sentiment into rowsOpenAI
  • ActionAppend rows to BigQuery warehouseGoogle BigQueryBigQuery
  • LogicQuery for features newly below baseline
  • OutputCreate Notion tracker card per regressionNotionNotion

What it does

On a recurring cadence it scrapes new reviews, classifies feature and sentiment, and appends the structured rows to a BigQuery table for long-term analysis. It then runs a regression query against the warehouse, and for each feature whose sentiment newly crossed below baseline it creates a tracker card in Notion with the metric, trend, and linked reviews so the team can pick it up.

When to use it

Use it when you need both a queryable analytics history and a working surface product can act on. Combines durable warehousing for ad-hoc analysis with a lightweight Notion board for triage, avoiding a separate ingestion job.

How it works

  1. 1A recurring schedule starts the run.
  2. 2Apify scrapes the latest reviews.
  3. 3OpenAI classifies feature and sentiment into structured rows.
  4. 4BigQuery receives the appended rows for warehousing.
  5. 5A regression query flags features newly below baseline.
  6. 6Notion gets a tracker card per new regression with metrics and linked reviews.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect BigQueryDatasets, queries, schemas.
  4. 4
    Connect NotionPages, databases, comments.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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