MARKET RESEARCH

Geo-Expansion Signal from Job Locations

Aggregates the office and remote-eligible locations across competitor job postings, detects a new city or country appearing.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule starts the analysis
  • ActionApify scrapes postings and extracts each role locationApify
  • ActionNormalize and load location rollups into SnowflakeSnowflakeSnowflake
  • LogicIsolate net-new cities or countries vs. footprint
  • ActionOpenAI summarizes which functions lead the entryOpenAI
  • OutputSlack report of new markets and entry patternSlack

What it does

The location field on job postings is an underused tipoff: when a competitor starts posting roles based in a city where they had none, they are usually opening a market. This workflow rolls up posting locations per competitor, compares against their established geo footprint, and flags new markets.

When to use it

Run it when geographic expansion by rivals matters to your planning, for example to anticipate a competitor entering a region where you currently have an open field.

How it works

  1. 1A weekly schedule starts the analysis.
  2. 2Apify scrapes current postings and extracts the location of each role.
  3. 3Locations are normalized and aggregated per competitor, then loaded into Snowflake.
  4. 4A logic step compares this week's location set to the historical footprint and isolates net-new cities or countries.
  5. 5For each new geo, OpenAI summarizes which functions are being hired there (sales-led entry vs. engineering hub).
  6. 6A Slack message reports each competitor's new market and the likely entry pattern.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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