Case study

An automated pipeline that turns raw market data into a ranked opportunity list

How a manual, once a quarter research exercise became a scheduled pipeline that runs itself and hands back a ranked report.

Why is manual market research a problem?

It's slow, inconsistent between people, and stale by the time a report is finished.

Spotting where a market is growing, or where a gap in the competition is opening up, is normally a slow, manual research exercise: pull data by hand, eyeball it in a spreadsheet, and try to remember to redo the whole thing next quarter. It's inconsistent between people, easy to skip when things get busy, and by the time a report is finished the data underneath it has already moved.

How does the market intelligence pipeline work?

It runs four automated stages, collection, scoring, synthesis, ranked output, with no manual pull required.

An automated, multistage pipeline that collects data from public sources on a schedule, scores each item for growth signal and competitive gap, synthesizes the results, and produces a ranked output automatically, no manual pull required.

STAGE 1

Data collection from free public sources on a regular schedule

STAGE 2

Signal scoring: growth trend, competitive density, and gap size per category

STAGE 3

Synthesis: filtering noise down to the categories worth a human's attention

STAGE 4

Ranked output: a report ready to read, not a raw data dump

Market intelligence dashboard mockup showing ranked opportunities, trend cards, and collection volume

A dashboard view of the pipeline's output: ranked opportunities, trending categories, and pipeline volume in one screen.

Outcome

The category names and numbers shown above and below are illustrative examples built to demonstrate the shape of the output. They are not real market data or real company figures.

4 stages collection, scoring, synthesis, ranked output, running on a schedule

0 manual pulls the pipeline runs itself; a person reviews the ranked output, not raw data

Hours, not weeks example turnaround from collection to a ranked report (illustrative)

Consistent the same scoring method every run, instead of a different person's judgment each quarter

Tech approach

Built in Python against free public API endpoints, with a lightweight scoring layer that ranks categories by growth signal and competitive gap rather than raw volume. Results are stored in SQLite and exported to a spreadsheet report so the output is easy to hand to a nontechnical reader. The whole pipeline is designed to run unattended on a schedule and produce a ready to read report at the end, not a dataset that still needs a human to interpret it.

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View the dashboard mockup → Back to projects