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Business caseFebruary 2026Internship application business case

French Solar Market Study

The Power BI business case that won me the internship, turning 23,368 installations into a two-speed marketing strategy.

23,368installations analysed
70%of production in the top 5 regions
9% → 41%large sites: share of projects vs production

The need

Two questions from the business: where should marketing campaigns be concentrated, and which strategy suits which part of the market? A uniform national campaign would dilute the budget, so the answer had to separate regions and installation sizes.

What I owned

All of it, as part of my application: the framing, the data preparation, the dashboard, the recommendation and the presentation. I also taught myself Power BI for this project.

The solution

An interactive dashboard over public data from data.gouv, built to answer the business questions and not simply describe the data.

The dashboard: geographic concentration, top regions, share of production and evolution over time.
Prepare
Drop irrelevant columnsHandle missing valuesKeep France onlyHarmonise region names
Enrich
Production per m²Size categoriesPower in kW
Analyse
KPIsRegional rankingsSize segmentsTrends over time
Recommend
Volume engineValue engine
From raw open data to a recommendation.

How it was built

The story came first. I defined the business objective and the analytical questions before touching the data, then checked the figures against external references. The main obstacle was geographic data, where the same region appeared under different spellings and languages, such as Bretagne and Brittany.

Results

  • Concentration. Five regions lead on volume, and the top five hold about 70% of the 127.54M kWh produced each year.
  • The volume and value paradox. Small sites are 58% of projects, while large sites are only 9% of projects but 41% of production.
  • Timing. After a peak around 2010 and a long decline, the market has recovered strongly since 2022, especially on medium and large sites.

The recommendation was a two-speed approach. A volume engine targets small and medium sites in the leading regions with digital lead generation, simulators and quick quotes. A value engine targets large projects with direct outreach.

A bonus proposal covered two AI agents for lead handling and customer support, with human validation built in. It set out the direction my internship then took.