Kevin Larretche. Discuss your business questionDiscuss your question
Method

How the studies are built, checked and refused

Three rules I do not break, what happens to the file you send me, and who is behind it.

Three rules

Checkable

Each study includes the data as used, calculation code and supporting workbook, with assumptions and setup instructions. A tool for refreshing new periods is a separate agreed scope.

Flexible

I work directly with your team. We agree whether you need a one-off study, a recurring review, or an Excel or Python tool with training for your analyst.

Refusals

Some questions your data cannot answer. When that happens the deliverable says so, explains what is missing, and designs the test or the export that would answer it.

Your data

What I need

One export: product by store by week or day, with units and revenue, two years or more. Unit costs where you have them. Margin scenarios need actual costs or explicit assumptions. Proposed price changes still need testing. Promotion flags if your system records them. Any column names, CSV or Excel.

What I do not need

No system access, no integration, no IT project. Customer names and personal data are not required for any of these studies and should not be sent.

How it is handled

Files go through a private workspace under a signed data processing agreement, are used only for your engagement, and are deleted at the end on request. The studies on this site run on public files, because client work is not published. Your own study stays yours.

What you keep

The findings, assumptions, calculations, workbook and code. If you commission a refreshable tool, the handover includes setup instructions, input checks and training.

Who is behind it

Kevin Larretche

I'm Kevin Larretche. 7+ years in FP&A, including five at Devialet in Hong Kong.

I owned budgets, closed months and presented the P&L and studies to management. I built the automated models behind price, volume and mix analysis and ROI work, set pricing structures with new partners while protecting the margin. Those are the same questions I measure now, with better statistics behind them.

The data science is the other half. FP&A taught me which questions are worth asking and how a CFO will test the answer. The statistics are how I answer with a measurement instead of an opinion.

A number that does not change a decision is not worth the work. So every study ends in a decision: raise this price, drop that promotion, stop funding this market.