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Service

Consulting and decision support

Your organisation accumulates data it never uses, or produces figures nobody quite trusts. I bring the missing statistical dimension so that data actually informs decisions.

Discuss your project
01

Who it is for

This service is for organisations that have data and decisions to make, but no statistician to connect the two.

  • SMEs holding customer, sales or operational data
  • Management teams looking to ground a strategic choice
  • Public bodies and non-profits with survey data
  • Marketing teams wanting to go beyond overall averages
  • Healthcare organisations steering their activity
  • Firms needing an independent statistical opinion
02

The problem it solves

The problem is rarely a lack of data. It is that the data stays descriptive: you count, you average, you chart — and stop there. The questions that actually matter (what explains this gap, what happens next, where to act first) need different tooling.

The opposite failure is producing sophisticated indicators nobody trusts, because nobody knows how they are computed or what conclusion they can carry.

My job is to pick the right method — often simpler than feared — and to make it transparent to the people who will rely on it.

  • Your reports describe the past but do not help you decide
  • Two departments produce two different figures for the same thing
  • You suspect a trend but cannot show it is real
  • Decisions rest on intuition for want of available analysis
  • A dashboard exists but nobody opens it
  • You need to justify an investment with evidence
03

What you get

Deliverables are designed to be used by your teams after I leave, not to create a dependency.

  • A written analysis answering your questions, without jargon
  • The relevant indicators with their calculation documented
  • Predictive or scoring models where they are warranted
  • A segmentation of your customers, sites or cases
  • An R Shiny dashboard fed by your data
  • A presentation of the results to your teams or management
04

How it works

  1. Understanding

    I start with your business and your decisions, not your data. Otherwise the analysis answers the wrong question.

  2. Stocktake

    A review of what you already have: sources, quality, usability. The diagnosis is often instructive in itself.

  3. Analysis

    Iterative work with checkpoints. You watch the results take shape rather than discovering a final report.

  4. Handover

    Debrief, documentation and, if you wish, training your teams to maintain it.

05

Concrete examples

Types of question handled, by way of illustration.

Segmenting a customer base

Grouping customers by actual behaviour rather than categories decided in advance, and deriving profiles you can act on.

Understanding revenue

Which factors are genuinely linked to it, and which are only a misleading correlation? Modelling and ranking of effects.

Anticipating a risk

Estimating the probability that a site, contract or customer tips over, so effort goes where it counts.

Comparing entities

Does one site really outperform another, or does size and context explain the gap? A like-for-like comparison.

Using a satisfaction survey

Beyond the overall score: what genuinely drives satisfaction and what is just noise.

Automating reporting

A monthly report rebuilt by hand becomes a document that regenerates itself in one command.

Frequently asked questions

Our data is messy and spread across several files. Is that a blocker?
No, that is the normal situation. Reshaping, merging sources and handling inconsistencies are an integral part of the assignment. It is better to give me access to the data as it is than to a “cleaned” version whose cleaning decisions I cannot see.
Does this kind of project need artificial intelligence?
Rarely, and that is good news. On business data of typical size, a classical statistical model performs just as well, explains itself in one sentence and needs no special infrastructure to maintain. I propose a complex method only when it delivers something simple methods do not.
What happens after the assignment?
You have everything: the code, the documentation and the explanations. Your teams can run and extend the analyses without me. If you would rather have ongoing support, that is agreed at scoping — but it should be a choice, not a dependency you are stuck with.
Do you work on site?
Yes. I am based in Belgium, so meeting in person is immediate there, and it remains possible in neighbouring countries depending on the project. Scoping and debrief sessions benefit greatly from it; the analysis work itself is done remotely, which makes distance irrelevant to the rest.
Can we start small?
That is what I recommend. A first focused analysis on one precise question shows you what your data genuinely supports, and saves you committing to a large project on a hunch.

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A dataset to analyse, a method to validate?

Describe your need in a few lines, or book a first no-commitment call. I will tell you straight if I am the right person for it.

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