Mix Marketing Modeling
🏢 Client Overview
Ekimetrics is a European leader in AI‑powered and data science solutions designed to drive business performance.
The company develops software platforms that combine Mix Marketing Modeling, Insights & Optimization, predictive modeling, and automation to help organizations steer their investments, understand growth drivers, and optimize decision‑making.
Its solutions turn data into operational tools, seamlessly integrated into existing technology ecosystems and built to support both human workflows and AI systems.
📇 Key Elements
| Category | Data |
|---|---|
| Client | Ekimetrics |
| Project Year | 2026 |
| Language | English |
| Tools and Frameworks | - Sphinx 9.1.0 - PyData Theme 0.17.0 |
| Expertise | Audit et consulting |
| Role | Expert Technical Writer & Consultant |
| Collaboration Mode | Collaboration via "Crème de la Crème" |
🧩 Project Overview
Audit and POC
During this audit phase, I conducted an in‑depth analysis of the four technical documentations that form the core of Ekimetrics' Mix Marketing Modeling (MMM) solution. Although each documentation targets both internal and external data scientists and contains highly advanced content, the overall experience did not yet constitute a unified documentation ecosystem.
The main objective of the audit was to identify areas for improvement in terms of user experience and to propose an architecture for a unified documentation portal. This portal needed to centralize access to resources for internal consultants while enabling controlled access to selected documentation for clients.
To support this analysis, I delivered a detailed recommendation outlining the target architecture of the portal, taking into account:
- the working practices of the different teams (manual documentation, auto‑generated documentation, deployment workflows),
- the constraints of the codebase (including the use of Sphinx),
- and the distinct needs of internal and external audiences.
Finally, I developed a proof of concept using Sphinx and the PyData theme to validate the technical feasibility of the proposed solution and to illustrate the best practices required for more coherent, maintainable, and user‑oriented documentation.
👩💻 My Role
I drew on my ability to quickly acquire new knowledge, immerse myself in highly technical content — in this case aimed at teams of data scientists and engineers — and continuously learn throughout the project. I also applied my skills in documentation UX and website design to deliver a unified experience tailored to different levels of expertise and internal needs.
I conducted a comprehensive audit of the four technical documentations that form the core of the MMM solution. The audit report provides precise recommendations to improve documentation UX, structured best practices, and steps that can be reused within an AI‑driven workflow for continuous documentation improvement.
I proposed a unified architecture for the documentation portal, bringing together manual documentation, auto‑generated content, and existing deployment workflows.
I learned to use Sphinx, the PyData theme, and their directives, and adopted reStructuredText to build a proof of concept demonstrating the technical feasibility of the documentation portal. This prototype allowed me to illustrate best practices, validate architectural choices, and secure the future implementation.
🛰️ Technologies & Tools
Sphinx, PyData theme, ReStructured Text, Visual Studio Code, GitHub, Sourcetree, English.
🔗 Link to Final Site
This documentation is not publicly available and therefore cannot be included in my portfolio.
©Author: Florence Venisse, Expert Technical Writer – Initial version of 05/16/2026.