Feedback from Crédit Agricole de Bretagne
Let's get back to basics! Far from complex algorithms and LLMs (Large Language Models), let's rediscover the fundamentals of analysis: explore, analyze, cross-reference, interpret, understand and synthesize.
In a world where everything seems to become programming, implementing data analysis (PCA, AFC, ACM) and unsupervised classification can sometimes be a real challenge.
Segmentation.AI, Micropole'slatest no-codesoftware innovation, puts analysis and interpretation back at the heart of the reactor.
Brigitte LE ROUX, researcher at Cevipof / SciencesPo / MAP5 and author of numerous books, will remind us of the interest and power of these approaches.
Chantal Renault, data scientist in the RDM (Marketing Research and Development) department at Crédit Agricole de Bretagne, will give a concrete illustration of the contribution of Segmentation.AI through the implementation of a CSR customer segmentation. In other words: how can we segment our customers from the point of view of their energy expenditure (transport, housing, etc.) in order to offer them support strategies adapted to their needs and means?
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We invite you to join us at Micropole on March 27, starting at 9:00 a.m., to discover the benefits of these approaches and of our Segmentation.AI solution, thanks to the insights of our speakers.
Register now and don't miss this unique opportunity to discover how Segmentation.AI can transform your perception of your customers and give you the inputs you need to better interact with them.
Agenda
9:00 am: Welcome and breakfast
9:30 a.m.: Plenary session
- Introduction - Brigitte Le Roux
- Overview of Segmentation.AI functionalities and use cases - Tanguy Le Nouvel
- Feedback: CSR customer segmentation - Chantal Renault
11:15 a.m.: Networking
Your stakeholders
Chantal Renault
Data Scientist - R&D Marketing Department
Crédit Agricole de Bretagne
Brigitte Le Roux
Researcher at Cevipof/CNRS - Sciences Po Paris
Researcher at MAP5/CNRS - Université Paris Cité
Tanguy Le Nouvel
Director of Data Science
Micropole
Location
Segmentation.AI's strengths include:
- NoCode: specify the analysis you want to perform in just a few clicks.
- Simplified interpretation: Free up unnecessary programming time to concentrate on analysis, thanks to graphical tools and interpretation aids formatted in Excel, while benefiting from smooth automation.
- Richness of approaches/methods proposed: PCA, AFC, ACM, specific ACM, CAH, Kmeans Classification, Mixed Classification, CAH deployment by Mahalanobis distance, classical deployment or by a supervised algorithm
- Simplified deployment: Benefit from automation in batch or real-time mode, without code rewriting.
