Today, machine learning (ML) offers a powerful solution to address this challenge
Posted: Thu Feb 13, 2025 5:42 am
Precisely identifying different consumer communities is one of the first use cases that can be achieved with machine learning. The tool offers you, from your data, to obtain a fine segmentation of your prospects or customers according to groupings that you would have had difficulty identifying manually (see the example of Salomon in this article ). Clustering algorithms analyze behavioral, social and demographic data to group individuals who share common characteristics: interests (sport, music, lifestyle), purchasing habits (frequency, type of product, average basket), etc. This makes it possible to detect specific communities that are difficult to identify with traditional methods.
You will tell me that to do ML, you need data. This is where having a real qatar telegram data customer experience platform (DXP) integrating a Customer Data Platform (CDP) becomes essential. I invite you to reread the CDP chapter of this article . In summary, the CDP offers a 360° customer view allowing a global understanding of the customer.
CDP + ML = analysis of behaviors, preferences, interactions to identify different communities or tribes sharing common values or interests.
You will tell me that to do ML, you need data. This is where having a real qatar telegram data customer experience platform (DXP) integrating a Customer Data Platform (CDP) becomes essential. I invite you to reread the CDP chapter of this article . In summary, the CDP offers a 360° customer view allowing a global understanding of the customer.
CDP + ML = analysis of behaviors, preferences, interactions to identify different communities or tribes sharing common values or interests.