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Customer Analytics, Segmentation and Churn study from Facebook data

In this blog, we will see how we can perform in-depth customer analytics using publicly available inputs from the customers on company Facebook pages.  For this showcase, we will focus on the media sector and more precisely on the RTL group  (leading TV & Radio on the French speaking side of Belgium).  We analyzed the behavior of people acting on the Facebook pages of the RTL group and aggregated all available information to perform per-user analytics and predictions.  We are reusing the techniques detailed in previously published blog posts on Facebook Mining and Sentiment Analysis.  The techniques described in this post can be very useful for all major B2C companies involved in the media, telecoms, retails sectors. ...

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Sentiment Analysis of French texts using deep learning techniques

In this blog, we will see how deep learning techniques (Recurrent Neural Network, RNN and/or Convolutional Neural Network, CNN) can be used to determine the sentiment polarity of a written text.  This is call "sentiment analysis" and it's very useful to enhanced the communication with your customers.  Such algorithms are typically used to analyze emails, website or even Facebook posts where your customers may talk about your products. Thanks to this, you can prioritize your answers and react faster to the unsatisfied customers... ...