In this article, we will focus on customers and personalisation, and the different applications of Customer segmentation. In addition, listen to our podcast to know more about how we have approached segmentation and helped our clients with their business transformations.

Already covered B2B applications:

  • Identify Focus groups for business and marketing campaigns
  • Identify Loyal Customers
  • Build Customer/Consumer Personas

Use cases that explain how the personalization problem is solved:

  • About Spotify. How did they solve the personalization problem? – B2C
  • Collaborative Filtering
    • Implicit feedback by looking at song counts, timings and other features instead of user star ratings
  • NLP 
    • Scrape Blog posts on music in general
    • Associate adjectives with songs based on user feedbacks/comments
  • Raw audio modelling
    • Capture Time signature, key, mode, tempo, and loudness
    • Neural networks based model for similarity calculation for audios
    • Similarity of songs becomes a criteria for new recommendations

How the team at Coditation helped clients by working on their data science requirements transforming business by providing cutting-edge solutions for them

Industry Segmentation for a B2B company:

  1. Missing Industry ‘Parameter’ of a Customer (B2B)
  2. Input from the customer segments (based on industry and other parameters) to develop higher level models for: Cross Channel marketing, Budget allocation, Formulating  Campaign strategies, Up/X-sell
  3. Industry Segmentation using Google AdWords Data: 
    1. Searched keywords by customers
    2. Manual annotation
    3. Gensim/Word2Vec 

Customer Churn/Acquisition models:

  1. Industry segments as inputs, along with other usage, account and support ticket features for both churn and acquisition problems

Marketing Focus Group

  1. Identify Loyal Customer Segments for Review Collection Campaigns and Referral Programs  

Other Industries/businesses where coditation can help solve your problem

Our Team is working on various data science / ML problems 

  1. Video analytics 
  2. Audio analysis: Emotion detection
  3. NLP – Sentiment Analysis, Chatbots et. al.
  4. Deep Learning
  5. Data Gathering : Scraping

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