Customer Segmentation Recommendation

About

Customer Segmentation Recommendation is a Machine Learning based project which analyzes user data and preferences to provide product recommendations on a micro-segment level and also helps the marketing team in making better marketing-business strategy by analysing its customers, based on their buying behaviour.

Micro segmentation is the process of dividing a market of potential customers into groups, or segments, based on different characteristics.

Types of Market Segmentation:

• Demographic segmentation( age, gender, income, etc.)
• Psychographic segmentation( interests, lifestyle, etc.)
• Behavioral segmentation( purchase & spend habits, brand interactions, etc.)
• Geographic segmentation( city, country, etc.)

Based on data and after analysing, users were divided into various categories such as

• Tech (Tech Savy, Luxuorios, Flagship Killers, Others..)
• Brand Lovers (Apple, Samsung, Huawei, Others..)
• Camera Lovers (Sensible, PhotoPhile, SelfieLover)

Technical Architecture

Technology Stack

• Web Frontend developed using Html/css and Vanilla JavaScript

• Backend developed using Flask

• Machine Learning Classfication and Regression Algorithms, Neural Networks

• Models trained by scrapping data from the Internet

• Dataset comprises of users previous purchase history, product specifications, and user data on a micro-segment.

• • Data stored in excel

Features

Marketing Head

• Analyse Buying Behavior of customers based on various paramters and divide them into various categories.
• Make better marketing strategy based on customers like/dislike for a particular product.
• Analyse a product sale and take actions to increase it.
• Predict the performance of their product based on its specification(in this case mobile phones).
• Target a specific audience based on product performance.
• Predict the audience that should be targeted on the given product.
• Recommend similar products to customers.

Screenshots

Dashboard (Analytics)

Prediction (Performance and User Category)

Recommendations

Configure Project

• Install python on your machine python ver 3.5.9 link to download : python.org/downloads/

• After successful installation of python, install following python libraries in your machine

   Package               Version
   --------------------- --------
  
   matplotlib            3.1.2
   missingno             0.4.2
   numpy                 1.18.1
   pandas                1.0.0
   sklearn               0.0
   tensorflow 2.0        1.15
   keras                 2.3.0
   seaborn               0.10.0
   flask                 1.1.1
   flask-Cors            3.0.8
  
   How to install?
   Open command prompt as administrator and type command pip install 'library-name'(For Windows)
  

• After successful installation of following libraries,navigate to the "DBS-Segmentation" folder where your project is kept,Open the folder and run "server.py" file Running this file will start the project server

• After successfully running "server.py" file, go to "frontend folder ->html folder ->dbshome.html",run "dbshome.html" file,thus the project will be setup successfully

• Inorder to have better understanding of code,install Anaconda-Navigator after that open jupyter-notebooks and open this folder "python-notebooks" on jupyter notebook and run files accordingly in that folder