sarthak7295 / recommender_segmentation

A Recommender for Retail Business using Consumer Segmentation with Clustering Techniques

Customer segmentation

Retail marketers are constantly looking for ways to improve the effectiveness of their campaigns. One way to do this is to target customers with offers that are more likely to attract them back to the store and to spend more time and money on their next visit. Demographic market segmentation is an approach to segmenting markets. A company divides the larger market into groups based on several defined criteria. Age, gender, marital status, occupation, education and income are among the commonly considered demographics segmentation criteria. We propose a study wherein we would try to help online retail business by better understand its customers and therefore conducting customer-centric marketing more effectively. Based on Recency, Frequency, and Monetary model, customers of the business have been segmented into various meaningful groups using the different clustering algorithm. Furthermore, using this knowledge we would build a better recommender system personalized to different segment of customers.

Design architecture

How to Run?

• clone the git repo

• how the structure of dash board should look like after extracting dataset

• extract the datatset from dataset.zip file

• To install dependencies run "pip install -r requirements.txt"

• In the terminal type "python app.py" to run the flask applicaiton

• The segmentation analysis can be run using jupyter notebook

Dashboard