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Click Stream Analysis

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Click Stream Analysis

  1. 1. 1 Bisen Vikrantsingh Kodamasimham Pridhvi Vaibhav Singh Rajput
  2. 2.  Intro  Dataset  Analysis  Approach 2
  3. 3.  The goal  To design models  To support web-site personalization and  To improve the profitability of the site by increasing customer response. 3
  4. 4. SET - I SET - II  Session, cookies  Session  ID, date, time  Customer  ID, visit count, gender, age, martial status,Income, occupation, home market value , howDidYouHearAboutUs, HowDidYouFindUs, U.S. state  Page View  View count of 80+ different pages/product, last page view, assortment path with level  ID, date, time  Customer  ID, visit count, gender, age, martial status,Income, occupation, home market value , howDidYouHearAboutUs, HowDidYouFindUs, U.S. state  Order  Date,time, amount, tax, discount, shipping amount, promotion code 4
  5. 5.  Questions - When given a set of page views,  will the visitor view another page on the site or leave?  which product brand will the visitor view in the remainder of the session?  characterize heavy spenders  characterize killer pages 5
  6. 6.  Stage-I  Data cleaning Technology Stack: • Python • MYSQL  Filtering attributes  Load into RDBMS  Stage-II  Classification of users  RFV analysis  Classify users into potential or not  Decision tree algorithm  Clustering of products  Dimension to be consider Product, age group, location, purchase amount  Fuzzy clustering  Find correlation  Correlation between {advertise,gender,income,brand} and {product view/purchase}  Stage-III  Answer question mention in previous slides 6
  7. 7. 7

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