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Personalization on the Web
Format ,[object Object],[object Object],[object Object],[object Object],[object Object]
Goals ,[object Object],[object Object]
The Online Channel The online channel can be segmented into interaction on external websites and on our own website. Scenario 1:  A user is on a partner website Scenario 2:  A user is accessing a search engine Scenario 3:  A user is on our website ,[object Object],[object Object],Internal Sites ,[object Object],[object Object],[object Object],External Sites
Scenario: Amazon’s Website ,[object Object],[object Object],[object Object],[object Object],[object Object]
Scenario Specifics ,[object Object],[object Object],[object Object],[object Object]
Customizing the Landing Page Without any information about Ailian, Amazon delivers generic product offers.
Physical Location IP Address mapping can be used to determine where Ailian is located.  (See Quova)
IP Address Mapping Stubhub can use IP addresses to offer ads targeted to a user’s location. Banner ads based on a New York location
What Does Ailian Like? With cookies, Amazon can offer specific products tailored to Ailian based on her past behavior.
What Else Might Ailian Like? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why are Recommenders Important? Used by major Internet firms (Amazon, NetFlix, Yahoo!) Recommenders can be beneficial to both consumers and firms.  Increasing Supply-side Offerings Value to  Consumers: •  Learn about new products  • Sort through myriad choices  Value to  Firms: •  Convert browser to buyers  • Cross-sell   • Increase loyalty Online Offline Books CDs DVDs 3,000,000 250,000 18,000 70,000 10,000 1,000
Personalized Retail ,[object Object],[object Object]
Personalized Radio
Personalized News ,[object Object]
The Amazon Approach Amazon uses all available information about customers in order to present the most relevant offer possible. 2 A bundle is created based on the look-alikes Customer views an item Amazon immediately identifies his profile and recent history, the product attributes and the behavior of similar customers 1 3 Additional items are proposed, based on  other  customers’ buying behavior
The Netflix Approach Utilizing customer information, Netflix is able to improve profits through its recommendation engine.  The customer is asked to rate movies  Movie ratings are used by Netflix to build the customer “persona” 1 2 Based on the “ persona ”, a group of movies the Customer will probably like is created 3 A movie is then recommended Based on the ratings provided by customer with a similar “persona” 4
Cinematch Engine User Ratings 100s Million 100,000+ Movies Dataset Correlation Engine vs. High Correlation Recommendations ,[object Object],[object Object],[object Object],Cinematch uses statistical techniques to identify similar users and recommend movies based on ratings of similar users.
Netflix Challenge Netflix Dataset 480,000 Anonymous Customers X 18,000 Movie Titles 100 Million Ratings Date Oct 98 – Dec 05 (7 yrs) ,[object Object],[object Object],[object Object],http://www.netflixprize.com/leaderboard
Recommender Designs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommender Designs ,[object Object],[object Object],[object Object],[object Object],[object Object],I1 I2 I3 I4 I5 I6 I7 I8 I9 U1 - - - - 3 - - - - U2 - - - - - - - - 4
Recommenders and Sales Concentration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Source: Longtail.com
Impact of Recommenders ,[object Object]
An Empirical Investigation ,[object Object],[object Object],[object Object]
Purchase Volume for Active Users ,[object Object]
Sales Diversity O i http://papers.ssrn.com/sol3/papers.cfm?abstract_id=955984 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1321962   Product Consumer User  k User  j User  i User  l GINI Unique artists Before After Diff Before After Diff 0.9094 0.8781 -0.0312 10377.8 15033.22 4655.47 0.9069 0.9098 0.0029 10940 10240 -700
Summary of Website Interactions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Internet Marketing Recap ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Appendix
Purchase volume for active users

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[livecast] Personalization on the Web

  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. Customizing the Landing Page Without any information about Ailian, Amazon delivers generic product offers.
  • 8. Physical Location IP Address mapping can be used to determine where Ailian is located. (See Quova)
  • 9. IP Address Mapping Stubhub can use IP addresses to offer ads targeted to a user’s location. Banner ads based on a New York location
  • 10. What Does Ailian Like? With cookies, Amazon can offer specific products tailored to Ailian based on her past behavior.
  • 11.
  • 12. Why are Recommenders Important? Used by major Internet firms (Amazon, NetFlix, Yahoo!) Recommenders can be beneficial to both consumers and firms. Increasing Supply-side Offerings Value to Consumers: • Learn about new products • Sort through myriad choices Value to Firms: • Convert browser to buyers • Cross-sell • Increase loyalty Online Offline Books CDs DVDs 3,000,000 250,000 18,000 70,000 10,000 1,000
  • 13.
  • 15.
  • 16. The Amazon Approach Amazon uses all available information about customers in order to present the most relevant offer possible. 2 A bundle is created based on the look-alikes Customer views an item Amazon immediately identifies his profile and recent history, the product attributes and the behavior of similar customers 1 3 Additional items are proposed, based on other customers’ buying behavior
  • 17. The Netflix Approach Utilizing customer information, Netflix is able to improve profits through its recommendation engine. The customer is asked to rate movies Movie ratings are used by Netflix to build the customer “persona” 1 2 Based on the “ persona ”, a group of movies the Customer will probably like is created 3 A movie is then recommended Based on the ratings provided by customer with a similar “persona” 4
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  • 26. Sales Diversity O i http://papers.ssrn.com/sol3/papers.cfm?abstract_id=955984 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1321962 Product Consumer User k User j User i User l GINI Unique artists Before After Diff Before After Diff 0.9094 0.8781 -0.0312 10377.8 15033.22 4655.47 0.9069 0.9098 0.0029 10940 10240 -700
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  • 30. Purchase volume for active users

Notes de l'éditeur

  1. Does not do justice to ur hairstyle
  2. primary source for IP address data is the regional Internet registries which allocate and distribute IP; Data contributed by internet service providers ; network tests; statistical analysis
  3. Amazon had a 28% lift in sales from its use of recommenders, driving almost $3B in revenue
  4. Party a lot; gambling problem; oakland raiders fan; fb fan. Given the options, I choose fb.
  5. 10M subscribers 100K titles