[livecast] Personalization on the Web


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  • Does not do justice to ur hairstyle
  • 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
  • Amazon had a 28% lift in sales from its use of recommenders, driving almost $3B in revenue
  • Party a lot; gambling problem; oakland raiders fan; fb fan. Given the options, I choose fb.
  • 10M subscribers 100K titles
  • [livecast] Personalization on the Web

    1. 1. Personalization on the Web
    2. 2. Format <ul><li>90 minute lecture </li></ul><ul><ul><li>Interactive with Q&A time every 15-20 minutes </li></ul></ul><ul><li>Best viewing experience: enabletech blog </li></ul><ul><ul><li>http://opim.wharton.upenn.edu/enabletech/ </li></ul></ul><ul><ul><li>chat functionality to interact </li></ul></ul>
    3. 3. Goals <ul><li>Provide a brief overview of personalization technologies on the web </li></ul><ul><li>Develop an understanding of the marketing capabilities built on the personalization tools and technologies </li></ul>
    4. 4. 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 <ul><li>Offer matching </li></ul><ul><li>Cross-Selling </li></ul>Internal Sites <ul><ul><li>Display ads </li></ul></ul><ul><ul><li>Affiliates </li></ul></ul><ul><ul><li>Search Engine Marketing </li></ul></ul>External Sites
    5. 5. Scenario: Amazon’s Website <ul><li>A user is on our website: What can we learn? </li></ul><ul><ul><li>Where is the user physically located? </li></ul></ul><ul><ul><li>Is the user a customer? </li></ul></ul><ul><ul><li>What products has she bought before? </li></ul></ul><ul><ul><li>What offers/products should be displayed? </li></ul></ul>
    6. 6. Scenario Specifics <ul><li>Ailian Gan is browsing the amazon.com website. </li></ul><ul><ul><ul><li>She is a very high wallet prospect, living in New York, NY. </li></ul></ul></ul><ul><ul><ul><li>She has an account with Amazon and keeps a “wish list” of things in which she is interested. </li></ul></ul></ul><ul><ul><ul><li>Last weekend, she evaluated “Art: A modern history”, but did not buy it. </li></ul></ul></ul>
    7. 7. Customizing the Landing Page Without any information about Ailian, Amazon delivers generic product offers.
    8. 8. Physical Location IP Address mapping can be used to determine where Ailian is located. (See Quova)
    9. 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. 10. What Does Ailian Like? With cookies, Amazon can offer specific products tailored to Ailian based on her past behavior.
    11. 11. What Else Might Ailian Like? <ul><li>With recommender systems, Amazon is able to leverage other users’ actions to offer specific products tailored to Ailian </li></ul><ul><ul><li>Amazon attempts to guess what product a customer might like even if she has never attempted to view the product </li></ul></ul><ul><ul><li>Recommender Systems </li></ul></ul><ul><ul><ul><li>Use data on purchases, product ratings, and user profiles to predict which products are best suited to a particular customer </li></ul></ul></ul><ul><ul><ul><li>Most common design is a collaborative filter </li></ul></ul></ul><ul><ul><ul><ul><li>“ Customers who bought this item also bought….” </li></ul></ul></ul></ul><ul><ul><ul><ul><li>“ People like you bought…” </li></ul></ul></ul></ul>
    12. 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. 13. Personalized Retail <ul><ul><li>28-35% of sales originate from recommendations at Amazon </li></ul></ul><ul><ul><li>60% origination of Netflix rentals </li></ul></ul>
    14. 14. Personalized Radio
    15. 15. Personalized News <ul><ul><li>38% increase in usage at Google News </li></ul></ul>
    16. 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. 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
    18. 18. Cinematch Engine User Ratings 100s Million 100,000+ Movies Dataset Correlation Engine vs. High Correlation Recommendations <ul><li>Encourage users to rate movies </li></ul><ul><li>Determine correlations in user ratings to identify similar users </li></ul><ul><li>Recommend movies based on evaluations of similar people </li></ul>Cinematch uses statistical techniques to identify similar users and recommend movies based on ratings of similar users.
    19. 19. Netflix Challenge Netflix Dataset 480,000 Anonymous Customers X 18,000 Movie Titles 100 Million Ratings Date Oct 98 – Dec 05 (7 yrs) <ul><li>Date of Review </li></ul><ul><li>Title </li></ul><ul><li>Year of Release </li></ul>http://www.netflixprize.com/leaderboard
    20. 20. Recommender Designs <ul><li>Content-based Recommenders: create metadata about content & measure content similarity </li></ul><ul><li>Collaborative Filtering: use data from other users </li></ul><ul><ul><li>Item-to-Item CF: find items similar to given item </li></ul></ul><ul><ul><li>User-similarity based CF: find users similar to given user </li></ul></ul><ul><li>Social Network based Recommenders </li></ul><ul><li>Hybrid Systems </li></ul>
    21. 21. Recommender Designs <ul><li>Key design trade-offs </li></ul><ul><ul><li>Sparse data </li></ul></ul><ul><ul><li>Cold-start: New users and new items? </li></ul></ul><ul><ul><li>Relevance is not the main goal </li></ul></ul><ul><ul><ul><li>Discovery </li></ul></ul></ul>I1 I2 I3 I4 I5 I6 I7 I8 I9 U1 - - - - 3 - - - - U2 - - - - - - - - 4
    22. 22. Recommenders and Sales Concentration <ul><li>The “Long Tail” concept </li></ul><ul><li>Drivers? </li></ul><ul><ul><li>Supply side: </li></ul></ul><ul><ul><li>Demand side: </li></ul></ul><ul><li>Implications for firms? </li></ul><ul><li>Do recommenders really have an impact? </li></ul>Source: Longtail.com
    23. 23. Impact of Recommenders <ul><li>How might recommenders affect sales concentration? </li></ul>
    24. 24. An Empirical Investigation <ul><li>How might recommenders affect sales volume & diversity? </li></ul><ul><li>Recommender design: primarily content-based </li></ul><ul><li>Plugin for iTunes (and Facebook App) </li></ul>
    25. 25. Purchase Volume for Active Users <ul><li>Songs added by user (median) </li></ul>
    26. 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
    27. 27. Summary of Website Interactions <ul><li>Smart design of landing pages can: </li></ul><ul><ul><li>Convert browsers to buyers </li></ul></ul><ul><ul><li>Increase time spent on website </li></ul></ul><ul><ul><ul><li>Upsell </li></ul></ul></ul><ul><ul><ul><li>Cross-sell </li></ul></ul></ul><ul><ul><li>Increase loyalty </li></ul></ul><ul><ul><li>Improve RoI from advertising </li></ul></ul>
    28. 28. Internet Marketing Recap <ul><li>Internet presence management entails: </li></ul><ul><ul><li>Advertising: The online channel offers unique characteristics </li></ul></ul><ul><ul><ul><li>Customizable </li></ul></ul></ul><ul><ul><ul><li>Measurable </li></ul></ul></ul><ul><ul><li>Landing page design: Engaging customer interest online requires </li></ul></ul><ul><ul><ul><li>Designing the right interfaces </li></ul></ul></ul><ul><ul><ul><li>Customizing the look and feel: IP address, cookies, clickstream data, recommender systems </li></ul></ul></ul>
    29. 29. Appendix
    30. 30. Purchase volume for active users