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Academically-Practical and Practically-Academic Social Commerce Learnings in Interactive Media

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The world of practice and academia have never collided so positively and mightily as in the sector of interactive media. The presentation will focus on practically-academic and academically-practical …

The world of practice and academia have never collided so positively and mightily as in the sector of interactive media. The presentation will focus on practically-academic and academically-practical findings in interactive media and their implications for social commerce and social shopping. The intent is to encourage people to look towards data-oriented academics to assist in the understanding of this complex new media and develop practical applications from the data that arises from it.

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  • 1. Academically‐Practical and Practically‐Academic Learnings in Interactive Media Wharton Interactive Media Initiative Professor Eric T. Bradlow K.P. Chao Professor Professor of Marketing, Statistics, and Education  Vice‐Dean and Director, Wharton Doctoral Programs Co‐Director, Wharton Interactive Media Initiative  www.whartoninteractive.com
  • 2. T H E R E I S N O G R E AT D I V I D E ! Practice Academics Academically‐Practical Practically‐Academic
  • 3. H O W D O I K N O W W H AT A C A D E M I C S K N O W A N D H O W D O I K N O W W H AT P R A C T I T I O N E R S C A R E A B O U T ? • WIMI Corporate Partners  o Travel and Listen! • Matchmaking Webinars o Take Corporate Partner Business Problems and Present Them to  the Academic Community • My Own Academic Research • Academic Research Conferences and WIMI‐Funded Research
  • 4. W IM I’ S “ L E A R N I N G N ET W O R K” Global network of research partners Wharton Lab for Publishing WIMI Research, Innovation Student Placement Interns, Partners W H A R T O N I N T E R A C T I V E M E D I A I N I T I AT I V E
  • 5. MATCHMAKING WEBINARS + + =
  • 6. What is the #1 Problem Today for Internet Ad Publishers? NOBODY KNOWS HOW MUCH TO PAY THEM! ADVERTISING ATTRIBUTION * NOT LAST CLICK * NOT EQUALLY SPREAD WHARTON INTERACTIVE MEDIA INITIATIVE
  • 7. O R G A N I C TA C K L E S A D AT T R I B U T I O N ! ORGANIC DEVELOPED AND MANAGED A COMPLETE DIGITAL MARKETING  STRATEGY FOR THE CLIENT, A NEW CAR MANUFACTURER Display advertising on media sites Sponsored search Shopping sites Advertiser sites
  • 8. DIGITAL ADVERTISING “PATHS” FOR NEW CAR SHOPPERS (HYPOTHETICAL) DATA View Ad View Ad Edmunds.com CNN.com Day 1 View Ad View Ad View Ad Click‐through @  CNN.com KBB.com CNN.com CNN.com Day 6 Page view at advertiser  “Conversion” at  Click‐through @ Google site advertiser site Day 20 User 1 User 2 User 3
  • 9. D ATA AVAILABLE FIELDS Display advertising impressions For each activities at the advertisers site  (including conversions) • User • User • Date & time • Date and time • Advertiser organization (i.e., brand) • Type of activity • Media buy name • “Conversion” or “Success” activities • Site where ad was displayed (28 sites) • Search inventory • User’s country, state & area code (based on IP) • Find a dealer • Build & price • Get a quote • Other activities Click‐throughs • User’s state & area code (based on IP) • User • Whether the conversion occurred in the  • Date & time same session as a click‐through • Advertiser organization (i.e., brand • Media buy name • Site where ad was displayed • Ad id number                                          (no info on ad content) • User’s country & state code               KEY IS HAVING ALL THREE (based on IP) LINKED TOGETHER
  • 10. What Is The # 1 Problem Today In Search, From the Search  Firm’s Perspective?
  • 11. W H AT T O S H O W W H E N S O M E O N E S E A R C H E S ? WHARTON INTERACTIVE MEDIA INITIATIVE
  • 12. E X P E D I A TA K E S O N O P T I M A L S E A R C H R E S U LT S Retail  Opaque Package Corporate Media
  • 13. DATA ON 10,000K+ HOTEL SEARCHES CONDUCTED OCT 1-15, 2009 Free Text  Associated with  Search Region/Distinct Keyword  Assigned to that Text Travel Dates Number of  Number of  Rooms Travelers Time/Date of Search
  • 14. FOR EACH SEARCH WE OBSERVE WHICH HOTELS WERE DISPLAYED Number of  Hotels that Meet  Search Criteria Hotels Displayed Price Displayed  for Each Hotel Was the Price a  Promo?
  • 15. WE ALSO OBSERVE WHICH HOTELS WERE VIEWED AND PURCHASED Which Hotels  Got Click‐ Throughs? (if any) Which Hotels got  “Book It” Click‐ Throughs? Which Hotels  Were Purchased? 
  • 16. ERIC “THE WIZARD”: PREDICTING AND M O N E T I Z I N G F U T U R E B E H AV I O R W H A R T O N I N T E R A C T I V E M E D I A I N I T I AT I V E
  • 17. D E S C R I P T I O N O F D ATA • Contains 23,000 users of hulu.com who registered during February  2009. o Take 10% random sample • Tracking daily incidence of visiting to view videos for each of 120 days  starting March 1, 2009. • Summary Statistics of 90‐day in‐sample period: o Reach:  46% of people visit at least once o Frequency: 4.3 visits on average, among those who visit  o Streakiness:  446 total streaks of visits lasting 3 or more consecutive days (across all  people) • Last 30 days are the holdout (out‐of‐sample) period used for model  validation. 17
  • 18. EXAMPLE 1: MAKING MONEY FOR HULU ALIVE, THEN  DEAD ALIVE, THEN COLD ALIVE OR  “DEAD” ALIVE OR  COLD WINNER ‐> DON’T PAY TO BRING BACK FROM THE “DEAD” 18
  • 19. MAKING MONEY FOR MECOX LANE • A retailer (with catalogs, stores and a website) would like a tool to identify which  consumers active and which ones have ended their relationship with the firm  • The retailer provided transaction history across three channels (web, store & catalog) for  a random sample of 30,000 customers • Using this data, researchers at WIMI are developing a model that can be used to:  o Identify ‘inactive’ customers o Forecast future sales o Plan capacity o Understand multi‐channel behavior • Unlike many other forecasting  approaches does not require any  information about the consumer other  than her purchase history o Easily applied in many settings
  • 20. REMARKABLE PREDICTION ACCURACY Cummulative Orders • The model is based on the simple  Actual cummulative Forecast Cummulative idea that people buy at a steady rate  8000 until they become inactive 7000 o But different people have  6000 different rates 5000 • By using the data to estimate the  4000 rates at which people buy and  become inactive, we build a model  3000 that can forecast orders into the  2000 future 1000 • These models have proven accurate  0 across many industries and contexts  * All results preliminary
  • 21. INSIGHT INTO DIFFERENCES BETWEEN CHANNELS Beta Density for Dropout Rate • Even though we never observe when  a customer becomes inactive, the  Overall Catalog (Method=1) 1000.0 .com (method=8) Store (Method=M) model gives us an estimate of the  Proportion of Customers (Probability Density) 900.0 drop‐out rates 800.0 .com customers are  700.0 less likely to drop out  o Most .com customers have a  than others 600.0 very low drop‐out rate 500.0 Catalog customers are  o Most catalog customers have a  400.0 more likely to drop  much higher drop‐out rate out than others 300.0 o Store shoppers vary widely in in  200.0 their propensity to drop out 100.0 0.0 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 Daily Dropout Rate (!) * All results preliminary
  • 22. FO R ECAST ING M ULTI ‐ C H A NNEL  M E D I A  CO N S U M PT ION D U R I N G T H E   WO R LD C U P Elea McDonnell Feit Pengyuan Wang Eric T. Bradlow Peter S. Fader Wharton Interactive Media Initiative
  • 23. CONTEXT
  • 24. ESPN OBJECTIVE FOR WIMI Build a state‐of‐the‐art predictive model to  understand and project "multichannel"  consumption habits across digital properties  (Internet, Mobile & Streaming Video)
  • 25. M U LT I - C H A N N E L T O U R N A M E N T F O R E C A S T I N G • The Wharton Interactive Media Initiative developed a state‐of‐the‐art predictive  modeling method to understand and project multi‐channel consumption habits across  media platforms (web, video and mobile). • We tested this model using usage data for individual fans across three channels and were  able to make accurate forecasts, measure the relationships between channels, and  estimate the media attractiveness of individual teams. Soccer Reach for ESPN Digital Properties During World Cup .com U U U U (number of registered fans visiting daily)  Video S S S S Mobile Soccer Reach 6/4 6/5 6/6 6/7 6/8 6/9 6/10 6/11 6/12 6/13 6/14 6/15 6/16 6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/24 6/25 6/26 6/27 6/28 6/29 6/30 7/1 7/2 7/3 7/4 7/5 7/6 7/7 7/8 7/9 7/10 7/11 Day
  • 26. FINDINGS Daily Soccer Reach Forecast for ESPN.com  • Forecasting during World Cup (Number of registered fans visiting daily  Predicted Actual o By summing up predictions for  100 individual fans, we make accurate  per 1,000 fans) forecasts of overall reach for each  80 Reach channel. 60 • Multi‐channel behavior 40 20 o Fans are less likely to use ESPN.com  0 on weekends, but Mobile usage is  6/4 6/6 6/8 6/10 6/12 6/14 6/16 6/18 6/20 6/22 6/24 6/26 6/28 6/30 7/2 7/4 7/6 7/8 7/10 unaffected by weekends. o Among those who use mobile and  Cumulative Soccer Frequency for ESPN.com  .com, the more a fan uses Mobile, the  during World Cup 2000 less he uses ESPN.com. (Total visits for 1000 registered fans) 1800 • Team strengths Cumulative Frequency 1600 1400 o The method we have developed can  1200 be used to estimate the media  1000 800 attractiveness of individual teams.  600 400 200 0 6/4 6/11 6/18 6/25 7/2 7/9
  • 27. A C A D E M I C A L LY P R A C T I C A L I N T E R A C T I V E M E D I A 2009-2010 Cross‐Platform Data: Dec 2010 Social Networking: Jan 2009 Impact and Emergence of UGC:  Dec 2009 Mobile Marketing: 2011
  • 28. Mine Your Own Business Market Structure Surveillance through Text Mining Feldman, Goldenberg, Netzer Is “classic” Marketing  Research dead? Use analytics to explore the  relationship between brands Perceptual Map of US Car Makes Text mine consumer posts compact sport old Audi A6 67 345 56 Honda 1384 539 245 Civic Toyota 451 128 211 Corolla Customers are telling us things for “free” 
  • 29. Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance Tirunillai and Tellis Short‐term Long‐term  effect on  effect on  stock  stock  returns returns Chatter 3.8 4.8 Consumer  ‐2.1 ‐3.6 Opinion Negative  ‐2.9 ‐3.9 Chatter Negative  ‐3.7 ‐4.7 Expressions What your customers are saying matters (if you own stock) “You can take UGC to the Bank”
  • 30. Crowdsourcing New Product Ideas Bayus “The goal is for you, the customer, is to tell Dell what new products or  services you’d like to see Dell develop.” Prior Experience Relationship to Future  Daily: Feb 2007 – Feb 2009 Performance 7,100+ ideas 4,300+ ideators # prior good ideas not significant 170 ideas implemented # prior reviewed ideas not significant # prior ideas not significant # prior comments not significant The value of the crowd is in the “crowd”
  • 31. Modeling Connectivity in Online Networks Ansari, Koenigsberg & Stahl • Social network data helps to improve predictions of behavior above  and beyond just behavior + > • More popular social networkers are also more active • Online popularity is a more important correlate of online behavior than  offline > Knowing a customers social graph helps predict their purchases
  • 32. Econometric Modeling of Social Interactions Hartmann Michael  Michael’s  Promote to  goes golfing  friends golf  Michael more more Fraction of customer’s value that derives from others in the group Direct Value Indirect Value 65% 35% Consumers bring additional value through their community
  • 33. Opinion Leadership and Social Contagion in New Product Diffusion Iyengar, Van den Bulte, Valente Target social influencers Physician most often nominated by his  peers as influential is targeted and is  persuaded to increase his/her  prescription by 10 units  vs. Across the board promotion Each physician is given an additional detailer visit Influencers work, but slowly and “locally”
  • 34. Pricing Digital Content Iyengar, Abhishek, & Bradlow Popularity begets popularity; but  how do you get it? But, free is free! Freemium works!
  • 35. The Future: Data Minimization www.whartoninteractive.com
  • 36. L O N G - S TA N D I N G I T C H A L L E N G E 37
  • 37. T O O - M U C H - D ATA P R O B L E M 38
  • 38. D ATA P R I VA C Y I S S U E S 39
  • 39. S O L U T I O N : K E E P W H AT I S N E E D E D , F I T W H AT I S T H E R E
  • 40. SUMMARY • It is all about the data! o In many cases, practitioners have it – academics want it. o Scraping programs mean we can now all have it and in real‐time. • Convergence of problems between academia and practice, in the  interactive media space, has never been higher. o Advances still need to be made on scale of academic methods. • Let’s look for the next great divide!   It demonstrates an opportunity for  further study.
  • 41. W H A RTO N I N T E R A C T I V E M E D I A I N I T I AT I V E Eric T. Bradlow ebradlow@wharton.upenn.edu www.whartoninteractive.com W H A R T O N I N T E R A C T I V E M E D I A I N I T I AT I V E

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