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Discovery oriented social media research from descriptive to analytic (2011奢侈品panel分享)

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Discovery oriented social media research from descriptive to analytic (2011奢侈品panel分享)

  1. 1. Discovery-Oriented Social Media Research: from Descriptive to Analytic Deqiang Zou School of Management, Fudan University November 2, 2011
  2. 2. Why We Need to Know Research • From a professor? After all, we can get  Best practice from leading-edge business experts  Latest observations and insights from marketing research or consulting firms, and • Professors always have some theories  Unfortunately, I do not have any theories particularly for luxury goods • If no theories, maybe you can expand my horizon  By showing me what I don’t know 2 © Copy rights reserved, Zou Deqiang
  3. 3. Political Science Greg Miller (2011), “Social Scientists Wade into the Tweet Stream,” Science, 333 (6051), 1814‐1815. 3
  4. 4. The Global Mood: Psychologist 4 © Copy rights reserved, Zou Deqiang Miller (2011)
  5. 5. Discovery Orientation • This is not my job  Transmit the knowledge • I can share something with you  A discovery oriented, critical, and generalizable mindset, so that you’d be a  Qualified consumer or even producer of social media research • The key  A transformation: from descriptive to analytic 5 © Copy rights reserved, Zou Deqiang
  6. 6. Descriptive: Military Budget 6‐is‐beautiful‐military‐spending © Copy rights reserved, Zou Deqiang
  7. 7. Analytic: Who’s the Big Spender
  8. 8. Descriptive: Who has more soldiers? 8 © Copy rights reserved, Zou Deqiang
  9. 9. Analytic: We need a ratio
  10. 10. 11 GroupM and CIC (2011), “The Voice of Luxury: Social Media and Luxury Brands in China,” GroupM Knowledge ‐ CIC White Paper on Luxury, August.
  11. 11. So What? • Buzz volume vs. Sales volume? • NSR vs. Brand attitude in the market place? • We need marketing research to support marketing decision making  If X, then Y  Straightforward?  No, marketing is losing its clout. We cannot be over-optimistic  “Marketing has lost its seat at the (boardroom) table.” (from a CMO) V. Kumar and Denish Shah (2009), “Expanding the Role of Marketing: From Customer Equity to Market Capitalization,” Journal of Marketing, 73 (6), 119‐136. 12 © Copy rights reserved, Zou Deqiang
  12. 12. The Cruel Reality • The CMO is currently the most frequently fired C-level executive, with an average tenure of less than 24 months (Welch 2004) • A research based on a multi-industry sample of 167 firms finds that the CMO presence in top management teams has almost no impact on firm performance (Nath and Mahajan 2008) • There’s a perceived lack of marketing accountability, which has undermined marketing’s credibility, threatened marketing’s standing in the firm, and even threatened its existence as a distinct capability within the firm 13 © Copy rights reserved, Zou Deqiang Kumar and Shah (2009)
  13. 13. Establish the Accountable Relation • Possible reasons lie in the failure of the marketer to accurately prove his or her worth and/or the inability to relate marketing performance to reliable financial metrics (Lehmann 2004; Rust, Lemon, and Zeithaml 2004) • This entails relating marketing performance to a higher- level financial metric that is of concern to the CEO • CIC and its partners have taken initiatives in attaching a monetary label to each online buzz 14 © Copy rights reserved, Zou Deqiang Kumar and Shah (2009)
  14. 14. 15 R3 and CIC (2010), Internet Word of Mouth Proven to Have Impact on Auto Sales in China.
  15. 15. What is the Causality? R3 and CIC (2010)
  16. 16. Causality: Temporal Sequence Take the Teane for example, its positive IWOM led the way by one month © Copy rights reserved, Zou Deqiang for positive sales results R3 and CIC (2010)
  17. 17. Confounding? (I) 18 © Copy rights reserved, Zou Deqiang R3 and CIC (2010)
  18. 18. Confounding? (II) 19 © Copy rights reserved, Zou Deqiang R3 and CIC (2010)
  19. 19. Discovery: Three Components • Y= f (x) Why? How? So what? 20 © Copy rights reserved, Zou Deqiang
  20. 20. 21 Nielsonwire (2011), How Social Media Impacts Brand Marketing, Oct 14.
  21. 21. Diagnose: One by One • Why  Differentiated effect of positive vs. negative WOM  Other marketing efforts, e.g., pricing, channel, communication? • So what  Sales volume or incremental sales volume? • How  Clarify the causal relation, the underlying mechanism  WOM can be the consequences of other marketing efforts  Where is competition? 22 © Copy rights reserved, Zou Deqiang
  22. 22. Price Elasticity • The refrigerated juice own- and cross-elasticities a The percentage change in the sales of MinuteMaid with response to a 23 1% change in the price of Tropicana Dominique M. Hanssens, Leonard J. Parsons, and Randall L. Schultz (2003), Market Response Models: Econometric and Time Series Analysis (2nd Ed.), Springer.
  23. 23. Groupon’s Influence on Reputation (Sep 12, 2011) (Chinese) 24 Source:
  24. 24. Conceptual Framework of UGC and Stock Performance Third party reports Gerard J. Tellis and Seshadri Tirunillai (2011), “Does Chatter Really Matter? Validity and Meaningfulness of User‐Generated Content (UGC) for Brand Equity,” Marketing Scholar Forum (XI), Beijing: Peking University. © Copy rights reserved, Zou Deqiang
  25. 25. Summary of the Measures UGC Metrics Brand Performance • Chatter (volume) • Abnormal Returns • Ratings • Risk • Positive Chatter • Trading Volume • Negative Chatter • Competitor metrics (chatter, positive, negative) Tellis and Tirunillai (2011) © Copy rights reserved, Zou Deqiang
  26. 26. Effect of Chatter on Brand Performance Brand Performance Trading Returns Risk Volume Chatter +++ 0 +++ Negative Chatter ‐‐‐ ++ ‐‐‐ Cause Positive Chatter 0 0 0 Ratings 0 0 0 (Based on Granger Causality Test) Tellis and Tirunillai (2011) © Copy rights reserved, Zou Deqiang
  27. 27. Effects of UGC On Brand Equity Returns Immediate 5% Chatter Accumulated 15% Immediate 1% Ratings Accumulated 2% Immediate ‐3% Negative Chatter Accumulated ‐7% Immediate 1% Positive Chatter Accumulated 3% Tellis and Tirunillai (2011) © Copy rights reserved, Zou Deqiang
  28. 28. Effects of UGC On Competitor Equity Returns Chatter Immediate 5% Competitor Immediate -1% Chatter Accumulated -2% Competitor Immediate 2% Negative Chatter Accumulated 3% • Any opportunities for new research?  A missing link: online buzz the product market the financial market Tellis and Tirunillai (2011) © Copy rights reserved, Zou Deqiang
  29. 29. Online Consumer Review and Box Office Revenue • The volume, but not the valence, of online user reviews has a positive impact on box office revenues (Liu 2006) • The valence, but not the volume, of online user reviews has a positive impact on box office revenues (Chintagunta et al. 2010), exploiting the sequential release of movies across markets Pradeep K. Chintagunta, Shyam Gopinath, and Sriram Venkataraman(2010), “The Effects of Online User Reviews on Movie Mox Office Performance: Accounting for Sequential Rollout and Aggregation across Local Markets,” Marketing Science, 29(5), 944‐957. Yong Liu (2006), “Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue,” Journal of Marketing, 70(3), 74‐89. 30 © Copy rights reserved, Zou Deqiang
  30. 30. The $ Value of the Star Luca (2011) 31 © Copy rights reserved, Zou Deqiang
  31. 31. It Pays! • The impact of consumer reviews on the restaurant industry:  a one-star increase in Yelp rating leads to a 5-9 percent increase in revenue,  this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and  chain restaurants have declined in market share as Yelp penetration has increased Michael Luca (2011), “Reviews, Reputation, and Revenue: The Case of,” HBS Working Paper, September. 32 © Copy rights reserved, Zou Deqiang
  32. 32. 35 GroupM and CIC (2011)
  33. 33. Our Research Plan • Relate monthly car sales in China to CIC data  Buzz volume and valence at attribute level  Incorporate competition effects • Even more ambitious idea  Relate OCJ sales data to natural language analysis of its infomercial at attribute level • Because analysis at attribute level is highly informative and insightful  Analyze and visualize market structure by automatically eliciting product attributes and brand’s relative positions from online customer reviews (Lee and Bradlow 2011) 36 © Copy rights reserved, Zou Deqiang
  34. 34. 37 Lee and Bradlow (2011)
  35. 35. Thomas Y. Lee and Eric T. Bradlow (2011), “Automated Marketing Research Using Online 38 Customer Reviews,” Journal of Marketing Research, 48 (5), 881‐894.
  36. 36. Mapping the Market Using Customer Reviews Lee and Bradlow (2011) Technically sophisticated consumers: low‐light or ISO controls and lens characteristics (e.g., name brand optics such as Zeiss) Lay consumer would notice: easy‐to‐use menus and navigation 39 interfaces, the number of pictures available, and video capabilities
  37. 37. Evolution of Market Structure 40 Lee and Bradlow (2011)
  38. 38. Market Structure by Cons and Pros 41 © Copy rights reserved, Zou Deqiang Lee and Bradlow (2011)
  39. 39. 42 CIC, 新浪 (2011), “微博引领的中国社会化商业发展与变革,” CIC·新浪合作微博白皮书, 10月.
  40. 40. We Care About, More • Tweet diffusion model for Burberry 3D fashion show GroupM and CIC (2011) 43 © Copy rights reserved, Zou Deqiang
  41. 41. Descriptive Statistics GroupM and CIC (2011), “The Voice of Luxury: Social Media and Luxury Brands in China,” GroupM Knowledge ‐ CIC White Paper on Luxury, August. 44 © Copy rights reserved, Zou Deqiang
  42. 42. Scale and Speed • To profile the consequences of the tweet diffusion process  Scale: how many people are infected?  Speed: how fast are people infected? • Infection?  Yes, fashion is contagious.  Its diffusion is just like epidemics 45 © Copy rights reserved, Zou Deqiang
  43. 43. Can the Diffusion Process Be Predictive? • In recent years, scientists have improved disease surveillance systems that enable public health officials to follow the emergence and spread of infectious diseases  Most systems collect information from the entire population, such as how many people have visited the doctor for flulike symptoms and how many flu tests have been submitted to the health department  In recent flu seasons, the lag time—the gap between a person showing symptoms of an illness and that data being available to epidemiologists—has decreased to as little as 1 week • Although this improvement gives epidemiologists a better idea of how many people are ill, it doesn’t allow them to track an outbreak in real time or anticipate its spread‐network‐predicts‐flu‐spre.html 46 © Copy rights reserved, Zou Deqiang
  44. 44. Social Network Predicts Flu Spread • Researchers who tracked flu symptoms in the friends of a group of college students during the 2009 H1N1 "swine flu" pandemic predicted the flu outbreak in the general college population with at least 2 weeks' advance notice  Randomly chose 319 Harvard undergraduates, who then named 425 of their friends. Checked on the health of these 744 students between 1 Sep 2009, and 31 Dec 2009 using two different methods: a twice-weekly e-mail survey and the students' records at the campus health clinic  Students in the friend group showed signs of the flu between 14 and 69 days before the epidemic peaked in the control group of randomly selected undergraduates 47 © Copy rights reserved, Zou Deqiang
  45. 45. Segmentation: Friendship Paradox • Their predictions depend on a characteristic of social networks known as the friendship paradox, which states that your friends have more friends than you do  Although it seems that on average your friends should have the same number of friends as you do, a person named as a friend actually has more friends than you, because people named as friends tend to be more popular  They also tend to be better connected and more central to the social network • Previous research showed that well-connected people in a network caught infectious diseases before those with fewer connections 48 © Copy rights reserved, Zou Deqiang
  46. 46. Progression of flu contagion in the friendship network over time © Copy rights reserved, Zou Deqiang
  47. 47. Differences in Contagion Nicholas Christakis: How social networks predict epidemics (Sep 2010), 50 © Copy rights reserved, Zou Deqiang
  48. 48. Is it Marketing Relevant? • "What our method offers is the premise of predicting the future," Christakis says  To apply the strategy to the general population, he notes, all researchers would have to do is ask a randomly selected group of people to identify their friends and then track when these friends become ill. "Today, you can know where the epidemic will be in 2 weeks.“ • This lead time can give public health officials more time to develop an effective response to the outbreak • In the context of social media  We care about the “lead time”  How to define “a fiend”? 51 © Copy rights reserved, Zou Deqiang
  49. 49. The Long-term Downside of Overnight Success • Professor Jonah Berger, and Gael Le Mens tracked the popularity of first names over 100 years in France and the United States  The names that soar into popularity fastest, they discovered, also tend to fall out of favor more quickly.  "We often see products, ideas and behaviors catch on and spread like wildfire. New high-tech gadgets or YouTube videos go from unknown to amazingly popular," says Berger. "But we know less about why once-popular things become unpopular." 52 © Copy rights reserved, Zou Deqiang Berger and Le Mens (2009)
  50. 50. A few trajectories of first-name popularity Jonah Berger and Gael Le Mens (2009), “How Adoption Speed Affects the Abandonment of Cultural Tastes,” Proceedings of the National Academy of Sciences, 106, 8146‐8150. 53 © Copy rights reserved, Zou Deqiang
  51. 51. Bill Marsh and Alicia DeSantis (2009), “Quick Arriving Fads Quick to Flame Out,” The New 54 York Times, May 16.
  52. 52. 100 Years of Names • Names are also a good proxy for products and services that convey symbolic meaning about identity • Being the same, yet different  parents' attitudes toward naming their children reflect a fundamental tension between an individual's desire to conform and fit in with others, while at the same time preserving a distinctive identity • That’s how people buy luxury goods 55 © Copy rights reserved, Zou Deqiang
  53. 53. Social Preference in Conspicuous Consumption • Snobs U s ( z1e , p1 )   s v1  p1   ts  s z1e (1) • Conformists U c ( z1e , p1 )   c v1  p1   tc  c z1e (5) Wilfred Amaldoss and Sanjay Jain (2005), “Pricing of Conspicuous Goods: A Competitive 56 Analysis of Social Effects,” Journal of Marketing Research, 42 (February), 30‐42. © Copy rights reserved, Zou Deqiang
  54. 54. Settle the Conflicting Motives • Conflicting motives for similarity, identity-signaling, and uniqueness can be resolved at different product levels  People tend to choose options preferred by in-group members on dimensions linked to their social identities (e.g., brands), and this is driven by desires to be associated with those groups  Higher needs for uniqueness lead people to make differentiating choices among group associated options (i.e., select less popular products from in-group associated brands) • Evidence in social media?  Effects with regard to their WOM behavior? Cindy Chan, Jonah Berger, Leaf Van Boven (2011), Differentiating the “I” in “In‐Group”: How Identity‐Signaling and Uniqueness Motives Combine to Drive Consumer Choice, Journal of Consumer Research, Conditionally Accepted 57 © Copy rights reserved, Zou Deqiang
  55. 55. Two Types of WOM • A psychosocial cost associated with positive WOM:  positive WOM can decrease the uniqueness of one’s possessions, which hurts high-uniqueness individuals • High-uniqueness individuals are less willing to generate positive WOM for publicly consumed products that they own • They are as willing to discuss product details • How to figure them out  In the context of social media? Amar Cheema and Andrew M. Kaikati (2010), “The Effect of Need for Uniqueness on Word Of Mouth,” Journal of Marketing Research, 47 (3), 553‐563. 58 © Copy rights reserved, Zou Deqiang
  56. 56. Two Types of Online WOM? 59 © Copy rights reserved, Zou Deqiang GroupM and CIC (2011)
  57. 57. Conspicuous Consumption and Race • Blacks and Hispanics devote larger shares of their expenditure bundles to visible goods (clothing, jewelry, and cars) than do 60 comparable Whites Charles, Hurst, and Roussanov (2009)
  58. 58. Alternative Explanations • Inherent ethnic difference • Conspicuous consumption is used as a costly indicator of a household’s economic position  Status signaling: individuals derive utility from status, which depends on others’ beliefs about their income. Although income (or wealth) is not observed, visible consumption is  Visible consumption rise in own income, and fall in the income of the reference group— for each race group  An individual’s reference group is defined as persons of the individual’s race living in his state • Reference group in social media Kerwin Kofi Charles, Erik Hurst, and Nikolai Roussanov (2009), “Conspicuous Consumption and Race,” Quarterly Journal of Economics, 124 (2), 425‐467. 61 © Copy rights reserved, Zou Deqiang
  59. 59. Not everything that can be counted counts, and not everything that counts can be counted - Albert Einstein
  60. 60. Computers are useless. They can only give you answers. - Pablo Picasso
  61. 61. Discovery: Review • Y= f (x) Why? How? So what? 64 © Copy rights reserved, Zou Deqiang
  62. 62. Yuval Atsmon, Vinay Dixit, Glenn Leibowitz, and Cathy Wu (2011), “Understanding China’s Growing Love for Luxury,” McKinsey Consumer & Shopper Insights, McKinsey Insights China. 65 © Copy rights reserved, Zou Deqiang
  63. 63. Return on Marketing
  64. 64. Jon Iwata (2011), “From Stretched to Strengthened: Insights from the Global Chief Marketing Officer Study,” C‐Suite 67 © Copy rights reserved, Zou Deqiang Studies (CMO), IBM.
  65. 65. 68 © Copy rights reserved, Zou Deqiang