Social Learning and Consumer Demand

1. Social Learning and Consumer Demand Markus Mobius (Harvard University and NBER) Paul Niehaus (Harvard University) Tanya Rosenblat (Wesleyan University and IAS) CMPO, 2 June 2006

2. Motivation We want to study social learning in the context of how consumer preferences form. • How strong are social learning effects absolutely and relatively compared to informative advertising? • How strong are social influence effects (on valuations) absolutely and relatively compared to persuasive advertising? • Which agents are influential?

3. Learning Persuasion Strong Social Learning • Agents communicate directly about the product, sharing factual information: • “I didn’t buy it because it’s not Mac compatible” • “I’ve heard Sony makes the most reliable ones” • “They have a lot of vegetarian dishes on the menu”

4. Learning Persuasion Strong Social Learning Weak Social Learning Agents observe their friends’ consumption decisions and enjoyment of products and make inferences about the products’ attributes. “Greg got one for Christmas and I know he really liked it” These inferences should be sharper when friends know their friend’s preferences well.

5. Learning Persuasion Social Influence Strong Social Learning Weak Social Learning • Agents observe their friends’ consumption decisions and.... • Their private tastes are altered • The status value of consuming the product is altered

6. Learning Persuasion Social Influence Strong Social Learning Weak Social Learning Persuasive Advertising Informative Advertising Agents observe advertising for the product. They may learn about objective features of the product or be persuaded to like it or be persuaded of its prestige value.

7. Methodology: basic paradigm Stage 1: Measure the network (Harvard Undergraduates) Stage 2: Distribute actual products and track social learning

8. Methodology Measuring the Social Network

9. Measuring the Network • Rather than surveys, agents play in a trivia game • Leveraged popularity of www.thefacebook.com • Membership rate at Harvard College over 90% * • 95% weekly return rate * * Data provided by the founders of thefacebook.com

10. Markus • His Profile • (Ad Space) • His Friends

11. Trivia Game: Recruitment • On login, each Harvard undergraduate member of thefacebook.com saw an invitation to play in the trivia game. • Subjects agree to an informed consent form – now we can email them! • Subjects list 10 friends about whom they want to answer trivia questions. • This list of 10 people is what we’re interested in (not their performance in the trivia game)

13. Trivia Game: Trivia Questions • Subjects list 10 friends – this creates 10*N possible pairings. • Every night, new pairs are randomly selected by the computer • Example: Suppose Markus listed Tanya as one of his 10 friends, and that this pairing gets picked.

14. Trivia Game Example • Tanya (subject) gets an email asking her to log in and answer a question about herself • Tanya logs in and answers, “which of the following kinds of music do you prefer?”

16. Trivia Game Example (cont.) • Once Tanya has answered, Markus gets an email inviting him to log in and answer a question about one of his friends. • After logging in, Markus has 20 seconds to answer “which of the following kinds of music does Tanya prefer?”

18. Trivia Game Example (cont.) • If Markus’ answer is correct, he and Tanya are entered together into a nightly drawing to win a prize.

19. Trivia Game: Summary • Subjects have incentives to list the 10 people they are most likely to be able to answer trivia questions about • This is our (implicit) definition of a “friend” • This definition is suited for measuring social learning about products. • Answers to trivia questions are unimportant • ok if people game the answers as long as the people it’s easiest to game with are the same as those they know best. • Roommates were disallowed • 20 second time limit to answer • On average subjects got 50% of 4/5 answer multiple choice questions right – and many were easy

20. Recruitment • In addition to invitations on login, • Posters in all hallways • Workers in dining halls with laptops to step through signup • Personalized snail mail to all upper-class students • Article in The Crimson on first grand prize winner • Average acquisition cost per subject ~= $2.50

21. Network Data • 23,600 links from participants • 12,782 links between participants • 6,880 of these symmetric (3,440 coordinated friendships) • Similar to 2003 results • Construct the network using “or” link definition • 5576 out of 6389 undergraduates (87%) participated or were named • One giant cluster • Average path length between participants = 4.2 • Cluster coefficient for participants = 17% • Lower than 2003 results – because many named friends are in different houses

22. Number of Roommate links, friend (N1), indirect friend (N2), and friends of distance 3 (N3) for an average subject (OR network on all participants of trivia game)

23. Methods in Comparison • 2003 House Experiment in 2 undergraduate houses • Email-data: Sacerdote and Marmaris (QJE 2006) • Mutual-friend methods with facebook data? (Glaeser et al, QJE 2000)

24. Methodology Seeding Information

25. Seeding Information • Elicit subjects’ initial valuations • Center empirical estimates • Decompose valuations (hedonics) • Randomized treatments • Distribute product samples • Information / instructions • Randomized advertising • Print (Crimson) and online (thefacebook.com) • Informative and persuasive • Elicit subjects’ final valuations

26. Example • A hypothetical subject “Paul” might be exposed to the following treatments: • A friend of Paul’s of social distance 2 used a PDA • The friend was told about the PDA’s instant messenger capabilities • Paul saw an advertisement for the PDA in the newspaper that emphasized it’s hip-ness • Paul did not see online advertising for the PDA

27. Product Samples • We want new products to maximize the potential for social learning. • Want to vary products by • Likely demographic appeal • Potential for strong learning (need a manual?) • Potential for weak learning and social influence – the “buzz factor”

28. Durables T-Mobile Sidekick II Philips Key019 Digital Camcorder Philips ShoqBox

29. Perishables Student Advantage Discount Card Baptiste Studios Yoga Vouchers Qdoba Meal Vouchers

30. Step I: Elicit Valuations • We want to elicit valuations for a product without telling subjects what the product is. • Our solution: We treat a product as a vector of attributes which span a space containing the specific product. • We can elicit valuations for each attribute without revealing product.

31. Step I: Configurators • Familiar examples with posted menus of prices • many computer manufacturers (e.g. Dell) • some car manufacturers • Here, subjects bid for features • Baseline bid for “featureless” product • Incremental bids for distinct features

32. Constructed Bids • Subjects told that either this bid or their bid in the followup will be entered into a uniform-price auction with equal probability • Construction: • Incentives: bid as accurately as possible • Extension: interactions between features

33. Feature descriptions Feature bids Baseline bid

34. (Price) ($20) ($50) ($35) ($150) ($150) ($250)

35. Distributions of Imputed Bids • Results from configurators look sensible • In each case, market prices lie between median bid and upper tail • T-Mobile and Philips confirmed that demand curves for their products are similar to results from more traditional analysis

36. Step 2: Randomized Product Trials • Perishables • ½ year Student Advantage cards • 5 yoga vouchers • 5 meal vouchers • Durables • Try out for approximately 4 weeks during end of term

37. Randomization • Blocked by year of graduation, gender, and residential house • Email invitations to come pick up samples • Invitation times varied to vary strength of exposure (April 26th – May 3rd)

39. Info Treatments • Varied information communicated verbally by workers doing distribution • Information treatments correspond to product features in our configurators (5 or 6 features for each product). • Reinforced this information treatment with reminder emails • Each treatment given with 50% probability to each subject

41. “Buzz” Treatments • Product-specific treatments without information content • Intended to increase subject’s enjoyment of the product • Examples • Subway tokens for yoga, Qdoba • 5 free MP3s on ShoqBox • Extra pre-paid balance on Sidekicks • Special one-store subsidy on Student Advantage cards • Given with 50% probability to each subject

42. Step 2: Advertising Online Advertising • Delivered via thefacebook.com • Mixed in with normal paid advertising • 65% of subjects saw ads • 232,736 impressions (approx. 300 per treated subject) • 136 clicks (in line with averages)

43. Advertising Content • Content from sponsor companies • Tweaked to vary informational content in line with product features • Also non-informative versions

44. Step 2: Advertising Print Advertising • Inlets in The Crimson, Harvard’s student newspaper • One of nation’s largest student papers, daily readership approx. 14,000 • Delivered to undergrad students’ rooms • Inlets allow randomization across residential houses

47. All ads for a product has the same style and differed only in the informational content.

48. Print advertising • 4 inlets with two ads each. • 3 ads emphasizing a single feature of a product. • Residents in a house were exposed to either 2 or 3 impressions of the same print ad.

49. Step 4: Final Valuations • Subjects receive full product descriptions and submit a second round of bids, which go into the auctions with 50% probability • Subjects also… • Predict what the average bid will be • Predict what a sample of their friends will bid in the auction • Answer factual questions about each product • Indicate their confidence in these answers