How is the Mobile Internet Different? Search Costs and Local Activity

WIMI-MSICross-platform and Multi-channel Customer Behavior Conference Search Costs and Local Activities in the Mobile Internet: A Comparison of Micro-blogging Click-through Responses on Mobile Phones and PCs Anindya Ghose*, Avi Goldfarb**, and Sang-Pil Han* *New York University **University of Toronto

Mobile Internet Revolution• Mobile Internet and E-commerce

Search CostsAmazon in Mobile PhonesAmazon in PCs

Cost-Per-Click Bidding in PC Settings Sponsored LinksAdvertisers bid on keywords to locate their ads near the top ofthe list in PC settings.

Cost-Per-Click Bidding in Mobile Phone Settings Sponsored Link Advertisers may bid higher on keywords to locate their ads near the top of the list in mobile phone settings as compared to in PC settings.

Local Search for Coffee Shops Near You• Google Maps/Places using a Mobile Phone You Are Here! You Are Here! You Are Here! Advertisers may need to rank their ads higher for nearby customers browsing on their mobile phones

Research Questions• How search costs vary across Internet access technologies: mobile phones and PCs?• How local activities affect user behaviors in mobile Internet as compared to in PC-based Internet?

Search Costs• Online Search Costs – Reduced search costs in the online environment (Bakos 1997, Shapiro and Varian 1998) – Consequences on prices, price dispersion, product variety, unemployment (Lynch and Ariely 2000, Autor 2001, Brynjolfsson et al 2009, etc)

Search Costs• Online Search Costs – Reduced search costs in the online environment (Bakos 1997, Shapiro and Varian 1998) – Consequences on prices, price dispersion, product variety, unemployment (Lynch and Ariely 2000, Autor 2001, Brynjolfsson et al 2009, etc)• Importance of Position (rank) of Content in Different Online Media – Email (Ansari and Mela 2003) – Search engines (Ghose and Yang 2009) – Shopping bots (Baye et al. 2009, Brynjolfsson et al. 2009) But we do not know the relative importance of search costs in mobile devices as compared to those in PCs.

Geography• Geographic Proximity in Electronic Commerce – Impact of physical proximity between people on demand diffusion (Yang and Allenby 2003, Bell and Song 2007, Choi et al. 2010) – Impact of offline store proximity on online purchase propensity (Forman et al. 2009, Anderson et al. 2010) No prior work has empirically examined the relative impact of geographical proximity on user click throughs in mobile devices as compared to in PCs.

Other Related Prior Work• Economics of User-Generated Content – Reputation systems, online reviews, stock market discussion forums (Pavlou and Dimoka 2006, Ghose and Ipeirotis 2010, Das and Chen 2007) – Mobile multimedia content generated by users (Ghose and Han 2009)• Impact of Online Advertisement – Impact of exposure on brand awareness, brand attitudes, and purchase intentions (Gallagher et al. 2001, Drèze and Hussherr 2003) – Increased site visits (Sherman and Deighton 2001, Ilfeld and Winer 2002) – Actual purchase behavior in search keyword advertising (Rutz and Bucklin 2007, Ghose and Yang 2009, Yang and Ghose 2010) We investigate the impact of user-generated brand posts on user click decisions.

Data Description• 8,896 User-Level Clickthrough Data• 260 Users and 3,396 Distinct Brands – Examples: Starbucks, Jamba Juice, McDonalds, etc. – 17 brand categories (Computer Games, Restaurants, Books, etc.)• Access Channels – Mobile phones and PCs• Geographic Information of Users and Brand Stores – Euclidian distance between a user and brand stores

Brand CategoriesCOMPUTER GAMESGAME RESTAURANTS RESTAURANTBOOKSBOOK MUSICIT/INTERNET MULTIMEIDA MULTIMDEDIASHOPPING EDUCATIONCAFE/DESSERT ART/PERFORMANCEHOTEL/TRIP PUBLIC OFFICEOTHERS HEALTH/MEDICAL SVCLEISURE/SPORTS LIVING/HOMENIGHT CULTURE/BARS

Flows of Brand Posts in Our Empirical Setting User B User A Brand 1

Descriptive Statistics Variable Mean Std. dev. Min MaxBrand-LevelBrand profile tenure (days) 274 159 1 501Post tenure (days) 8.380 14.269 0 97.1Physical brand store rate 0.865 0.342 0 1User-LevelAge 24.987 11.818 0 54Sex (Male =1, Female = 0) 0.769 0.422 0 1Number of followees (those one follows) 10.414 30.609 0 373.6Number of bookmarks 15.711 56.946 0 350Mobile phone access rate 0.073 0.242 0 1Location revelation rate 0.813 0.391 0 1User- and Individual-LevelRank of brand post 39.107 26.859 1 90Distance between a user and a brand store (mile) 43.181 110.304 0.1 730.9Click-through rate on brand posts 0.030 0.171 0 1

Conceptual Framework• Rank measures search costs.• Distance measures local activities.• Click-through (CTR) measures economic behaviors. Rank CTR Distance Mobile

Endogeneity Issue in Rank• In our empirical setting, the rank is determined independent of prior click-throughs.• We utilize a unique source of randomization in the ranking mechanism to ensure that the rank is not endogenous to user click-throughs. The rank is determined by – The timing of posting by a post creator – The frequency of log-in by a user.

A Natural Experiment [1]• The randomization in the ranking mechanism Access Messages Channel 1. “Happy Halloween!!!” 2. “Spring break is in about 4 User A months . Mom is thinking of going to NYC“ 3. “Hey sweetie how’s ur day ??” Post Creator 4. “I liked Strawberry Frappuccino at @Starbucks”“I liked Strawberry 5. “I wanna go to CANADA”Frappuccino at @Starbucks” …..

A Natural Experiment [1]• The randomization in the ranking mechanism Access Messages Channel 29.“Happy Halloween!!!” User A 30. “Spring break in about 4 months . I am thinking of going to Tokyo“ 31. “Hey buddy what up ur Post day ??” Creator User B 32. “I liked Strawberry“I liked Strawberry Frappuccino at @Starbucks”Frappuccino at @Starbucks” 33. “I wanna go to Mexico” …..

A Natural Experiment [1]• The randomization in the ranking mechanism Access Messages Channel User A 15.“Happy Halloween!!!” 16.“I liked Strawberry Post Frappuccino at @Starbucks” Creator 17.“Hey honey what up ur User B day ??”“I liked StrawberryFrappuccino at @Starbucks” 18.“Spring break in about 4 months . I am thinking of going to Seoul“ 19.“I wanna go to Miami” User C …..

A Natural Experiment [1]• The same brand post appears at different positions (ranks) by users.• Control for any brand post-related unobserved quality issues Access Message Channel Rank 4 User A Post 32 Creator User B“I liked StrawberryFrappuccino at @Starbucks” 16 User C

A Natural Experiment [2]• We only use the rank of the first appeared brand post on a user’s screen. – Excluding the repeated appearances of the same brand post – We addressed another endogeneity issue. Login Access Message Time Channel Rank 1 4 Post User A 2 7 Creator“I liked StrawberryFrappuccino at @Starbucks” 3 12 …

An Overview of Model and Estimation• Hierarchical Logit Model – Dependent variable: whether to click (1) or not (0) – User-level parameters – Population-level parameters• Estimation using Markov Chain Monte Carlo (MCMC) Methods – Adaptive Metropolis-Hastings (MH) algorithm with a random walk chain (Atchadè 2006) – Two MH methods (one for user-level and the other for population-level estimation)

User Click Decisions• User Click Decisions – A user decides whether to click and visit a brand post when logging in via a PC – The observed user binary response, yijk is modeled through a random-utility or a mobile phone. framework as follows: yijk = 0 if uijk ≤ 0 1 if uijk > 0 where uijk is the latent utility for brand post k at time j for user i.

User-Level Model• User i’s latent utility with respect to brand post k at time j – For brand post k = 1, 2,..., s, uijk = βij0 + βij1 Rankijk + βij2 Distanceik + βij3 Rank ijk Distanceik + μ1 Mobileij + μ2 Followeeij +μ3 Bookmarkij + μ4 Agei + μ5 Sexi + μ6 BrandTenurek + μ7 PostTenurek + eijk• User i’s latent utility from not choosing any posts uijs +1 = eijs +1• Assuming that the error term is i.i.d from Type I extreme value distribution, the difference between error terms follows logistic distribution.

Choice Probability• Probability of user i choosing brand post k at time j exp�vijk � Pr �yijk = 1|βi � = 1 + exp�vijm � – where uijk = vijk + eijk and eijk is i.i.d from Type I extreme value distribution.

Population-Level Model [1]• Modeling three individual-specific parametersRank � βij1 = β1 + α1 Mobileij + α2 Followeeij + α3 Bookmarkij + α4 Agei + α5 Sexi + λi1Distance � βij2 = β2 + α6 Mobileij + α7 Followeeij + α8 Bookmarkij + α9 Agei + α10 Sexi + λi2 � βij3 = β3 + α11 Mobileij + α12 Followeeij + α13 Bookmarkij + α14 Agei + α15 Sexi + λi3Rank *Distance – λi1 , λi2 , and λi3 are unobserved, user-specific variables (i.e., random coefficients).

Population-Level Model [2] �• Unobserved heterogeneity with a random coefficient on the intercept – We capture the brand post-level (k) attractiveness denoted as β0k , � βij0 = β0k + λi0 – Where λi0 is unobserved heterogeneity across users.• Unobserved covariation among λi0 , λi1 , λi2 , and λi3 λi0 Σ0,0 λ Σ0,1 λ Σ0,2 λ Σ0,3 λ 0 ⎡ λ ⎤ λ ⎛ 0 ⎢Σ1,0 Σ1,1 λ Σ1,2 λ Σ1,3 ⎥⎞ λ � i1 � ~MVN ⎜�0� , ⎢ λ λi2 Σ Σ2,1 λ Σ2,2 λ Σ2,3 ⎥⎟ λ λi3 0 ⎢ 2,0 ⎥ ⎝ ⎣Σ3,0 Σ3,1 Σ3,2 Σ3,3 ⎦⎠ λ λ λ λ

Identification• Mathematical Identification – A location normalization restriction by setting the constant utility term for one brand post (i.e., “local restaurant”) to be zero to be the type I extreme value distribution, whose variance is set to be π2 ⁄6 – A scale normalization restriction by allowing the distribution for the error term• Empirical Identification – A unique source of randomization in the ranking mechanism – Unlike in the search engine context where the rank is determined by algorithm based on popularity and relevance, the rank of posts in our microblog setting is determined by “recency.”

Result [1]• Coefficient Estimates on User Click Decisions Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Brand Male Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: We ran the MCMC chain for 60,000 iterations and used the last 20,000 iterations to compute the mean and standard deviation of the posterior distribution of the model parameters. Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Main Effects• Coefficient Estimates on User Click Decisions Search Costs Effects in PC Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Male Brand Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Interaction Effects• Coefficient Estimates on User Click Decisions Larger Search Costs Effects in Mobile Phones Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Male Brand Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Main Effects• Coefficient Estimates on User Click Decisions Local Interests Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Male Brand Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Interaction Effects• Coefficient Estimates on User Click Decisions Larger Local Interests in Mobile Phones Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Male Brand Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Interaction Effects• Coefficient Estimates on User Click Decisions Two-way Interaction Effect Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Male Brand Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Interaction Effects• Coefficient Estimates on User Click Decisions Three-way Interaction Effect Variable Main Effect Moderating Effect Mobile Followee Bookmark Age Male Brand Post Tenure Tenure Intercept Brand Post 0.153*** 0.003*** 0.042*** -0.045*** -0.128*** -0.003*** -0.074*** Fixed Effect (0.001) (0.001) (0.002) (0.002) (0.003) (0.001) (0.002) Rank -0.230*** -0.078*** 0.0001 -0.003 -0.001 -0.010*** (0.002) (0.005) (0.0001) (0.003) (0.001) (0.003) Distance -0.107*** -0.098*** -0.001 -0.014*** 0.014*** 0.003 (0.002) (0.002) (0.0006) (0.002) (0.003) (0.002) Rank × -0.068*** -0.007*** 0.0003 -0.001 -0.002** -0.007*** Distance (0.002) (0.003) (0.0002) (0.002) (0.001) (0.003) Note: Posterior means and posterior standard deviations (in parentheses) are reported. *** denotes statistically significant at 0.01, ** denotes statistically significant at 0.05.

Economic Impact [1]• The impact of the search costs in mobile phones is larger than in PCs. – For PC users, one position increase (“UP”) in the rank of a brand post would increase the odds of clicking on that brand post by 26% (exp(0.230) = 1.259). 7. “Happy Halloween!!!” 8. “I liked Strawberry Frappuccino at @Starbucks” 9. “Hey honey what up ur day ??” 10.“This spring break in about 4 months . I am thinking of going to Seoul“ 11.“I wanna go to Miami”

Economic Impact [1]• The impact of the search costs in mobile phones is larger than in PCs. – For PC users, one position increase (“UP”) in the rank of a brand post would increase the odds of clicking on that brand post by 26% (exp(0.230) = 1.259). 7. “I liked Strawberry Frappuccino at @Starbucks” 8. “Happy Halloween!!!” 9. “Hey honey what up ur day ??” 10.“This spring break in about 4 months . I am thinking of More Click-throughs going to Seoul“ 11.“I wanna go to Miami”

Economic Impact [1]• The impact of the search costs in mobile phones is larger than in PCs. – For PC users, one position increase (“UP”) in the rank of a brand post would increase the odds of clicking on that brand post by 26% (exp(0.230) = 1.259). – For mobile phone users, one position increase (“UP”) in the rank would increase the odds of clicking on the brand post by 36%. 1. “Happy Halloween!!!” 2. “I liked Strawberry Frappuccino at @Starbucks” 3. “Hey honey what up ur day ??” 4. “This spring break in about 4 months . I am thinking of going to Seoul“ 5. “I wanna go to Miami”

Economic Impact [1]• The impact of the search costs in mobile phones is larger than in PCs. – For PC users, one position increase (“UP”) in the rank of a brand post would increase the odds of clicking on that brand post by 26% (exp(0.230) = 1.259). – For mobile phone users, one position increase (“UP”) in the rank would increase the odds of clicking on the brand post by 36%. More Click-throughs 1. “I liked Strawberry Frappuccino at @Starbucks” 2. “Happy Halloween!!!” 3. “Hey honey what up ur day ??” 4. “This spring break in about 4 months . I am thinking of going to Seoul“ 5. “I wanna go to Miami”

Economic Impact [2]• The impact of the local activities in mobile phones is larger than that in PCs. – For PC users, one mile decrease in the distance between a user and a brand store would increase the odds of clicking on that brand post by 11%. – For mobile phone users, one mile decrease in the distance between a user and a brand store would increase the odds of clicking on that brand post by 23%. User A 0 1 2 3 4 Miles

Economic Impact [2]• The impact of the local activities in mobile phones is larger than that in PCs. – For PC users, one mile decrease in the distance between a user and a brand store would increase the odds of clicking on that brand post by 11%. – For mobile phone users, one mile decrease in the distance between a user and a brand store would increase the odds of clicking on that brand post by 23%. More Click-throughs User A 0 1 2 3 4 Miles

Conclusion• We find the substantial impact of search costs and local activities on user click-throughs in mobile phones and in PCs.• The mobile internet is somewhat less “internet-like”: search costs are higher and distance matters more.• We model unobserved heterogeneity more explicitly using a hierarchical Bayesian modeling framework and efficiently estimating with adaptive MH algorithm.

Managerial Implications• We provide some insights: 1) Access channel-based pricing of sponsored messages - There exist asymmetric search costs between in mobile phones and in PCs. - Advertisers buy positions (rank) in sponsored search engine listings. They have incentives to bid higher on sponsored links in mobile phones than in PCs 2) Sponsored messages for local Commerce - Sponsored messages need to be about brand stores near a user’s geographical location (i.e., )

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How is the Mobile Internet Different? Search Costs and Local Activity

by Wharton Customer Analytics Initiative

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How is the Mobile Internet Different? Search Costs and Local Activity Presentation Transcript