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Matching Mobile Applications for Cross Promotion

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Gene Moo Lee
Gene Moo Lee

Matching Mobile Applications for Cross Promotion Presented in Conference on Big Data Marketing Analytics, Chicago, IL

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Matching Mobile Applications for Cross Promotion Gene Moo Lee! Ph.D. candidate University of Texas at Austin ! Joint work with Joowon Lee and Andrew B. Whinston
Success of mobile app markets # Apps (K) Big Data Marketing Analytics, Chicago, IL Google Play Apple App Store Windows Phone BlackBerry 0 200 400 600 800 1000 1200 1400 1600
Success and challenges • App diversity is a key success factor ! • Too many apps: visibility/search issue! • Developers: difficult to gain visibility! • Users: hard to search for the right apps! • Going towards a “winner-takes-all” market, not a long-tail market [Petsas et al. 2013], [Zhong and Michahelles 2013] Big Data Marketing Analytics, Chicago, IL 3
Mobile app ad channels Big Data Marketing Analytics, Chicago, IL Mobile Display Ads 4 Cross Promotions (Incentivized) Social Network Ads In this work, we study cross promotions! • Incentivize app installs with rewards! • It is a two-sided matching market
Our contributions 1. Evaluate ad effectiveness of cross promotion! 2. Model ad placements as matching problem! 3. Identify determinants of ad effectiveness: Novel app similarity measure with machine learning technique! 4. Design app matching mechanism Big Data Marketing Analytics, Chicago, IL 5
Roadmap • Data! • Model! • App Similarity! • Empirical Analysis! • Matching Mechanism Big Data Marketing Analytics, Chicago, IL 6
IGAWorks data • IGAWorks: Mobile ad platform company in Korea! • 1011 cross promotions (Sept 2013 ~ May 2014)! • 325 mobile apps (195K apps with meta info)! • 1 million users Big Data Marketing Analytics, Chicago, IL 7
Ad effectiveness • We measure the ad effectiveness by the user engagement level: ! • Session duration! • Number of app usages! • Number of item purchases! • Comparison by inflow channels: ! • Organic users, mobile display ads, cross promotions ! • Cross promotions: 10%, 1% best placements Big Data Marketing Analytics, Chicago, IL 8
Ad effectiveness • Bad news: free riders don’t stay! • Good news: good matching can make improvements!! • Question: what makes a good match? channel hour_avg Big Data Marketing Analytics, Chicago, IL 9 20 15 10 5 0 total organic display cross cross_10% cross_1%
Model • Source s: (established) app where ad is placed! • Target t: (new) app to promote! • Ad effectiveness of a match u(s, t) depends on ! • (1) Characteristics of source and target: X_s, X_t! • (2) Similarity between source and target: P_s,t Big Data Marketing Analytics, Chicago, IL 10
Individual app features • Developer-given variables: ! • days_regist (age)! • days_update (engagement)! • file_size (functional complexity)! • User-given variables: ! • num_rates (visibility)! • avg_rate (user perceived app quality)! • Both source and target Big Data Marketing Analytics, Chicago, IL 11
App similarity • Assumption: similar text descriptions -> similar apps! • Our approach: Apply latent Dirichlet allocation [Blei et al. 2003] algorithm on text descriptions of all apps! • App market is described by “topics”! • Each app is represented by a topic vector! • Calculate Topic_Similarity with cosine similarity Big Data Marketing Analytics, Chicago, IL 12
Topic models of app markets • Input: 195,956 mobile apps in Korean market! • Output: 100 topics (set of keywords related to a common theme) Big Data Marketing Analytics, Chicago, IL 13 * Translated from Korean to English for readers
Empirical analysis Big Data Marketing Analytics, Chicago, IL 14 Significantly positive:! Topic_Similarity Avg_Rate_Src,Tgt File_Size_Tgt Significantly negative:! Days_Update_Tgt Similar results with other metrics
Matching in cross promotions • Problem definition: Given S andT, ad platform should find a stable matching m that maximizes expected utilities! • Utility is transferable between s and t! • Leverage our model to predict utility of a match (s, t)! • One-to-one matching: one s promotes one t! Big Data Marketing Analytics, Chicago, IL • Stability: ! • No matched pairs prefer to deviate from the resultant matching ! • [Gale and Shapley 1962], [Roth 1984], [Hatfield et al. 2013] 15 s_1 s_2 s_3 s_n t_1 t_2 t_3 t_m
Matching mechanism • Linear Programming from [Gale 1960] and [Shapley and Shubik 1972]! • Stable matching! • Price suggestion! • Improved utility: ! • 260% (predicted) Big Data Marketing Analytics, Chicago, IL 16
Future Directions • Extending matching model! • Many-to-many matching! • Pricing mechanism! • Empirical analysis! • Randomized field experiments Big Data Marketing Analytics, Chicago, IL 17
Thank you!! ! Contact Info: Gene Moo Lee! gene@cs.utexas.edu
References • Mobile app stats: http://en.wikipedia.org/wiki/List_of_mobile_software_distribution_platforms! • Mobile app revenue by developers: http://techcrunch.com/2014/07/21/the-majority-of-todays-app-businesses-are-not-sustainable/ ! • Facebook Mobile App Ad: https://developers.facebook.com/products/ads/! • Twitter mobile app promotion suite: https://blog.twitter.com/2014/a-new-way-to-promote-mobile-apps-to-1-billion-devices-both-on-and- Big Data Marketing Analytics, Chicago, IL off-twitter! • IGAWorks: http://www.igaworks.com/en/ ! • Petsas T., Papadogiannakis A., Polychronakis M., Markatos E. P., and Karagiannis T. (2013). “Rise of the Planet of the Apps: A Systematic Study of the Mobile App Ecosystem.” Proceedings of the Internet Measurement Conference, pp. 277-290.! • Zhong N. and Michahelles F. (2013). "Google Play Is Not A Long Tail Market: An Empirical Analysis of App Adoption on the Google Play App Market." Proceedings of the Annual ACM Symposium on Applied Computing: pp. 499-504.! • Blei D. B., Ng A. Y., and Jordan M. I. (2003). “Latent Dirichlet Allocation.” Journal of Machine Learning Research, 3: pp. 993-1022.! • Shapley L. and Shubik M. (1972). “The Assignment Game 1: The Core.” International Journal of Game Theory, 1: pp. 111-130.! • Roth A. E. (1984). “The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory.” Journal of Political Economy, 92(6): pp. 991–1016.! • Hatfield J.W., Kominers S. D., Nichifor A., Ostrovsky M., andWestkamp A. (2013). “Stability and Competitive Equilibrium in Trading Networks.” Journal of Political Economy, 12(5): pp. 966-1005.! • Gale D. (1960). “The Theory of Linear Economic Models.” New York: McGraw-Hill.
Backup Slides 20
Winner-takes-all market Big Data Marketing Analytics, Chicago, IL 21 http://techcrunch.com/2014/07/21/the-majority-of-todays-app-businesses-are-not-sustainable/

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Matching Mobile Applications for Cross Promotion

  • 1. Matching Mobile Applications for Cross Promotion Gene Moo Lee! Ph.D. candidate University of Texas at Austin ! Joint work with Joowon Lee and Andrew B. Whinston
  • 2. Success of mobile app markets # Apps (K) Big Data Marketing Analytics, Chicago, IL Google Play Apple App Store Windows Phone BlackBerry 0 200 400 600 800 1000 1200 1400 1600
  • 3. Success and challenges • App diversity is a key success factor ! • Too many apps: visibility/search issue! • Developers: difficult to gain visibility! • Users: hard to search for the right apps! • Going towards a “winner-takes-all” market, not a long-tail market [Petsas et al. 2013], [Zhong and Michahelles 2013] Big Data Marketing Analytics, Chicago, IL 3
  • 4. Mobile app ad channels Big Data Marketing Analytics, Chicago, IL Mobile Display Ads 4 Cross Promotions (Incentivized) Social Network Ads In this work, we study cross promotions! • Incentivize app installs with rewards! • It is a two-sided matching market
  • 5. Our contributions 1. Evaluate ad effectiveness of cross promotion! 2. Model ad placements as matching problem! 3. Identify determinants of ad effectiveness: Novel app similarity measure with machine learning technique! 4. Design app matching mechanism Big Data Marketing Analytics, Chicago, IL 5
  • 6. Roadmap • Data! • Model! • App Similarity! • Empirical Analysis! • Matching Mechanism Big Data Marketing Analytics, Chicago, IL 6
  • 7. IGAWorks data • IGAWorks: Mobile ad platform company in Korea! • 1011 cross promotions (Sept 2013 ~ May 2014)! • 325 mobile apps (195K apps with meta info)! • 1 million users Big Data Marketing Analytics, Chicago, IL 7
  • 8. Ad effectiveness • We measure the ad effectiveness by the user engagement level: ! • Session duration! • Number of app usages! • Number of item purchases! • Comparison by inflow channels: ! • Organic users, mobile display ads, cross promotions ! • Cross promotions: 10%, 1% best placements Big Data Marketing Analytics, Chicago, IL 8
  • 9. Ad effectiveness • Bad news: free riders don’t stay! • Good news: good matching can make improvements!! • Question: what makes a good match? channel hour_avg Big Data Marketing Analytics, Chicago, IL 9 20 15 10 5 0 total organic display cross cross_10% cross_1%
  • 10. Model • Source s: (established) app where ad is placed! • Target t: (new) app to promote! • Ad effectiveness of a match u(s, t) depends on ! • (1) Characteristics of source and target: X_s, X_t! • (2) Similarity between source and target: P_s,t Big Data Marketing Analytics, Chicago, IL 10
  • 11. Individual app features • Developer-given variables: ! • days_regist (age)! • days_update (engagement)! • file_size (functional complexity)! • User-given variables: ! • num_rates (visibility)! • avg_rate (user perceived app quality)! • Both source and target Big Data Marketing Analytics, Chicago, IL 11
  • 12. App similarity • Assumption: similar text descriptions -> similar apps! • Our approach: Apply latent Dirichlet allocation [Blei et al. 2003] algorithm on text descriptions of all apps! • App market is described by “topics”! • Each app is represented by a topic vector! • Calculate Topic_Similarity with cosine similarity Big Data Marketing Analytics, Chicago, IL 12
  • 13. Topic models of app markets • Input: 195,956 mobile apps in Korean market! • Output: 100 topics (set of keywords related to a common theme) Big Data Marketing Analytics, Chicago, IL 13 * Translated from Korean to English for readers
  • 14. Empirical analysis Big Data Marketing Analytics, Chicago, IL 14 Significantly positive:! Topic_Similarity Avg_Rate_Src,Tgt File_Size_Tgt Significantly negative:! Days_Update_Tgt Similar results with other metrics
  • 15. Matching in cross promotions • Problem definition: Given S andT, ad platform should find a stable matching m that maximizes expected utilities! • Utility is transferable between s and t! • Leverage our model to predict utility of a match (s, t)! • One-to-one matching: one s promotes one t! Big Data Marketing Analytics, Chicago, IL • Stability: ! • No matched pairs prefer to deviate from the resultant matching ! • [Gale and Shapley 1962], [Roth 1984], [Hatfield et al. 2013] 15 s_1 s_2 s_3 s_n t_1 t_2 t_3 t_m
  • 16. Matching mechanism • Linear Programming from [Gale 1960] and [Shapley and Shubik 1972]! • Stable matching! • Price suggestion! • Improved utility: ! • 260% (predicted) Big Data Marketing Analytics, Chicago, IL 16
  • 17. Future Directions • Extending matching model! • Many-to-many matching! • Pricing mechanism! • Empirical analysis! • Randomized field experiments Big Data Marketing Analytics, Chicago, IL 17
  • 18. Thank you!! ! Contact Info: Gene Moo Lee! gene@cs.utexas.edu
  • 19. References • Mobile app stats: http://en.wikipedia.org/wiki/List_of_mobile_software_distribution_platforms! • Mobile app revenue by developers: http://techcrunch.com/2014/07/21/the-majority-of-todays-app-businesses-are-not-sustainable/ ! • Facebook Mobile App Ad: https://developers.facebook.com/products/ads/! • Twitter mobile app promotion suite: https://blog.twitter.com/2014/a-new-way-to-promote-mobile-apps-to-1-billion-devices-both-on-and- Big Data Marketing Analytics, Chicago, IL off-twitter! • IGAWorks: http://www.igaworks.com/en/ ! • Petsas T., Papadogiannakis A., Polychronakis M., Markatos E. P., and Karagiannis T. (2013). “Rise of the Planet of the Apps: A Systematic Study of the Mobile App Ecosystem.” Proceedings of the Internet Measurement Conference, pp. 277-290.! • Zhong N. and Michahelles F. (2013). "Google Play Is Not A Long Tail Market: An Empirical Analysis of App Adoption on the Google Play App Market." Proceedings of the Annual ACM Symposium on Applied Computing: pp. 499-504.! • Blei D. B., Ng A. Y., and Jordan M. I. (2003). “Latent Dirichlet Allocation.” Journal of Machine Learning Research, 3: pp. 993-1022.! • Shapley L. and Shubik M. (1972). “The Assignment Game 1: The Core.” International Journal of Game Theory, 1: pp. 111-130.! • Roth A. E. (1984). “The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory.” Journal of Political Economy, 92(6): pp. 991–1016.! • Hatfield J.W., Kominers S. D., Nichifor A., Ostrovsky M., andWestkamp A. (2013). “Stability and Competitive Equilibrium in Trading Networks.” Journal of Political Economy, 12(5): pp. 966-1005.! • Gale D. (1960). “The Theory of Linear Economic Models.” New York: McGraw-Hill.
  • 21. Winner-takes-all market Big Data Marketing Analytics, Chicago, IL 21 http://techcrunch.com/2014/07/21/the-majority-of-todays-app-businesses-are-not-sustainable/