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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
Big Data Marketing Analytics, Chicago, IL
Success of mobile app markets
Google PlayApple App StoreWindows Phone
BlackBerry
0 200 400 600 800 1000 1200 1400 1600
# Apps (K)
Big Data Marketing Analytics, Chicago, IL
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]
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Big Data Marketing Analytics, Chicago, IL
Mobile app ad channels
Mobile Display Ads
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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
Big Data Marketing Analytics, Chicago, IL
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
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Big Data Marketing Analytics, Chicago, IL
Roadmap
• Data!
• Model!
• App Similarity!
• Empirical Analysis!
• Matching Mechanism
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Big Data Marketing Analytics, Chicago, IL
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
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Big Data Marketing Analytics, Chicago, IL
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
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Big Data Marketing Analytics, Chicago, IL
Ad effectiveness
• Bad news: free riders don’t stay!
• Good news: good matching can make improvements!!
• Question: what makes a good match?
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0
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10
15
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total organic display cross cross_10% cross_1%channel
hour_avg
Big Data Marketing Analytics, Chicago, IL
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
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Big Data Marketing Analytics, Chicago, IL
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
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Big Data Marketing Analytics, Chicago, IL
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
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Big Data Marketing Analytics, Chicago, IL
Topic models of app markets• Input: 195,956 mobile apps in Korean market!
• Output: 100 topics (set of keywords related to a common theme)
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* Translated from Korean to English for readers
Big Data Marketing Analytics, Chicago, IL
Empirical analysis
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Significantly positive:!
Topic_Similarity
Avg_Rate_Src,Tgt
File_Size_Tgt
Significantly negative:!
Days_Update_Tgt
Similar results with other metrics
Big Data Marketing Analytics, Chicago, IL
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!
• Stability: !
• No matched pairs prefer to deviate from the resultant matching !
• [Gale and Shapley 1962], [Roth 1984], [Hatfield et al. 2013]
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s_1
s_2
s_3
s_n
t_1
t_2
t_3
t_m
Big Data Marketing Analytics, Chicago, IL
Matching mechanism
• Linear Programming from [Gale 1960] and [Shapley and Shubik 1972]!
• Stable matching!
• Price suggestion!
• Improved utility: !
• 260% (predicted)
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Big Data Marketing Analytics, Chicago, IL
Future Directions• Extending matching model!
• Many-to-many matching!
• Pricing mechanism!
• Empirical analysis!
• Randomized field experiments
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Big Data Marketing Analytics, Chicago, IL
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-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
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Big Data Marketing Analytics, Chicago, IL
Winner-takes-all market
21http://techcrunch.com/2014/07/21/the-majority-of-todays-app-businesses-are-not-sustainable/
