Estimating the Capacity of the Location - Based Advertising Channel

Delivering "relevant" advertisements to consumers carrying mobile devices is regarded by many as one of the most promising mobile business opportunities. The relevance of a mobile ad depends on at least two factors: (1) the proximity of the mobile consumer to the product or service being advertised, and (2) the match between the product or service and the interest of the mobile consumer. The interest of the mobile consumer can be either explicit (expressed by the mobile consumer) or implicit (inferred from user characteristics). This paper tries to empirically estimate the capacity of the mobile advertising channel, i.e., the number of relevant ads that can be delivered to mobile consumers. The estimations are based on a simulated mobile consumer population and simulated mobile ads. Both of the simulated data sets are realistic and derived based on real world data sources about population geo-demographics, businesses offering products or services, and related consumer surveys. The estimations take into consideration both the proximity and interest requirements of mobile ads, i.e., ads are only delivered to mobile consumers that are close-by and are interested, where interest is either explicit or implicit. Results show that the capacity of the LBA channel is rather large, which is evidence for a strong business case, but also indicate the need for user-control of the received mobile ads.

[1]  Thomas Brinkhoff,et al.  A Framework for Generating Network-Based Moving Objects , 2002, GeoInformatica.

[2]  Johanna Still,et al.  Mobile advertising in the eyes of retailers and consumers - empirical evidence from a real-life experiment , 2006, 2006 International Conference on Mobile Business.

[3]  Marko Merisavo,et al.  The effectiveness of targeted mobile advertising in selling mobile services: an empirical study , 2006, Int. J. Mob. Commun..

[4]  Heikki Karjaluoto,et al.  Factors influencing consumers' willingness to accept mobile advertising: a conceptual model , 2005, Int. J. Mob. Commun..

[5]  Key Pousttchi,et al.  A Contribution to Theory Building for Mobile Marketing: Categorizing Mobile Marketing Campaigns through Case Study Research , 2006, 2006 International Conference on Mobile Business.

[6]  Stuart J. Barnes,et al.  Mobile marketing: the role of permission and acceptance , 2004, Int. J. Mob. Commun..

[7]  Davar Rezania,et al.  Will Mobiles Dream of Electric Sheep? Expectations of the New Generation of Mobile Users: Misfits with Practice and Research. , 2006, 2006 International Conference on Mobile Business.

[8]  A. Guttman,et al.  A Dynamic Index Structure for Spatial Searching , 1984, SIGMOD 1984.

[9]  ThaeMin Lee,et al.  The role of contextual marketing offer in Mobile Commerce acceptance: comparison between Mobile Commerce users and nonusers , 2007, Int. J. Mob. Commun..

[10]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[11]  Lars Lundqvist,et al.  Dynamic allocation of urban space , 1975 .

[12]  Kristina Heinonen,et al.  Consumer responsiveness to mobile marketing , 2007, Int. J. Mob. Commun..

[13]  Gyözö Gidofalvi,et al.  The legal aspects of a location-based mobile advertising platform , 2008 .

[14]  A. Guttmma,et al.  R-trees: a dynamic index structure for spatial searching , 1984 .

[15]  Dieter Pfoser,et al.  Novel Approaches to the Indexing of Moving Object Trajectories , 2000, VLDB.

[16]  Torben Bach Pedersen,et al.  ST--ACTS: a spatio-temporal activity simulator , 2006, GIS '06.