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Selecting Important Features Related to Efficacy of Mobile Advertisements

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Book cover Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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Abstract

With growing use of mobile devices, mobile advertisement is playing increasingly important role. It can reach potential customers at any time and place based on individual’s real-time needs. Factors for success of mobile advertisements are different from similar media like Television or large screen monitors. We investigated the important factors to enhance click through rate (CTR) for a mobile Ad. As CTR is directly related to revenue, it is used to measure success of a mobile Ad. To identify important factors that determine CTR, we took two approaches - one directly asking subjects, and the other, from analyzing their selective attention. Subjects were asked to respond to questionnaire. From the responses important features were selected using Least Absolute Shrinkage and Selection Operator (LASSO). For the other approach, selective attention was inferred from subjects’ eye-tracking data. When results from two approaches were compared, the findings were similar. Those features will be helpful for designing Ads favored by users, as well as could earn more revenues.

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References

  1. Bridget, K.B., Mikyeung, B., Patricia, T.H., Lynnell, S.: The effect of involvement on visual attention and product choice. J. Retail. Consum. Serv. 24, 1021 (2015)

    Google Scholar 

  2. Chen, P.T., Hsieh, H.P.: Personalized mobile advertising: its key attributes, trends, and social impact. Technol. Forecast. Soc. Chang. 79, 543–557 (2012)

    Article  Google Scholar 

  3. Wong, C.H., Tan, W.H., Tan, B.I., Ooi, K.B.: Mobile advertising: the changing landscape of the advertising industry. Telematics Inform. 32, 720–734 (2015)

    Article  Google Scholar 

  4. Tibshirani, R.J.: Regression shrinkage and selection via the LASSO. J. Roy. Stat. Soc.: Ser. B (Methodol.) 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  5. Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: SODA 2007, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035 (2007)

    Google Scholar 

  6. Erik, S.: 5 mobile ad trends to watch in 2014 (2013). http://www.inc.com/erik-sherman/make-your-online-ads-move-into-mobile.html

  7. Gartner Inc., Gartner Says Mobile Advertising Spending Will Reach $18 Billion in 2014, newsroom of Gartner (2014). http://www.gartner.com/newsroom/id/2653121

  8. Interactive Advertising Bureau (IAB), IAB internet advertising revenue report, PwC Advisory Services LLC (PwC), April 2016. https://www.iab.com/wp-content/uploads/2016/04/IAB-Internet-Advertising-Revenue-Report-FY-2015.pdf

  9. Jos, M.P., Silvia, S.B., Carla, R.M., Joaquin, A.M.: Key factors of teenagers mobile advertising acceptance. Ind. Manage. Data Syst. 113(5), 732–749 (2013)

    Article  Google Scholar 

  10. Kim, Y.J., Han, J.: Why smartphone advertising attracts customers: a model of Web advertising, flow, and personalization. Comput. Hum. Behav. 33, 256–269 (2014)

    Article  Google Scholar 

  11. Lauren, J.: Mobile marketer reported: top 10 mobile advertising campaigns of 2013, Mobile Marketer (2013). http://www.mobilemarketer.com/cms/news/advertising/16847.html

  12. Mahesh, K.K., Reddy, A.R.M.: A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method. Pattern Recogn. 58, 39–48 (2016)

    Article  Google Scholar 

  13. Ufuoma, A., Ayesha, B.D.: Mobile advertisements and information privacy perception amongst South African generation of students. Telematics Inform. 32, 1–10 (2015)

    Article  Google Scholar 

  14. William, F., Chen, J.C., William, H.R.: The effect of variations in banner ad, type of product, website context, and language of advertising on internet users attitudes. Comput. Hum. Behav. 31, 37–47 (2014)

    Article  Google Scholar 

  15. Yang, B., Kim, Y., Yoo, C.: The integrated mobile advertising model: the effects of technology- and emotion-based evaluations. J. Bus. Res. 66, 1345–1352 (2013)

    Article  Google Scholar 

  16. Zhou, Q., Song, S., Huang, G., Wu, C.: Efficient lasso training from a geometrical perspective. Neurocomputing 168, 234–239 (2015)

    Article  Google Scholar 

  17. Chakraborty, G., Kozma, R., Murata, T., Zhao, Q.: Awareness in brain, society and beyond, a bridge connecting raw data to perception and cognition. IEEE Syst. Man Cybern. Mag. 1, 9–16 (2015)

    Article  Google Scholar 

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Acknowledgment

This project was partially supported by research grant from Iwate Prefectural University, iMOS research center

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Authors and Affiliations

  1. Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, 020-0693, Japan

    Goutam Chakraborty & L. C. Cheng

  2. Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung, Taiwan

    L. S. Chen

  3. Department of Industrial Technologies, University of Applied Sciences (HES-SO), Yverdon-les-Bains, Switzerland

    Cedric Bornand

Authors
  1. Goutam Chakraborty
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  2. L. C. Cheng
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  3. L. S. Chen
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  4. Cedric Bornand
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Corresponding author

Correspondence to Goutam Chakraborty .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Chakraborty, G., Cheng, L.C., Chen, L.S., Bornand, C. (2017). Selecting Important Features Related to Efficacy of Mobile Advertisements. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_68

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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