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Targeted Ads Experiment on Instagram

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10047))

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Abstract

Ensuring media is brought appropriately and directed toward the “right people” is an important challenge. Marketers have traditionally employed demographic-based strategies such as age and gender to find target ad viewers. This research explores an alternative method by utilizing the embedding of brand relationships drawn from rich social media data. We presume that co-mentioned brands reflect the interest relationships of people and seek to exploit such information for targeted advertisements. Our 3-week experiment demonstrates the efficacy of the relationship-based ad campaign in yielding high click-through-rates. We also discuss the implications of our finding in designing social media-based marketing strategies.

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Notes

  1. 1.

    We utilized our experiment results as well as previous ad records from the studied brand.

References

  1. Chen, J., Haber, E., Kang, R., Hsieh, G., Mahmud, J.: Making use of derived personality: The case of social media ad targeting. In: International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  2. De Bock, K., Van den Poel, D.: Predicting website audience demographics forweb advertising targeting using multi-website clickstream data. Fund. Inf. 98(1), 49–70 (2010)

    Google Scholar 

  3. Foux, G.: Consumer-generated media: Get your customers involved. Brand Strategy 8, 38–39 (2006)

    Google Scholar 

  4. Hanna, R., Rohm, A., Crittenden, V.L.: Were all connected: The power of the social media ecosystem. Bus. Horiz. 54(3), 265–273 (2011)

    Article  Google Scholar 

  5. Lempert, P.: Caught in the web. Progressive Grocer 85(12), 18 (2006)

    Google Scholar 

  6. Mahmud, J., Fei, G., Xu, A., Pal, A., Zhou, M.: Predicting attitude and actions of twitter users. In: International Conference on Intelligent User Interfaces, ACM (2016)

    Google Scholar 

  7. Mangold, W.G., Faulds, D.J.: Social media: The new hybrid element of the promotion mix. Bus. Horiz. 52(4), 357–365 (2009)

    Article  Google Scholar 

  8. Morstatter, F., Pfeffer, J., Liu, H., Carley, K.M.: Is the sample good enough? comparing data from Twitter’s streaming API with Twitter’s firehose. In: International AAAI Conference on Web and Social Media (2013)

    Google Scholar 

  9. Park, J.Y., Sohn, Y., Moon, S.: Power of earned advertising on social network services: a case study of friend tagging on facebook. In: International AAAI Conference on Web and Social Media (2016)

    Google Scholar 

  10. Rashtchy, F., Kessler, A.M., Bieber, P.J., Shindler, N.H., Tzeng, J.C.: The user revolution: The new advertising ecosystem and the rise of the Internet as a mass medium. Piper Jaffray Investment Research, Minneapolis (2007)

    Google Scholar 

  11. Solis, B.: Engage: The Complete Guide for Brands and Businesses to Build, Cultivate, and Measure Success in the New Web. Wiley, Hoboken (2010)

    Google Scholar 

  12. Vollmer, C., Precourt, G.: Always on: Advertising, Marketing, and Media in an Era of Consumer Control. McGraw Hill Professional, New York (2008)

    Google Scholar 

  13. Yang, L., Sun, T., Zhang, M., Mei, Q.: We know what@ you# tag: does the dual role affect hashtag adoption?. In: International conference on World Wide Web (2012)

    Google Scholar 

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Acknowledgement

This work was partly supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0115-15-100).

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Correspondence to Meeyoung Cha .

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© 2016 Springer International Publishing AG

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Kim, H., Cha, M., Kim, W. (2016). Targeted Ads Experiment on Instagram. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-47874-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47873-9

  • Online ISBN: 978-3-319-47874-6

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