Skip to main content
SpringerLink
Log in

A Framework of Matching Algorithm for Influencer Marketing

  • Article
  • Published:
The Review of Socionetwork Strategies Aims and scope Submit manuscript

Abstract

Modern lifestyles have been altered significantly by recent developments in information and communications technology. In the marketing field, enterprises often use video advertising because of its effectiveness. The number of content producers is increasing, and YouTube is an effective medium for advertising. This advertising method is called ‘influencer marketing.’ Therefore, both the demand and the supply are increasing in the field of video advertising. In the present study, this trend was analysed from the viewpoints of an enterprise and a content producer. Then, a new business model was developed for increasing the demand and supply. The business model includes a matching function between enterprises and content producers for video advertising to achieve such increases. Second, a matching algorithm based on the calculation of the relativity between enterprises and content producers was proposed. Because the inputs of an enterprise and content producer include both numerical and textual data, a relativity-value calculation algorithm using these inputs was developed. Moreover, the feasibility of the proposed algorithm was evaluated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Expansion of data traffic. White paper on Japanese telecommunication in 2018. https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h30/pdf/30honpen.pdf. Accessed 26 Dec 2019.

  2. Japanese E-commerce market research in 2018. (April 2019). https://www.meti.go.jp/press/2018/04/20180425001/20180425001.html. Accessed 26 Dec 2019.

  3. Market size of influencer marketing in Japan. (March 2019). https://digitalinfact.com/release190328/. Accessed 26 Dec 2019.

  4. The state of influencer marketing 2019: benchmark report. https://influencermarketinghub.com/influencer-marketing-2019-benchmark-report/. Accessed 26 Dec 2019.

  5. Eroglu, F., & Bayraktar, E. (2019). Utilization of online influencers as an experimental marketing tool: A case of Instagram micro-celebrities. International Journal of Social Research, 12(63), 1057–1067.

    Article  Google Scholar 

  6. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58–73.

    Article  Google Scholar 

  7. Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798–828.

    Article  Google Scholar 

  8. Xiao, M., Wang, R., & Chan-Olmsted, S. (2018). Factors affecting YouTube influencer marketing credibility: A heuristic-systematic model. Journal of Media Business Studies, 15(3), 188–213.

    Article  Google Scholar 

  9. Garland, C. (2018). How to measure the value of influencer marketing: By applying the principles of growth marketing to influencer marketing, brands can now effectively track the success of an influencer partnership. Global Cosmetic Industry, 186(6), 22–25.

    Google Scholar 

  10. Conway, R. (2019). Influencer marketing: addressing the challenge. Business + Management, 33(1), 38–39.

    Google Scholar 

  11. Lawley, J. (2018). Brands to increase spend on influencer marketing despite fraud concerns. Marketing Week (Online Edition), 7(20/2018), 1.

    Google Scholar 

  12. Melnik, S., Garcia-Molina, H., & Rahm, E. (2002). Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proc. 18th international conference on data engineering (ICDE’02).

  13. Borgefors, G. (1988). Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6), 849–865.

    Article  Google Scholar 

  14. Cordella, L. P., Foggia, P., Sansone, C., & Vento, M. (2004). A graph isomorphism algorithm for matching large graphs. IEEE Transactions On Pattern Analysis and Machine Intelligence, 26(10), 1367–1372.

    Article  Google Scholar 

  15. Hakak, S. I., Kamsin, A., Shivakumara, P., Gilkar, G. A., Khan, W. Z., & Imran, M. (2019). Exact string matching algorithms: Survey, issues, and future research directions. IEEE Access, 7, 69614–69637.

    Article  Google Scholar 

  16. Sakoe, H. (1979). Two-level DP-matching—A dynamic programming-based pattern matching algorithm for connected word recognition. IEEE Transactions On Acoustics, Speech, and Signal Processing, 27(6), 588–595.

    Article  Google Scholar 

  17. Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947–963.

    Article  Google Scholar 

  18. Lin, Y.-C., & Tai, S.-C. (1997). Fast full-search block-matching algorithm for motion-compensated video compression. IEEE Transactions on Communications, 45(5), 527–531.

    Article  Google Scholar 

  19. Rajeswari, V., Kavitha, M., & Varughese, K. (2019). A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in semantic web. Soft Computing, 23(18), 8661–8676.

    Article  Google Scholar 

  20. Weiner, P. (2008). Linear pattern matching algorithms. In Proc. 4th annual symposium on switching and automata theory.

  21. Jun, L., Guangkuo, B., Chao, Q., & Wenhui, L. (2019). A fast multi-pattern matching algorithm for mining big network data. China Communications, 16(5), 121–136.

    Google Scholar 

  22. Li, A., Jiang, W., Yuan, W., Dai, D., Zhang, S., & Wei, Z. (2017). An improved FAST+SURF fast matching algorithm. In Proc. of 7th international congress of information and communication technology (ICICT2017). Procedia computer science (vol. 107, pp. 306–312).

  23. Irving, R. W. (1994). Stable marriage and indifference. Discrete Applied Mathematics, 48(3), 261–272.

    Article  Google Scholar 

  24. Manlove, D. F., Irving, R. W., Iwama, K., Miyazaki, S., & Morita, Y. (2002). Hard variants of stable marriage. Theoretical Computer Science, 276(1–2), 261–279.

    Article  Google Scholar 

  25. Koo, J., Chae, D. K., Kim, D. J., & Kim, S. W. (2019). Incremental C-Rank: An effective and efficient ranking algorithm for dynamic web environments. Knowledge-Based Systems, 176, 147–158.

    Article  Google Scholar 

  26. Ghasemain, F., Zamanifar, K., & Ghasem-Aghaee, N. (2018). An evolutionary non-linear ranking algorithm for ranking scientific collaborations. Applied Intelligence, 48(2), 465–481.

    Article  Google Scholar 

  27. Derhami, V., Khodadadian, E., Ghasemzadeh, M., & Zareh Bidoki, A. M. (2013). Applying reinforcement learning for web pages ranking algorithm. Applied Soft Computing, 13(4), 1686–1692.

    Article  Google Scholar 

  28. Promotion effectiveness of tie-up video content by YouTuber. (March 2015). https://www.uuum.co.jp/2015/03/25/2254. Accessed 26 Dec 2019.

  29. Sudden increase of YouTuber market in Japan. (February 2018). https://markezine.jp/article/detail/27843. Accessed 26 Dec 2019.

  30. MyVoice Enquete Library. (2017) Questionnaire for usage of YouTube. https://myel.myvoice.jp/products/detail.php?product_id=21012. Accessed 21 June 2019.

  31. Mood for side job. (September 2018). https://www.jiji.com/jc/graphics?p=ve_eco_company20180918j-02-w630. Accessed 26 Dec 2019.

  32. Ministry of Health, Labour and Welfare. (2018). White Paper of Freelance, 2018. https://www.mhlw.go.jp/file/05-Shingikai-12602000-Seisakutoukatsukan-Sanjikanshitsu_Roudouseisakutantou/0000189092_2.pdf. Accessed 27 June 2020.

  33. More than half of enterprises use influencers. (June 2018). https://www.cyberagent.co.jp/news/detail/id=21673. Accessed 26 Dec 2019.

  34. https://www.kotonoha.gr.jp/shonagon/. Accessed 26 Dec 2019.

Download references

Author information

Authors and Affiliations

  1. Faculty of Social Systems Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan

    M. Iwashita

Authors
  1. M. Iwashita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Iwashita.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iwashita, M. A Framework of Matching Algorithm for Influencer Marketing. Rev Socionetwork Strat 14, 227–246 (2020). https://doi.org/10.1007/s12626-020-00065-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12626-020-00065-2

Keywords

Search

Navigation