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The aim of the study is to map the segmentation expectations of the marketers and then draw a unified conclusion regarding the segmentation and targeting options in the advertising system of the most popular online platforms in Hungary. When and in what way did the advertising platform meet or fail to meet segmentation expectations. The question was when and how the advertising platform meets or fails to meet segmentation expectations. According to the secondary data, the most used social media platforms and advertising systems were determined. Then, in the Google search engine filtered research was started for all targeting options, which are listed in Table No. 1. with different keywords and search terms in English and Hungarian. The filtering included an annual time interval from the publication date of each platform. Last, the websites were reviewed in the hit list from which a conclusion could be drawn about the relationship between the platform and the targeting option available at a given time. The examination took place on 21st April, 2022. It can be stated that all systems have very different results and segmentation focus. Google's advertising uses a unique colour system with keywords that does not exist on other platforms, and outperformed competition from social media platforms Facebook and LinkedIn in the 2nd year after TikTok's launch. The researchers' goal was to inform practitioners and theorists first about the applicable methods and second about the expected changes based on the knowledge of this data. The protection of personal data, which is becoming increasingly important in advertising systems, as well as the growing awareness of consumers and the user-friendly attitude of some manufacturers (Apple- IOS) are together leading to a re-evaluation of the systems. Already, the management of individual customer data is being revised and replaced, and new, similar target groups are being created instead. The profile used by machine learning for tagging will remain, but the ability to identify individual consumers will be lost. More data upgrades are expected in the future, which could lead to changes in segmentation capabilities. The number of sources used in research is high, but gaps can occur even with a systematic review. Advertising systems create and manage segmentation and targeting options without an officially published document, so changes in the system can only be determined using secondary data. Further continuous systematic research is needed in order to identify the changes. This summary has been prepared by the researchers with the utmost care and summarizes the segmentation habits, knowledge and evolution over time of theoretical and practical marketing. This paper contributes to identify and study the segmentation practice in digital marketing.

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References

  1. Atkinson, J. (2014). Market Segmentation: Segmentation and Keywords Retrieved from: https://www.persuasionworks.com/market-segmentation/segmentation-and-keywords.html.
     Google Scholar
  2. Bain, D. (2020). How to Segment an Audience: 5 Overlooked Tactics & Advanced Hacks. Retrieved from: https://mobilemonkey.com/blog/how-to-segment-an-audience.
     Google Scholar
  3. Bond, C. (2021). Every Facebook Ad Targeting Option in One Epic Graphic (Updated) Retrieved from: https://www.wordstream.com/blog/ws/2018/12/10/facebook-ad-targeting-options.
     Google Scholar
  4. Bond, C. (2021). The 11 Biggest Google Ads Updates of 2019. Retrieved from: https://www.wordstream.com/blog/ws/2019/12/09/biggest-google-ads-updates.
     Google Scholar
  5. Boos, N., Google (2013). Improvements to location targeting for international searches. Inside AdWords. Retrieved from: https://adwords.googleblog.com/2013/10/improvements-to-location-targeting-for.html.
     Google Scholar
  6. Calif, Google (2003). Google Expands Advertising Monetization Program for Websites Retrieved from: http://googlepress.blogspot.com/2003/06/google-expands-advertising-monetization.html.
     Google Scholar
  7. Callahan, S., Linkedin.com. (2019). Introducing ‘A Brief History of Advertising on LinkedIn’ (Infographic). Retrieved from: https://www.linkedin.com/business/marketing/blog/linkedin-ads/introducing-a-brief-history-of-advertising-on-linkedin-infographic.
     Google Scholar
  8. Cardona, L. (2022). How to Advertise on TikTok: Step by Step Guide. Retrieved from: https://www.cyberclick.net/numericalblogen/how-to-advertise-on-tiktok-step-by-step-guide.
     Google Scholar
  9. Cherepakhin, I. (2021). Here’s How Meta Is Changing Facebook Ads Targeting For 2022. Retrieved from: https://www.searchenginejournal.com/meta-facebook-ads-targeting/430239/#close.
     Google Scholar
  10. Chiu, T., Fang, D., Chen, J., Wang, Y., Jeris, C. (2001). A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database. In Environment Proceedings, 7. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, pp. 263-268.
     Google Scholar
  11. Constine, J. (2013). Facebook Confirms It Will Acquire Atlas Advertiser Suite From Microsoft To Close The Ad Spend Loop. TechCrunch. Retrieved from: https://techcrunch.com/2013/02/28/facebook-acquires-atlas/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAANxcM7e5aSm7TIyYCeMh5iGpbqwjM9wxGbx98sspEaS03l3jWKlC56clsJnDn7i90tWQL9--Fgi_GNQGvxytlc7Gs3ykKdE0s8lvFWCPxtXrr9_-SkG483wXsXj_U6RPEFhcbUwlxwt1LcrsES2Vp1fNk1Pz6KUlZfFSlYldPysq.
     Google Scholar
  12. Cuadros, A. J., Domínguez, V. E. (2014). Customer segmentation model based on value generation for marketing strategies formulation. Estudios Gerenciales, 30, 25-30.
     Google Scholar
  13. Decker, A. (2021). The Marketer’s Guide to Segmentation, Targeting, & Positioning Retrieved from: (STP Marketing). https://blog.hubspot.com/marketing/segmentation-targeting-positioning.
     Google Scholar
  14. Desarbo, W. S., Ramaswamy, V. (1994). Customer response based iterative segmentation procedures for response modeling in direct marketing. Journal of Direct Marketing, 8(3), 7-20.
     Google Scholar
  15. Duch-Brown, N., Grzybowski, L., Romahn, A., Verboven, F. (2021). Are online markets more integrated than traditional markets? Evidence from consumer electronics. Journal of International Economics, 131, 103476.
     Google Scholar
  16. Duchi, P., Alcocer, G. (2021). Welcome to the Era of Me2B, Part 2. Retrieved from: https://www.epam.com/insights/blogs/welcome-to-the-era-of-me2b-part-two.
     Google Scholar
  17. Feng, Google (2008). Demographic bidding beta test. Inside AdWords. Retrieved from: https://adwords.googleblog.com/2008/01/demographic-bidding-beta-test.html.
     Google Scholar
  18. Friedman, D., Google (2010). Go Mobile! Series: iPad Device Targeting Now Retrieved from: AdWords. https://adwords.googleblog.com/2010/04/go-mobile-series-ipad-device-targeting.html.
     Google Scholar
  19. Friedman, D. - Google (2010). New keyword targeting feature for advertisers in the UK and Canada. Retrieved from: Inside AdWords. https://adwords.googleblog.com/2010/05/new-keyword-targeting-feature-for.html.
     Google Scholar
  20. Gál, K. (2020). Permission marketing - hatékonyabb vevőszerzés. Retrieved from: https://www.klikkmarketing.hu/blog/permission-marketing?fbclid=IwAR1AIopb1Fe8stbHkTA4gCFhn6Aiup1sqrBYi--6aEyGGtFOTuc4gYQYxDI.
     Google Scholar
  21. Garrett, S. (2013). LinkedIn Ads for B2B Advertising: The Smart Choice - Marketing Mojo. Retrieved from: Marketing Mojo. https://www.marketing-mojo.com/blog/linkedin-ads-for-b2b-advertising-the-smart-choice/.
     Google Scholar
  22. GKIdigital (2021). 2020-ban három évet ugrott előre az e-kereskedelem. Retrieved from: https://gkidigital.hu/2021/03/25/2020-online-kiskereskedelem/.
     Google Scholar
  23. Haley, R. I. (1984). Benefit Segments: Backwards and Forwards. Journal of Advertising Research, 24(1), 19-25.
     Google Scholar
  24. Hall, M. (2022). In: Encyclopædia Britannica. Retrieved from: https://www.britannica.com/topic/Facebook.
     Google Scholar
  25. Hanlon, A. (2022). STP marketing: The Segmentation, Targeting, Positioning model. Retrieved from: https://www.smartinsights.com/digital-marketing-strategy/customer-segmentation-targeting/segmentation-targeting-and-positioning/.
     Google Scholar
  26. Hop Online. (2021). 9 Google Ad Trends for 2021 You Want to Know About Retrieved from: https://hop.online/digital-strategy/9-google-ad-trends-youll-want-to-know-about-in-2021/.
     Google Scholar
  27. Horn, B., Huang, W. (2016). Comparison of Segmentation Approaches. Retrieved from: https://www.decisionanalyst.com/whitepapers/comparesegmentation/
     Google Scholar
  28. Jon. (2005). Targeting in a whole new way. Inside AdWords. Retrieved from: https://adwords.googleblog.com/2005/06/targeting-in-whole-new-way_16.html.
     Google Scholar
  29. Kelly, A. (2008). Extending your AdWords Campaigns to the G1 and iPhone. Inside AdWords. Retrieved from: https://adwords.googleblog.com/2008/12/extending-your-adwords-campaigns-to-g1.html.
     Google Scholar
  30. Kemp, S. (2022). Digital 2022: Hungary. Retrieved from: https://datareportal.com/reports/digital-2022-hungary.
     Google Scholar
  31. Kent, R., Brandal, H. (2003). Improving email response in a permission marketing context. International Journal of Market Research, 45(4), 489-503.
     Google Scholar
  32. Kimura, J. (2019). 7 Things You Need to Know About LinkedIn Lookalike Audiences. Retrieved from: https://www.linkedin.com/business/marketing/blog/linkedin-ads/7-things-you-need-to-know-about-linkedin-lookalike-audiences.
     Google Scholar
  33. Kirkpatrick, D. (2011). The Facebook Effect: The Inside Story of the Company That Is Connecting the World. ISBN: 978-1-4391-0980-9 (ebook).
     Google Scholar
  34. Kotler, P., Kartajaya, H., Setiawan, I. (2010). Marketing 3.0: from products to customers to the human spirit. Hoboken, N.J.: Wiley.
     Google Scholar
  35. Kotler, P., Keller, K. L. (2016). Marketing Management, 15. kiadás. Pearson.
     Google Scholar
  36. Lamberton, C., Stephen, A. T. (2016). A thematic exploration of digital, social media. Journal of Marketing, 80, 146–172.
     Google Scholar
  37. Lane, H., Google (2009). New ways to reach the right audience on the Google content network. Inside AdWords. Retrieved from: https://adwords.googleblog.com/2009/03/new-ways-to-reach-right-audience-on.html.
     Google Scholar
  38. Law, M. (2018). Facebook Ad Targeting Changes Are Coming. Retrieved from: https://www.hilemangroup.com/Thought-Leadership/Hilelights-Blog/facebook-ad-targeting-changes.
     Google Scholar
  39. Linkedin.com. (2016). Introducing LinkedIn Account Targeting. Retrieved from: https://www.linkedin.com/business/marketing/blog/linkedin-ads/-introducing-linkedin-account-targeting.
     Google Scholar
  40. Lister, M. (2021). All of Facebook’s Ad Targeting Options (in One Epic Infographic). Retrieved from: https://www.wordstream.com/blog/ws/2016/06/27/facebook-ad-targeting-options-infographic.
     Google Scholar
  41. Lix, T. S., Berger, P.D., Magliozzi, T. L. (1995). New Customer Acquisition Prospecting Models and the Use of Commercially. Journal of Direct Marketing, 9(4), 8-18.
     Google Scholar
  42. Magliozzi, T. L., Berger, P. D. (1993). List segmentation strategies in direct marketing. Omega, 21(1), 61-72.
     Google Scholar
  43. McDonald, M., Christopher, M., & Bass, M. (2003). Market segmentation. Marketing, 41–65. https://doi.org/10.1007/978-1-4039-3741-4_3.
     Google Scholar
  44. Mowat, J. (2019). How Did Google Ads Change in 2018? Retrieved from: https://www.mackerelmedia.co.uk/blog/how-did-google-ads-change-in-2018/.
     Google Scholar
  45. Murakami, K., Natori, S. (2013). New Customer Management Technique: CRM by “RFM+I” Analysis. NRI Papers, 186, 1-13.
     Google Scholar
  46. Musinszki, Z. (2012). A kontrolling alapjai - oktatási segédlet. 7. Retrieved from: https://gtk.uni-miskolc.hu/files/13235/Kontrolling+%28alap+kieg%C3%A9sz%C3%ADt%C3%A9s%29+k%C3%A9zirat.pdf.
     Google Scholar
  47. Müller, J. M., Pommeranz, B., Weisser, J., Voigt, K.-I. (2018). Digital, Social Media, and Mobile Marketing in industrial buying: Still in need of customer segmentation? Empirical evidence from Poland and Germany. Industrial Marketing Management, 73, 70-83.
     Google Scholar
  48. Nagy, Á., Szűcs, K., Kemény, I., Simon, J. (2017). Az ügyfélértékelési modellek szájreklámmal történő bővítésének irányai, eredményei. Marketing & Menedzsment, 51, 14-27.
     Google Scholar
  49. Nazareth, G. (2015). How to Optimize Your LinkedIn Targeting to Reach the Right Audience. Retrieved from: https://www.marketing-mojo.com/blog/how-to-optimize-your-linkedin-targeting-reach-right-audience/.
     Google Scholar
  50. Ogunleye, J. (2022). LinkedIn. Retrieved from: https://www.linkedin.com/pulse/20140826143139-54693680-facebook-launches-custom-audiences/?articleId=5910036474757148672.
     Google Scholar
  51. Oh, J. (2019). LinkedIn introduces Interest Targeting. Retrieved from: https://www.linkedin.com/business/marketing/blog/linkedin-ads/introducing-interest-targeting.
     Google Scholar
  52. Orbach, D. (2018). Segmenting B2B vs. B2C: 6 audience characteristics to consider when planning your next marketing campaign. Retrieved from: https://nichemktg.ca/segmenting-b2b-vs-b2c/.
     Google Scholar
  53. Patel, N. (2017). Matched Audiences: LinkedIn’s Newest Secret Advertising Weapon You’re Not Using. Retrieved from: https://neilpatel.com/blog/linkedin-matched-audiences/.
     Google Scholar
  54. Peltier, J. W., Schribrowsky, J. A. (1997). The Use of Need Based Segmentation for Developing Segment-Specific Direct Marketing Strategies. Journal of Direct Marketing, 11(4), 55-62.
     Google Scholar
  55. Protalinski, E. (2013). Facebook announces Lookalike Audiences, lets advertisers target potential customers similar to current ones. Retrieved from: https://thenextweb.com/news/facebook-announces-lookalike-audiences-lets-advertisers-target-potential-customers-similar-to-current-ones.
     Google Scholar
  56. Riemersma, F., Jansen, R. (2006). MRM: More for less in marketing. Journal of Database Marketing & Customer Strategy Management, 13(2), 122-125.
     Google Scholar
  57. Roizen, B. F. (2009). Facebook adds location and language targeting. Retrieved from: https://vator.tv/news/2009-03-12-facebook-adds-location-and-language-targeting.
     Google Scholar
  58. Searls, D. (2012). The Intention Economy: When Customers Take Charge. Harvard Business Review Press, Boston.
     Google Scholar
  59. Shieh, L., Google (2011). Location targeting on AdWords: Now with more advanced controls. Retrieved from: https://adwords.googleblog.com/2011/03/location-targeting-on-adwords-now-with.html.
     Google Scholar
  60. Shopify. (2022). The Future of Ecommerce + Trends 2022. Retrieved from: https://www.shopify.com/research/future-of-commerce/future-of-ecommerce#Trend2.
     Google Scholar
  61. Simon, J. (2006). A klaszterelemzés alkalmazási lehetőségei a marketing-kutatásban. Statisztikai szemle, 85(7), 627-650.
     Google Scholar
  62. Suh, E. H., Noh, K. C., Suh, C. K. (1999). Customer list segmentation using the combined response model. Expert Systems with Applications, 17, 89-97.
     Google Scholar
  63. Szegedi, É. (2022): Hátrányból kell előnyt kovácsolniuk az e-kereskedőknek a következő években. Retrieved from: https://kosarertek.hu/uzemeltetes/hatranybol-kell-elonyt-kovacsolniuk-az-e-kereskedoknek-a-kovetkezo-evekben/?utm_source=facebook&utm_campaign=publish&fbclid=IwAR3wryMmF3CNzdHKWVl6pGAnX4a9ucFgDWe34YGbIRAJzhyWXxzyKB3O2MM.
     Google Scholar
  64. Szűcs, K. (2008). Fogyasztói piacok szegmentációja a trendaffinitás dimenziójában.
     Google Scholar
  65. Take Some Risk Inc. (2020). TikTok Advertising: The Complete Guide - Take Some Risk Inc. Retrieved from: https://www.takesomerisk.com/tiktok-advertising-guide/.
     Google Scholar
  66. Weinberg, A., Google. (2010). Now available: Reach the right audience through remarketing. Retrieved from: https://adwords.googleblog.com/2010/03/now-available-reach-right-audience.html.
     Google Scholar
  67. Wenograd, S. (2020). Google Banning Certain Categories for Employment, Housing and Credit Ads. Search Engine Journal. Retrieved from: https://www.searchenginejournal.com/google-banning-certain-categories-for-employment-housing-and-credit-ads/371975/#close.
     Google Scholar
  68. Yieldify (2020.: 4 Types of Market Segmentation with Real-World Examples. Retrieved from: https://www.yieldify.com/blog/types-of-market-segmentation/.
     Google Scholar
  69. Zhang, T., Ramakrishnan, R., Livny, M. (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. Proceedings, ACM SIGMOD Conference on Management of Data. Montreal, (pp. 103-114).
     Google Scholar
  70. Zhang, X. (2009): Improving the profitability of direct marketing: A quantile regression approach. Retrieved from: http://dx.doi.org/10.14793/mkt_etd.5.
     Google Scholar