Targeting in Online Marketing: A Retrospective Analysis with a Focus on Practices of Facebook, Google, LinkedIn and TikTok

Abstract

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.