No abstract

The advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called microtargeting) is the use of detailed customer data and marketing automation to deliver highly targeted and personalized messages across a large number of channels. These campaigns are designed to appeal to specific people or small groups of customers. By using the ability to process large amounts of data through innovations, such as predictive analytics, marketers can gain a deeper understanding of their audiences, focusing on specific accounts and not on the entire segments. This reportedly allows B2B brands to target customers directly and provide unique personal and highly relevant experiences. However, the scientific evidence to support this claim is missing. Some previous studies even suggest a negative impact of highly personalized advertising content on user reactiveness and purchase behavior. In this article, we test the effects of different levels of personalized advertisements using the advanced campaign targeting tool called Facebook Lookalike Audiences. Facebook Lookalike Audiences works on the basis of the estimation of customer similarity based on the characteristics of a custom audience, as defined by the advertiser. We examine the performance of various targeting settings using data from 840 Facebook ads with different personalization levels. These advertisements are compared in terms of reach, number of reactions, frequency of impressions, number of clicks, average time spent on a website, number of viewed pages, number of conversions, and profitability. We believe that the findings presented in this article help clarify the factors that influence user reactiveness toward personalized online advertising using evidence from actual Facebook ad sets.

No abstract

This chapter demonstrates how to assess the performance of organic and sponsored activities on Facebook using the data available in Facebook Ads Manager, Facebook Page Insights, and Google Analytics. The main aim of the proposed ROI calculation model is to connect common social media marketing objectives with the analytical information available. The main emphasis is put on the technical aspect of ad performance assessment. The authors explain how the Facebook attribution system and post-impression algorithm work, describe the relation between advertising goals and metrics displayed as achieved campaign results, and demonstrate how to derive ROI indexes from different Facebook conversions. The chapter also includes a practical example how to calculate current and future value of ongoing ads.

No abstract