Computer Estimation of Customer Similarity with Facebook Lookalikes: Advantages and Disadvantages of Hyper-targeting

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F19%3A00006715" target="_blank" >RIV/46747885:24310/19:00006715 - isvavai.cz</a>

Result on the web

<a href="http://xplorestaging.ieee.org/ielx7/6287639/8600701/08877755.pdf?arnumber=8877755" target="_blank" >http://xplorestaging.ieee.org/ielx7/6287639/8600701/08877755.pdf?arnumber=8877755</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/access.2019.2948401" target="_blank" >10.1109/access.2019.2948401</a>

Result language

angličtina

Original language name

Computer Estimation of Customer Similarity with Facebook Lookalikes: Advantages and Disadvantages of Hyper-targeting

Original language description

Advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called micro targeting) 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 the entire segments. This allegedly allows B2B brands to target customers directly and provide unique personal and highly relevant experience. However, the scientific evidence to support this claim is missing. Some previous studies even suggest the 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 work 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 the data from 840 Facebook ads with different personalization level. 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.

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

50204 - Business and management

Project

<a href="/en/project/TJ02000206" target="_blank" >TJ02000206: Developing the skills necessary for the digital business transformation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

7

Issue of the periodical within the volume

prosinec

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

153365-153377

UT code for WoS article

000497163000238

EID of the result in the Scopus database

2-s2.0-85078331011