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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

<a href="/cs/project/TJ02000206" target="_blank" >TJ02000206: Rozvoj dovedností nezbytných pro digitální transformaci podnikání</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

prosinec

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

153365-153377

Kód UT WoS článku

000497163000238

EID výsledku v databázi Scopus

2-s2.0-85078331011