Machine learning as an effective paradigm for persuasive message design

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F20%3APU135317" target="_blank" >RIV/00216305:26510/20:PU135317 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007%2Fs11135-020-00972-0" target="_blank" >https://link.springer.com/article/10.1007%2Fs11135-020-00972-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11135-020-00972-0" target="_blank" >10.1007/s11135-020-00972-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning as an effective paradigm for persuasive message design

Popis výsledku v původním jazyce

The impact of the development of the internet and new communications channels on the marketing industry pushed practitioners to devise new tools and approaches for influencing consumer attitudes and behaviors towards products and services. This has led to new insights into persuasive message design. In general, persuasive advertisement messaging can be viewed as a combination of context, i.e., a message, and additional affiliated components such as images, video, and special graphics. It is composed out of two main attribute categorizations: (1) textual content such as a product description or affiliated message and (2) sensible content such as product image, color, or even scent. In a competitive market in which consumers are constantly exposed to a hyper-abundance of products that also contain sensible attributes, it is crucial to design persuasive messages that will maximally appeal to desired consumers and evoke their positive response. Yet only a few studies have focused on the effective design of persuasive advertisement messages characterized by two integrated elements. As such, this research focuses on effective persuasive message design with integrated product scent and color attributes. We demonstrate how a machine learning process can be utilized to generate optimal persuasive messages by estimating the contribution of each message attribute to the final class attribute: the purchase intention response. Our results show that several prediction algorithms can enhance consumer response value. In addition, correlations between several attributes affiliated with the message can be derived by graph theory-based estimation. This research thus provides insight into attribute values important for management decisions, with implications for effective persuasive message design. Ultimately, this may lead to higher response rates for marketing practitioners in an increasingly competitive market.

Název v anglickém jazyce

Machine learning as an effective paradigm for persuasive message design

Popis výsledku anglicky

The impact of the development of the internet and new communications channels on the marketing industry pushed practitioners to devise new tools and approaches for influencing consumer attitudes and behaviors towards products and services. This has led to new insights into persuasive message design. In general, persuasive advertisement messaging can be viewed as a combination of context, i.e., a message, and additional affiliated components such as images, video, and special graphics. It is composed out of two main attribute categorizations: (1) textual content such as a product description or affiliated message and (2) sensible content such as product image, color, or even scent. In a competitive market in which consumers are constantly exposed to a hyper-abundance of products that also contain sensible attributes, it is crucial to design persuasive messages that will maximally appeal to desired consumers and evoke their positive response. Yet only a few studies have focused on the effective design of persuasive advertisement messages characterized by two integrated elements. As such, this research focuses on effective persuasive message design with integrated product scent and color attributes. We demonstrate how a machine learning process can be utilized to generate optimal persuasive messages by estimating the contribution of each message attribute to the final class attribute: the purchase intention response. Our results show that several prediction algorithms can enhance consumer response value. In addition, correlations between several attributes affiliated with the message can be derived by graph theory-based estimation. This research thus provides insight into attribute values important for management decisions, with implications for effective persuasive message design. Ultimately, this may lead to higher response rates for marketing practitioners in an increasingly competitive market.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

QUALITY & QUANTITY

ISSN

0033-5177

e-ISSN

1573-7845

Svazek periodika

2020

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85079455343