Developing Personalized Marketing Service Using Generative AI

In today's world, the development of social network services (SNS) like Facebook and Instagram has enabled consumers to acquire information about products through various channels. The acquisition of diverse information has led to a diversification in consumer preferences and requirements. As c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access 2024, Vol.12, p.22394-22402
Hauptverfasser: Lee, Gun Ho, Lee, Kyoung Jun, Jeong, Baek, Kim, Taekyung
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 22402
container_issue
container_start_page 22394
container_title IEEE access
container_volume 12
creator Lee, Gun Ho
Lee, Kyoung Jun
Jeong, Baek
Kim, Taekyung
description In today's world, the development of social network services (SNS) like Facebook and Instagram has enabled consumers to acquire information about products through various channels. The acquisition of diverse information has led to a diversification in consumer preferences and requirements. As consumer preferences diversify and online channels expand, there is an increasing need for companies to provide personalized marketing. Among the means of personalized marketing, personalized marketing messages are a key tool that can enhance customer engagement. However, a limitation of personalized marketing message services is the cost issue associated with manually writing individual marketing messages for personalization. To solve this problem, when developing automated technology for personalized marketing messages, there were concerns about the complexity of model development and the quality of messages generated automatically. In this study, we propose the Persuasive Message Intelligence (PMI) service, which utilizes the recently prominent Large Language Model for automated individual personalized marketing messages. PMI generates marketing messages through prompt engineering based on the theory of persuasion in marketing and prior research on AI-generated messages, and validates the elements of prompts through surveys. The trial and error of researchers presented in this study, along with the know-how and rules of prompt engineering, will serve as guidelines for those who wish to develop services through prompts in the future.
doi_str_mv 10.1109/ACCESS.2024.3361946
format Article
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_10419357</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10419357</ieee_id><doaj_id>oai_doaj_org_article_57e366ae89b04522b6792001cdfbcaf5</doaj_id><sourcerecordid>2927685836</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-8c764d434cb4831199b1b9c0094a7d36fa4107dc8d6a94b7fc0ad9fef207c6553</originalsourceid><addsrcrecordid>eNpNUE1Lw0AQDaJg0f4CPQQ8p-73Zg8eSq21UFGoPS-bzaRsjd26mxb015uYIp3LzDzee8O8JLnBaIQxUvfjyWS6XI4IImxEqcCKibNkQLBQGeVUnJ_Ml8kwxg1qK28hLgfJwyMcoPY7t12nbxCi35ra_UCZvpjwAU0HLyEcnIV0FbttBlsIpnEHSMfz6-SiMnWE4bFfJaun6fvkOVu8zuaT8SKzlKsmy60UrGSU2YLlFGOlClwoi5BiRpZUVIZhJEubl8IoVsjKIlOqCiqCpBWc06tk3vuW3mz0LrhPE761N07_AT6stQmNszVoLoEKYSBXBWKckEJIRRDCtqwKa6rO66732gX_tYfY6I3fh_btqIkiUuQ8p6Jl0Z5lg48xQPV_FSPdxa772HUXuz7G3qpue5UDgBMFw4pySX8BhEl9AA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2927685836</pqid></control><display><type>article</type><title>Developing Personalized Marketing Service Using Generative AI</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Lee, Gun Ho ; Lee, Kyoung Jun ; Jeong, Baek ; Kim, Taekyung</creator><creatorcontrib>Lee, Gun Ho ; Lee, Kyoung Jun ; Jeong, Baek ; Kim, Taekyung</creatorcontrib><description>In today's world, the development of social network services (SNS) like Facebook and Instagram has enabled consumers to acquire information about products through various channels. The acquisition of diverse information has led to a diversification in consumer preferences and requirements. As consumer preferences diversify and online channels expand, there is an increasing need for companies to provide personalized marketing. Among the means of personalized marketing, personalized marketing messages are a key tool that can enhance customer engagement. However, a limitation of personalized marketing message services is the cost issue associated with manually writing individual marketing messages for personalization. To solve this problem, when developing automated technology for personalized marketing messages, there were concerns about the complexity of model development and the quality of messages generated automatically. In this study, we propose the Persuasive Message Intelligence (PMI) service, which utilizes the recently prominent Large Language Model for automated individual personalized marketing messages. PMI generates marketing messages through prompt engineering based on the theory of persuasion in marketing and prior research on AI-generated messages, and validates the elements of prompts through surveys. The trial and error of researchers presented in this study, along with the know-how and rules of prompt engineering, will serve as guidelines for those who wish to develop services through prompts in the future.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3361946</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Advertising ; Artificial intelligence ; Automation ; Business ; Channels ; Customer services ; Customization ; Error analysis ; Generative adversarial networks ; Generative AI ; Generative artificial intelligence ; History ; Large language models ; Marketing ; Messages ; personalized marketing message ; persuasion theory ; prompt engineering ; Psychology ; Public healthcare ; Social networking (online) ; Social networks</subject><ispartof>IEEE access, 2024, Vol.12, p.22394-22402</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-8c764d434cb4831199b1b9c0094a7d36fa4107dc8d6a94b7fc0ad9fef207c6553</cites><orcidid>0000-0003-4499-5583 ; 0000-0001-5089-2914 ; 0009-0009-6000-1674</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10419357$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27610,27900,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Lee, Gun Ho</creatorcontrib><creatorcontrib>Lee, Kyoung Jun</creatorcontrib><creatorcontrib>Jeong, Baek</creatorcontrib><creatorcontrib>Kim, Taekyung</creatorcontrib><title>Developing Personalized Marketing Service Using Generative AI</title><title>IEEE access</title><addtitle>Access</addtitle><description>In today's world, the development of social network services (SNS) like Facebook and Instagram has enabled consumers to acquire information about products through various channels. The acquisition of diverse information has led to a diversification in consumer preferences and requirements. As consumer preferences diversify and online channels expand, there is an increasing need for companies to provide personalized marketing. Among the means of personalized marketing, personalized marketing messages are a key tool that can enhance customer engagement. However, a limitation of personalized marketing message services is the cost issue associated with manually writing individual marketing messages for personalization. To solve this problem, when developing automated technology for personalized marketing messages, there were concerns about the complexity of model development and the quality of messages generated automatically. In this study, we propose the Persuasive Message Intelligence (PMI) service, which utilizes the recently prominent Large Language Model for automated individual personalized marketing messages. PMI generates marketing messages through prompt engineering based on the theory of persuasion in marketing and prior research on AI-generated messages, and validates the elements of prompts through surveys. The trial and error of researchers presented in this study, along with the know-how and rules of prompt engineering, will serve as guidelines for those who wish to develop services through prompts in the future.</description><subject>Advertising</subject><subject>Artificial intelligence</subject><subject>Automation</subject><subject>Business</subject><subject>Channels</subject><subject>Customer services</subject><subject>Customization</subject><subject>Error analysis</subject><subject>Generative adversarial networks</subject><subject>Generative AI</subject><subject>Generative artificial intelligence</subject><subject>History</subject><subject>Large language models</subject><subject>Marketing</subject><subject>Messages</subject><subject>personalized marketing message</subject><subject>persuasion theory</subject><subject>prompt engineering</subject><subject>Psychology</subject><subject>Public healthcare</subject><subject>Social networking (online)</subject><subject>Social networks</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1Lw0AQDaJg0f4CPQQ8p-73Zg8eSq21UFGoPS-bzaRsjd26mxb015uYIp3LzDzee8O8JLnBaIQxUvfjyWS6XI4IImxEqcCKibNkQLBQGeVUnJ_Ml8kwxg1qK28hLgfJwyMcoPY7t12nbxCi35ra_UCZvpjwAU0HLyEcnIV0FbttBlsIpnEHSMfz6-SiMnWE4bFfJaun6fvkOVu8zuaT8SKzlKsmy60UrGSU2YLlFGOlClwoi5BiRpZUVIZhJEubl8IoVsjKIlOqCiqCpBWc06tk3vuW3mz0LrhPE761N07_AT6stQmNszVoLoEKYSBXBWKckEJIRRDCtqwKa6rO66732gX_tYfY6I3fh_btqIkiUuQ8p6Jl0Z5lg48xQPV_FSPdxa772HUXuz7G3qpue5UDgBMFw4pySX8BhEl9AA</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Lee, Gun Ho</creator><creator>Lee, Kyoung Jun</creator><creator>Jeong, Baek</creator><creator>Kim, Taekyung</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4499-5583</orcidid><orcidid>https://orcid.org/0000-0001-5089-2914</orcidid><orcidid>https://orcid.org/0009-0009-6000-1674</orcidid></search><sort><creationdate>2024</creationdate><title>Developing Personalized Marketing Service Using Generative AI</title><author>Lee, Gun Ho ; Lee, Kyoung Jun ; Jeong, Baek ; Kim, Taekyung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-8c764d434cb4831199b1b9c0094a7d36fa4107dc8d6a94b7fc0ad9fef207c6553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Advertising</topic><topic>Artificial intelligence</topic><topic>Automation</topic><topic>Business</topic><topic>Channels</topic><topic>Customer services</topic><topic>Customization</topic><topic>Error analysis</topic><topic>Generative adversarial networks</topic><topic>Generative AI</topic><topic>Generative artificial intelligence</topic><topic>History</topic><topic>Large language models</topic><topic>Marketing</topic><topic>Messages</topic><topic>personalized marketing message</topic><topic>persuasion theory</topic><topic>prompt engineering</topic><topic>Psychology</topic><topic>Public healthcare</topic><topic>Social networking (online)</topic><topic>Social networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Gun Ho</creatorcontrib><creatorcontrib>Lee, Kyoung Jun</creatorcontrib><creatorcontrib>Jeong, Baek</creatorcontrib><creatorcontrib>Kim, Taekyung</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Gun Ho</au><au>Lee, Kyoung Jun</au><au>Jeong, Baek</au><au>Kim, Taekyung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Developing Personalized Marketing Service Using Generative AI</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>22394</spage><epage>22402</epage><pages>22394-22402</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>In today's world, the development of social network services (SNS) like Facebook and Instagram has enabled consumers to acquire information about products through various channels. The acquisition of diverse information has led to a diversification in consumer preferences and requirements. As consumer preferences diversify and online channels expand, there is an increasing need for companies to provide personalized marketing. Among the means of personalized marketing, personalized marketing messages are a key tool that can enhance customer engagement. However, a limitation of personalized marketing message services is the cost issue associated with manually writing individual marketing messages for personalization. To solve this problem, when developing automated technology for personalized marketing messages, there were concerns about the complexity of model development and the quality of messages generated automatically. In this study, we propose the Persuasive Message Intelligence (PMI) service, which utilizes the recently prominent Large Language Model for automated individual personalized marketing messages. PMI generates marketing messages through prompt engineering based on the theory of persuasion in marketing and prior research on AI-generated messages, and validates the elements of prompts through surveys. The trial and error of researchers presented in this study, along with the know-how and rules of prompt engineering, will serve as guidelines for those who wish to develop services through prompts in the future.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3361946</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-4499-5583</orcidid><orcidid>https://orcid.org/0000-0001-5089-2914</orcidid><orcidid>https://orcid.org/0009-0009-6000-1674</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2169-3536
ispartof IEEE access, 2024, Vol.12, p.22394-22402
issn 2169-3536
2169-3536
language eng
recordid cdi_ieee_primary_10419357
source IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals
subjects Advertising
Artificial intelligence
Automation
Business
Channels
Customer services
Customization
Error analysis
Generative adversarial networks
Generative AI
Generative artificial intelligence
History
Large language models
Marketing
Messages
personalized marketing message
persuasion theory
prompt engineering
Psychology
Public healthcare
Social networking (online)
Social networks
title Developing Personalized Marketing Service Using Generative AI
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T20%3A31%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Developing%20Personalized%20Marketing%20Service%20Using%20Generative%20AI&rft.jtitle=IEEE%20access&rft.au=Lee,%20Gun%20Ho&rft.date=2024&rft.volume=12&rft.spage=22394&rft.epage=22402&rft.pages=22394-22402&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2024.3361946&rft_dat=%3Cproquest_ieee_%3E2927685836%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2927685836&rft_id=info:pmid/&rft_ieee_id=10419357&rft_doaj_id=oai_doaj_org_article_57e366ae89b04522b6792001cdfbcaf5&rfr_iscdi=true