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...
Gespeichert in:
Veröffentlicht in: | IEEE access 2024, Vol.12, p.22394-22402 |
---|---|
Hauptverfasser: | , , , |
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 & 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 |