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Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective
Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Purpose: Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP. Design/methodology/approach: Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas. Findings: AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions. Research limitations/implications: New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda. Practical implications: This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives. Originality/value: This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

Artificial intelligence, Artificial intelligence-enabled personalization, Customer journey, Interactive marketing
2040-7122
1-18
Gao, Youjiang
d2a2feba-1db5-41ab-84d7-553b96838a4c
Liu, Hongfei
7d65edcf-20c9-452a-83c2-8b545b12f68c
Gao, Youjiang
d2a2feba-1db5-41ab-84d7-553b96838a4c
Liu, Hongfei
7d65edcf-20c9-452a-83c2-8b545b12f68c

Gao, Youjiang and Liu, Hongfei (2022) Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 1-18. (doi:10.1108/JRIM-01-2022-0023).

Record type: Article

Abstract

Purpose: Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP. Design/methodology/approach: Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas. Findings: AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions. Research limitations/implications: New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda. Practical implications: This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives. Originality/value: This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

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Accepted/In Press date: 27 June 2022
e-pub ahead of print date: 15 July 2022
Published date: 15 July 2022
Additional Information: Funding Information: Funding declarations: This work was funded by the National Office for Philosophy and Social Sciences, China, under grant no. 20ZDA087. Publisher Copyright: © 2022, Emerald Publishing Limited.
Keywords: Artificial intelligence, Artificial intelligence-enabled personalization, Customer journey, Interactive marketing

Identifiers

Local EPrints ID: 468617
URI: http://eprints.soton.ac.uk/id/eprint/468617
ISSN: 2040-7122
PURE UUID: 389cb3e6-5262-41e6-a6c2-fcc7d8ddd6e4
ORCID for Hongfei Liu: ORCID iD orcid.org/0000-0001-8539-9054

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Date deposited: 18 Aug 2022 17:09
Last modified: 17 Mar 2024 04:01

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Contributors

Author: Youjiang Gao
Author: Hongfei Liu ORCID iD

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