Academic Journal
AI for Customer Journeys: A Transformer Approach.
| Τίτλος: | AI for Customer Journeys: A Transformer Approach. |
|---|---|
| Συγγραφείς: | Lu, Zipei (AUTHOR), Kannan, P.K. (AUTHOR) pkannan@umd.edu |
| Πηγή: | Journal of Marketing Research (JMR). Feb2026, Vol. 63 Issue 1, p1-26. 26p. |
| Θεματικοί όροι: | *Artificial intelligence, *Customer experience, *Transformer models, Language models, Long short-term memory, Hidden Markov models |
| Περίληψη: | When analyzing a sequence of customer interactions, it is important for firms to understand how these interactions align with key objectives, such as generating qualified customer leads, driving conversion events, or reducing churn. The authors introduce a transformer-based framework that models customer interactions in a sequence similar to how a sentence is modeled as a sequence of words by large language models. They propose a heterogeneous-mixture multihead self-attention mechanism that captures individual heterogeneity in touchpoint effects. The model identifies self-attention patterns that reflect both population-level trends and the unique relationships between touchpoints within each customer journey. By assigning varying weights to each attention head, the model accounts for the distinctive aspects of the journey of each user. This results in more accurate predictions, enabling precise targeting and outperforming existing approaches such as hidden Markov models, point process models, and long short-term memory (LSTM) models. This empirical application in a multichannel marketing context demonstrates how managers can leverage the model's features to identify high-potential customers for targeting. Extensive simulations further establish the model's superiority over competing approaches. Beyond multichannel marketing, the transformer-based model also has broad applicability in customer journeys across other domains. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Marketing Research (JMR) is the property of American Marketing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: AI for Customer Journeys: A Transformer Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Zipei%22">Lu, Zipei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kannan%2C+P%2EK%2E%22">Kannan, P.K.</searchLink> (AUTHOR)<i> pkannan@umd.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Marketing+Research+%28JMR%29%22">Journal of Marketing Research (JMR)</searchLink>. Feb2026, Vol. 63 Issue 1, p1-26. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Customer+experience%22">Customer experience</searchLink><br />*<searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: When analyzing a sequence of customer interactions, it is important for firms to understand how these interactions align with key objectives, such as generating qualified customer leads, driving conversion events, or reducing churn. The authors introduce a transformer-based framework that models customer interactions in a sequence similar to how a sentence is modeled as a sequence of words by large language models. They propose a heterogeneous-mixture multihead self-attention mechanism that captures individual heterogeneity in touchpoint effects. The model identifies self-attention patterns that reflect both population-level trends and the unique relationships between touchpoints within each customer journey. By assigning varying weights to each attention head, the model accounts for the distinctive aspects of the journey of each user. This results in more accurate predictions, enabling precise targeting and outperforming existing approaches such as hidden Markov models, point process models, and long short-term memory (LSTM) models. This empirical application in a multichannel marketing context demonstrates how managers can leverage the model's features to identify high-potential customers for targeting. Extensive simulations further establish the model's superiority over competing approaches. Beyond multichannel marketing, the transformer-based model also has broad applicability in customer journeys across other domains. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Marketing Research (JMR) is the property of American Marketing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00222437251347268 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Customer experience Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Language models Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Hidden Markov models Type: general Titles: – TitleFull: AI for Customer Journeys: A Transformer Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Zipei – PersonEntity: Name: NameFull: Kannan, P.K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00222437 Numbering: – Type: volume Value: 63 – Type: issue Value: 1 Titles: – TitleFull: Journal of Marketing Research (JMR) Type: main |
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