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AI in customer experience and digital marketing: a bibliometric and thematic review (2004–2025) with sensory marketing lens

Yahia Mouammine

Cogent Business & Management2026https://doi.org/10.1080/23311975.2026.2613596article
AJG 1ABDC C
Weight
0.37

What the paper says

This study synthesizes two decades of research (2004–2025) on artificial intelligence (AI) in customer experience (CX) and digital marketing, with attention to the extent to which sensory marketing cues—visual, auditory, haptic, olfactory, and gustatory—are addressed. A structured search of the Scopus database was conducted, limited to English-language peer-reviewed articles, reviews, and conference papers published between 2004 and 2025. After de-duplication and screening, 191 records were retained and exported into Zotero. Bibliometric analyses mapped annual publication trends, outlets, and author/keyword co-occurrence. A rule-based content analysis of abstracts coded each study by sector, AI modality, channel, customer journey stage, outcomes, study design, region, data source, sample size, and sensory cues, using a transparent dictionary and multi-label mapping procedure, with ambiguous fields resolved through cross-checking. The corpus is dominated by applications in e-commerce/retail, banking/finance, and tourism/hospitality. Research prioritizes conversational AI, recommender systems, and NLP/sentiment analysis, with fewer studies on AR/VR/XR, computer vision, IoT + AI, or robotics. Sensory treatment is skewed toward visual proxies (e.g. AR try-on, interface design, image analytics), while auditory, haptic, olfactory, and gustatory cues are rarely considered. Outcomes center on satisfaction, engagement, purchase/conversion, and loyalty, with trust and privacy emerging as cross-cutting themes. Methodologies range from surveys/SEM and experiments to case-based technical models. This is the first review to combine an AI-in-CX synthesis with a sensory marketing lens. It shows where sensory dimensions are operationalized (mainly visual) and where gaps persist, while offering a reproducible coding framework and a research agenda to guide future inquiry.

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https://doi.org/https://doi.org/10.1080/23311975.2026.2613596

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@article{yahia2026,
  title        = {{AI in customer experience and digital marketing: a bibliometric and thematic review (2004–2025) with sensory marketing lens}},
  author       = {Yahia Mouammine},
  journal      = {Cogent Business & Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/23311975.2026.2613596},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.