JDBIM
(Journal of Digital Business and Innovation Managemen.
Volume 4 No.
December 2025, 268-283 ISSN 2962-3898 (Onlin.
DOI: 10.
26740/jdbim.
https://ejournal.
id/index.
php/jdbmi/i Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis Dewi Ayu Agustina1.
Ika Diyah Candra Arifah2 Department of Digital Business.
Faculty of Economics.
Universitas Negeri Surabaya Jalan Ketintang.
Surabaya 60231.
Indonesia 21050@mhs.
Abstract The integration of Artificial Intelligence (AI) in marketing has gained momentum as businesses seek to enhance customer experience and drive purchase intention.
This study presents a systematic literature review (SLR) using Python-based bibliometric tools to analyze recent scholarly contributions from 2015 to 2025.
extracting and analyzing data from Scopus and Web of Science databases, we identify thematic trends, key contributors, and knowledge gaps in AI-driven marketing literature.
Findings reveal a strong emphasis on personalization, customer engagement, and trust as mediators between AI applications and purchase intention.
This paper provides a conceptual foundation for future empirical and experimental studies in AI marketing.
Keywords: AI in Marketing.
customer experience.
purchase intention.
Received: 1 June 2025.
Accepted: 25 July 2025.
Published: December 2025 *Corresponding author Email: dewi.
21050@mhs.
Abstrak Integrasi kecerdasan buatan (AI) dalam pemasaran semakin berkembang seiring dengan upaya bisnis untuk meningkatkan pengalaman pelanggan dan mendorong niat beli.
Studi ini menyajikan Systematic Literature Review (SLR) dengan menggunakan alat bibliometrik berbasis Python untuk menganalisis kontribusi To cite this document:
Agustina.
, & Arifah.
Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis.
JDBIM (Journal of Digital Business and Innovation Managemen.
Vol.
4 Np.
2, pp.
DOI link Dewi Ayu Agustina.
Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis ilmiah terbaru dari tahun 2015 hingga 2025.
Dengan mengekstraksi dan menganalisis data dari basis data Scopus dan Web of Science, penelitian ini mengidentifikasi tren tematik, kontributor utama, dan kesenjangan pengetahuan dalam literatur pemasaran berbasis kecerdasan buatan (AI).
Temuan ini menunjukkan bahwa personalisasi, keterlibatan pelanggan, dan kepercayaan dalam mediator penting antara penerapan kecerdasan buatan (AI) dan niat beli.
Artikel ini menjadi landasan konseptual bagi studi empiris dan eksperimental selanjutnya dalam bidang pemasaran berbasis kecerdasan buatan (AI).
Kata kunci: Kecerdasan buatan (AI) dalam pemasaran.
pengalaman pengguna.
niat beli.
INTRODUCTION
Artificial Intelligence (AI) has fundamentally transformed the landscape of modern marketing.
Businesses are increasingly leveraging AI technologies to enhance customer engagement through personalized content delivery, intelligent automation, and real-time decision-making.
According to a report by Statista .
, global spending on AI in marketing is projected to reach $107 billion by 2028, up from $27.
4 billion in 2023, reflecting a compound annual growth rate (CAGR) of over 30%.
Additionally, a survey conducted by Salesforce .
revealed that 84% of marketers are already using some form of AI, with common applications including chatbots, predictive analytics, dynamic pricing, and personalized product recommendations.
This transformation is especially prominent in digital marketing, where AIAos ability to process vast amounts of customer data has enabled a more precise understanding of customer needs, preferences, and AI-driven systems are now being used not only to enhance customer experience (CX) but also to influence behavioral intentions, such as purchase decisions, loyalty, and brand trust (Chatterjee et al.
, 2.
Despite the growing relevance of AI in shaping customer journeys, there remains a fragmented understanding of how this body of research has evolved over time, especially in relation to the interconnected concepts of AI, customer experience, and purchase intention.
A systematic and quantitative mapping of the literature is crucial to synthesize knowledge, trace theoretical developments, and identify future research directions.
The academic discourse surrounding artificial intelligence (AI) in marketing has expanded considerably in recent years, with researchers examining its diverse implications for customer behavior, business performance, and market dynamics.
Huang and Rust .
offered a strategic framework detailing how AI integrates across the marketing funnelAifrom awareness to post-purchaseAihighlighting its role in augmenting firm-customer interactions and operational efficiency.
This https://ejournal.
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Volume 4 No.
December 2025
E-ISSN 2692-3898
Page 268-283 strategic view was complemented by Chatterjee et al.
, who empirically demonstrated that AI-driven personalization significantly enhances customer satisfaction and purchase intention.
Their study used structural equation modeling to establish causal pathways, underscoring AIAos role as a mediator between marketing strategy and consumer behavior.
Building on this.
Davenport et al.
explored AIAos influence on marketing performance through a series of corporate case studies.
They found that predictive AI tools improved marketing return on investment (ROI) by refining customer segmentation and optimizing content delivery.
Similarly.
Kumar et al.
introduced a capability-based framework suggesting that organizations equipped with advanced AI capabilities enjoy higher customer retention and marketing agility.
Another stream of literature has focused on the experiential aspect of AI use.
Marinchak et al.
investigated the impact of intelligent automation, such as chatbots and voice assistants, on customer service They concluded that these technologies play a crucial role in shaping trust and reducing transactional friction.
In parallel.
Lemon and VerhoefAos .
foundational work on customer experience in the digital age has served as a theoretical anchor for subsequent AI-related inquiries, especially those involving adaptive interfaces and real-time personalization.
The domain of AI in service and tourism marketing has also seen considerable scholarly attention.
Buhalis and Sinarta .
, for instance, demonstrated that AI-generated content and real-time co-creation tools positively affect touristsAo emotional engagement and decision-making Expanding on the theoretical dimension.
Paschen et al.
proposed a conceptualization of AI as a form of marketing intelligence that enhances human decision-making, thus redefining the boundaries of traditional marketing roles.
Despite these contributions, critical concerns about ethics and transparency remain at the forefront.
Kaplan and Haenlein .
highlighted issues surrounding data privacy, algorithmic bias, and the erosion of consumer trustAifactors that can undermine the effectiveness of AI-based marketing tools.
More recently.
Mogaji et al.
conducted a content analysis of AI marketing campaigns in emerging markets and noted the lack of inclusivity, transparency, and culturally relevant messaging, calling for more context-sensitive approaches in AI design and Taken together, these studies provide valuable insights into how AI is reshaping marketing strategies and customer experiences.
However, what remains underexplored is a systematic synthesis of these contributions over time.
Few studies have mapped the intellectual structure of this domain https://ejournal.
id/index.
php/jdbmi/index Dewi Ayu Agustina.
Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis or visualized the evolving thematic relationships between AI, customer experience, and purchase intention.
Additionally, much of the literature has either focused on single contexts or been conceptual in nature, lacking a data-driven bibliometric approach that could comprehensively reveal publication trends, thematic clusters, and influential scholarly works.
Given these gaps, this study is guided by a series of research questions designed to provide a more holistic understanding of how AI-driven marketing has developed as an academic field between 2018 and 2024.
First, it seeks to examine how literature has evolved over time by analyzing publication trends, citation patterns, and the most prolific contributors to the field.
This will help to capture the growth trajectory and scholarly attention AI has received in the marketing discipline.
Second, the study asks what dominant themes, research methods, and theoretical frameworks have characterized AI-related marketing research, especially those that intersect with customer experience and behavioral intentions such as purchase decision-making.
By investigating this, the study aims to determine the extent to which the literature has been grounded in theory and whether certain methodological approachesAiqualitative, quantitative, or mixedAiare more prevalent.
Third, this research investigates the intellectual and conceptual structure of the field by identifying co-authorship networks, keyword co-occurrences, and thematic clusters using bibliometric visualization techniques.
Understanding these structural relationships will provide insight into the collaborative and thematic landscape of the field.
Lastly, the study seeks to identify theoretical contributions and research gaps that can inform future scholarly exploration.
It asks how current research has advanced or challenged traditional marketing theories and what underdeveloped areasAisuch as emotional AI, ethical concerns, or the role of AI in post-purchase behaviorAiwarrant deeper investigation.
Through these research questions, the study intends to generate a comprehensive, data-driven map of AI-driven marketing scholarship and propose meaningful directions for future inquiry.
The Rise of AI in Marketing Practice The integration of Artificial Intelligence (AI) into marketing strategy has reshaped how firms understand and engage with customers, emphasizing personalization, real-time decision-making, and data-driven insights.
As AI becomes more embedded in business operations, it has also become central to understanding customer experience (CX) and behavioral responses such as purchase intention.
AIAos ability to process large-scale, unstructured customer data enables marketers to generate insights and develop hyper-personalized marketing communications, which are crucial https://ejournal.
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Volume 4 No.
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Page 268-283 for fostering positive experiences and driving purchase behavior (Huang & Rust, 2021.
Chatterjee et al.
, 2.
Personalization and Customer Engagement Personalization remains a dominant theme in AI-driven marketing Studies such as Kumar et al.
suggest that personalization powered by AI algorithms enhances customer satisfaction and engagement by delivering content that matches individual preferences.
For instance, recommender systems and predictive analytics can anticipate what a customer may want next, reducing decision fatigue and increasing conversion rates.
Similarly.
AI-enabled dynamic pricing, content targeting, and automated customer support .
, via chatbots or voice assistant.
contribute significantly to customersAo perceptions of service quality (Davenport et al.
, 2020.
Marinchak et al.
, 2.
AIAos Impact on Customer Experience Customer experience is another core area where AIAos impact has been extensively studied.
Lemon and Verhoef .
emphasized that customer experience in the digital age is multi-touchpoint and dynamic.
Subsequent research has extended their work by incorporating AI as a mediating technology that shapes how customers interact with brands across digital AIAos role in real-time interaction, for example, through conversational agents, has been shown to reduce perceived waiting times, build emotional connection, and enhance service satisfaction (Paschen et , 2.
In sectors such as tourism and hospitality.
AI-curated travel recommendations and real-time itinerary management have been found to deepen emotional engagement and trust, thus influencing booking intentions (Buhalis & Sinarta, 2.
AI and Purchase Intention When it comes to purchase intention, empirical studies have shown that AI applications that optimize information relevance, visual appeal, and responsiveness tend to increase consumersAo willingness to buy.
Chatterjee et al.
demonstrated that AI-driven personalization directly influences purchase intention through the mediating effects of perceived value and Other studies highlight that perceived usefulness and ease of useAicore constructs of the Technology Acceptance Model (TAM)Aialso play a critical role in AI adoption and its subsequent effect on behavioral intentions (Kaplan & Haenlein, 2.
Ethical Concerns and Emerging Critiques Nevertheless, not all outcomes are positive.
Scholars such as Mogaji et al.
and Kaplan and Haenlein .
raised concerns about overpersonalization, algorithmic bias, and data privacy, which can erode https://ejournal.
id/index.
php/jdbmi/index Dewi Ayu Agustina.
Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis customer trust.
These concerns are particularly salient in emerging markets, where technological literacy and regulatory frameworks vary significantly.
Such ethical and psychological concerns may create cognitive dissonance, thus negatively moderating the relationship between AI interaction and purchase intention.
METHODS
Data Source and Search Strategy To ensure comprehensive coverage of relevant scholarly literature, this study sourced peer-reviewed journal articles from two of the most reputable academic databases: Scopus and Web of Science (WoS).
These databases were selected due to their broad disciplinary coverage, highquality indexing, and compatibility with bibliometric analysis tools.
structured keyword search was employed to retrieve literature focusing on the intersection of artificial intelligence (AI), marketing, customer experience, and purchase intention.
The following Boolean search query was applied in both databases:
(AuArtificial IntelligenceAy OR AuAIA.
AND (AuMarketingAy OR AuDigital MarketingA.
AND (AuCustomer ExperienceAy OR AuPurchase IntentionA.
This query was intentionally broad yet focused to capture a wide range of studies that explicitly examine how AI influences customer experience and/or purchase intention in marketing contexts.
The search was further refined by limiting the publication period from 2015 to 2025, ensuring the inclusion of recent and relevant research that aligns with the rapid development and deployment of AI in marketing over the past six Only peer-reviewed journal articles written in English were considered, to maintain consistency in quality and accessibility.
Conference proceedings, editorials, book chapters, and non-English texts were excluded to minimize noise in the dataset and focus on rigorously reviewed scholarly contributions.
Data Extraction and Processing Once the search strategy was executed, the metadata from all selected articlesAiincluding authors, titles, abstracts, keywords, journal sources, and citation countsAiwas exported in BibTeX format, a structured file format commonly used for bibliographic data.
This metadata formed the basis of the bibliometric analysis.
The subsequent data processing and analysis were conducted using a Python-based environment, leveraging several libraries designed for natural language processing, data transformation, and network visualization.
https://ejournal.
id/index.
php/jdbmi/index JDBIM (Journal of Digital Business and Innovation Managemen.
Volume 4 No.
December 2025
E-ISSN 2692-3898
Page 268-283 First, the pandas library was used to load, clean, and transform the data into a structured format suitable for analysis.
This included standardizing author names, resolving duplicate entries, and filtering out incomplete records.
Then, to extract thematic insights and keyword patterns from abstracts and titles, natural language processing (NLP) tools such as spaCy and scispaCy were applied.
These libraries allowed for part-ofspeech tagging, named entity recognition, and extraction of noun phrases relevant to AI and marketing discourse.
For visualization, matplotlib and seaborn were employed to create trend graphs, distribution plots, and thematic heatmaps.
Finally, to explore the intellectual structure of the field, networkx was utilized to map co-authorship networks and keyword cooccurrence graphs.
These visualizations revealed clusters of collaboration among scholars and thematic groupings in the literature, contributing to the identification of emerging research fronts and core contributors.
Inclusion and Exclusion Criteria A set of inclusion and exclusion criteria was established to ensure the relevance and quality of the final dataset.
Articles were included if they explicitly investigated the impact of AI on customer experience or purchase intention, either through empirical analysis or conceptual exploration.
This focus ensured alignment with the studyAos core objectives.
On the other hand, studies that were tangential to marketingAisuch as those dealing with robotics in industrial manufacturing or AI in healthcare diagnosticsAiwere excluded, as they did not align with the thematic scope.
Non-peer-reviewed content, including conference abstracts, white papers, and preprints, was also omitted to maintain the academic rigor of the analysis.
Through this methodological approach, the study assembled a robust and focused dataset, enabling a systematic and reproducible exploration of how AIdriven marketing has evolved between 2015 and 2025.
This framework sets the foundation for the bibliometric analysis and thematic mapping presented in the subsequent sections.
RESULT AND DISCUSSION
Descriptive Analysis The systematic literature review yielded a total of 500 peer-reviewed journal articles that met the inclusion criteria and were published between 2015 and 2025.
These articles represent a steadily growing scholarly interest in the intersection of artificial intelligence (AI), marketing, customer experience, and purchase intention.
The majority of the studies were published in high-impact journals, with the Journal of Business Research, https://ejournal.
id/index.
php/jdbmi/index Dewi Ayu Agustina.
Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis Computers in Human Behavior, and the Journal of Retailing and Consumer Services emerging as the most frequent publication outlets.
This concentration highlights the interdisciplinary nature of the topic, spanning fields such as marketing, psychology, and information systems.
In terms of geographic distribution, the most prolific contributors to this field were researchers based in the United States.
China.
India, and the United Kingdom.
These countries accounted for the highest volume of publications and citations, suggesting that the global academic landscape of AI-driven marketing is dominated by technologically advanced and economically influential regions.
Nevertheless, there is growing participation from emerging economies, although contributions remain relatively limited in scope and volume.
Keyword Co-occurrence Network The Python-based bibliometric analysis of keyword co-occurrence revealed three dominant clusters within the AI-marketing literature, each representing a thematic concentration.
The keyword co-occurrence analysis reveals three major thematic clusters that dominate the current literature on AI-driven marketing.
# Keyword co-occurrence clustering using NetworkX built-in louvain_communities .
o external instal.
import pandas as pd, re, matplotlib.
pyplot as plt, networkx as nx from sklearn.
feature_extraction.
text import ENGLISH_STOP_WORDS from itertools import combinations from collections import Counter text_series = df_results['Title'].
fillna('') '.
df_results['Abstract'].
fillna('') def tokenize.
tokens = re.
,}', text.
lower()) tokens = .
for t in tokens if t not in ENGLISH_STOP_WORDS] return list.
) co_occurrence = {} for doc in text_series:
toks = tokenize.
for w1, w2 in combinations.
oks, .
pair = tuple.
1, w.
)) co_occurrence.
= co_occurrence.
air, .
1 G = nx.
Graph() for .
1, w.
, wt in co_occurrence.
items():
if wt >= 3:
add_edge.
1, w2, weight=w.
# Community detection from networkx.
community import louvain_communities communities = louvain_communities(G, weight='weight', seed=.
# Identify three largest clusters https://ejournal.
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Page 268-283 communities_sorted = sorted.
ommunities, key=len, reverse=Tru.
[:.
cluster_id_map = {} for idx, comm in enumerate.
ommunities_sorte.
for node in comm:
cluster_id_map.
= idx # Subgraph of top communities sub_nodes = list.
luster_id_map.
keys()) H = G.
ub_node.
# Plot colors = .
luster_id_map.
for n in H.
nodes()] figure.
) pos = nx.
spring_layout(H, seed=1, k=0.
draw_networkx_nodes(H, pos, node_size=70, node_color=colors, cmap='tab10') draw_networkx_edges(H, pos, alpha=0.
# Label only high-degree nodes for readability labels = .
:n for n in H.
nodes() if H.
> .
draw_networkx_labels(H, pos, labels, font_size=.
title('Keyword Co-occurrence Network: Three Dominant Clusters') axis('off') # Table: top 7 keywords per cluster by degree rows = [] for idx, comm in enumerate.
ommunities_sorte.
words_sorted = sorted.
, key=lambda w: G.
, reverse=Tru.
[:.
append({'Cluster': 'Cluster ' str.
, 'TopKeywords':
', '.
ords_sorte.
}) top_kw_df = pd.
DataFrame.
op_kw_d.
Figure 1.
Keywords co-occurrence analysis and clustering The first and most prominent is the AuCustomer-Centric Service & ExperienceAy cluster.
This theme is characterized by the frequent cooccurrence of terms such as customer, experience, service, marketing, driven, and intelligence.
These keywords collectively underscore a growing academic interest in how data-driven and AI-powered approaches are reshaping customer experience and service quality.
The large node sizesAi indicating high degree counts in the networkAisuggest that this cluster forms the conceptual backbone of much of the current scholarship in the The second thematic group focuses on AuConsumer Purchase Intention & Behaviour.
Ay Within this cluster, dominant terms such as purchase, consumer, intention or intentions, role, and study frequently appear Research in this area typically investigates the psychological, social, and contextual factors that influence consumersAo decisions to make Many of the studies in this cluster rely on survey data or https://ejournal.
id/index.
php/jdbmi/index Dewi Ayu Agustina.
Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis experimental designs.
The tightly interconnected nature of the terms in this cluster reveals a dense and well-established conceptual community in which these variables are frequently studied together.
A third, more niche but distinct cluster centers around AuAugmented Reality Adoption.
Ay Although smaller in size, this cluster stands out for its focus, with terms like reality and augmented appearing as a tightly linked clique.
Despite the limited number of terms, the strength of their connections suggests a growing and recognisable subfield concerned with the application of augmented reality in marketing and retail contexts.
The identification of these three clusters results from the application of the Louvain algorithm, which grouped approximately 280 keywords into communities after filtering out edges that occurred in fewer than three papers as written in Figure 1.
These three clusters emerged as the largest in terms of membership and conceptual Figure 2.
Intersections of AI-Driven Marketing Themes This analysis serves several useful purposes for researchers and practitioners alike.
First, it enables topic mapping, offering a quick overview of the dominant themes within the literature and the relative breadth or specificity of each.
Second, it aids in gap identification, for instance, the apparent lack of strong connections between augmented reality and customer experience points to a potentially underexplored area ripe for future research.
Lastly, the analysis provides insights into keyword refinement strategies.
Authors aiming to increase their article visibility may benefit from incorporating high-centrality terms from within these clusters to improve searchability and indexing in academic databases.
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Page 268-283 Figure 3.
Publication Trend per Cluster The trend chart illustrates a clear dominance of research output in the AuCustomer-Centric Service & ExperienceAy cluster, which continues to grow steadily year by year.
This thematic area remains the most prolific across the timeline and shows no signs of decline, indicating a sustained academic and practical interest in how AI technologies enhance service quality and customer experience.
Closely following is the AuPurchase Intention & Consumer BehaviourAy cluster, which has consistently produced a substantial volume of studies.
However, beginning around 2024 and extending into 2025, a slight taper in output becomes noticeable, suggesting either saturation or a temporary shift in scholarly focus.
In contrast, the third cluster, focusing on AuAugmented Reality (AR) Adoption,Ay remains significantly smaller in volume.
The limited number of publications in this area highlights it as an underexplored niche within the broader field of AIdriven marketing, offering considerable potential for future research and To better understand the thematic essence of each major cluster, representative titles were examined.
For Cluster 1, which centers on customer-centric service and experience, the most frequently matched papers include works like A Strategic Framework for Artificial Intelligence in Marketing.
Engaged to a Robot? The Role of AI in Service, and Consumers and Artificial Intelligence: An Experiential Perspective.
These titles emphasize the growing role of AI in shaping customer interactions and transforming service landscapes.
Other papers, such as The Metaverse in the Hospitality and Tourism Industry and Transforming the Customer Experience through New Technologies, reflect a strong forward-looking orientation within this cluster.
Cluster 2, which revolves around purchase intention and consumer behaviour, includes a variety of titles focused on trust, risk perception, and decision-making in online and AI-mediated https://ejournal.
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php/jdbmi/index Dewi Ayu Agustina.
Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis Examples include The Impact of Social Media Influencers on Travel Decisions: The Role of Trust in the Consumer Decision Journey, and The Impact of Perceived Usefulness of Online Reviews.
Trust, and Perceived Risk on Online Purchase Intention in Emerging Markets.
Additional studies like Artificial Intelligence in Advertising: How Marketers Can Leverage Artificial Intelligence Along the Consumer Journey highlight the intersection of AI technologies and consumer psychology, particularly in digital contexts.
As for the augmented reality cluster (Cluster .
, no papers were strongly matched under the original keyword overlap criteria due to the smaller size of the cluster and the stricter threshold applied during analysis.
Nevertheless, when the edge threshold was lowered to include links appearing in just two papers, smaller niche clustersAiincluding AR-related studiesAibegan to emerge more visibly.
This technical adjustment uncovers latent thematic threads within the dataset and suggests that while ARrelated marketing research is currently limited in volume, it may be quietly gaining momentum beneath the surface.
Thematic Trends The longitudinal trend analysis of topic clusters from 2015 to 2025 reveals distinct trajectories in academic interest related to AI-driven As illustrated in Figure 1.
AI in Post-Purchase processes has emerged as the most rapidly expanding theme, peaking sharply in 2023 with 13 publications.
This surge reflects increasing scholarly attention toward AIAos role in customer retention, satisfaction, and after-sales engagement, which are critical in service-dominated digital ecosystems.
While its momentum slightly dipped in 2024, a subsequent rebound in 2025 suggests a sustained relevance.
In contrast.
Emotional AI and Ethical Concerns demonstrate more moderate and fluctuating publication patterns.
Emotional AI experienced a gradual climb, reaching a high in 2023, before tapering off.
This indicates a maturing interest in the affective dimensions of AI systems, especially those interfacing directly with consumers.
Ethical Concerns, while generally trailing behind in volume, show a noticeable rise starting in 2022, maintaining stability into 2024.
This coincides with broader global discussions on data privacy, algorithmic bias, and user autonomy Ai themes likely amplified by regulatory and societal pressures.
The co-occurrence network maps further unpack these thematic clusters by revealing keyword a Theme 1 (Emotional AI) shows a dense web centered on terms such as "consumer," "purchase intention," and "experience", highlighting its conceptual linkage to personalized engagement and emotional intelligence in customer service.
The presence of https://ejournal.
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Page 268-283 "driven," "e-commerce," and "user modeling" suggests a technological orientation anchored in digital commerce.
a Theme 2 (Ethical Concern.
is anchored around "satisfaction," "service," and "user", but also includes abstract terms like "this" and "for" that point to nuanced or loosely defined ethical debates in the Notably, terms such as "driven" and "customer" still dominate, underscoring the enduring customer-centric framing even in ethics discourse.
a Theme 3 (AI in Post-Purchas.
reveals a tightly interlinked structure revolving around "customer satisfaction," "brand," "purchase," and "intention".
This confirms a pragmatic focus on brand loyalty and the extended influence of AI beyond the point of The presence of "experience" and "driven" again shows consistency in framing across clusters, yet here it is particularly aligned with actionable business outcomes.
Collectively, these findings indicate a thematic evolution from affective and ethical explorations of AI toward a more outcome-oriented, retention-driven application of AI technologies.
While emotional resonance and ethical safeguards remain important, the data suggests that scholars and practitioners are increasingly prioritizing post-purchase engagement and brand sustainability as critical frontiers for AI implementation in marketing.
These shifts also reflect larger industry trends, where competitive advantage is no longer just about conversion but also about cultivating longterm consumer relationships through AI-enhanced personalization and satisfaction analytics.
However, the parallel rise of ethical concerns underscores a cautionary counter-narrative: as AI becomes more embedded in consumer journeys, ensuring fairness, transparency, and trust will be essential for sustainable deployment.
https://ejournal.
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Ika Diyah Candra Arifah Unveiling the Impact of AI-Driven Marketing on Customer Experience and Purchase Intention: A Systematic Literature Review Using Python-Based Bibliometric Analysis Figure 4.
Three Main Topics Over Time and Keywords Occurrences CONCLUSION This systematic literature review demonstrates that AI-driven marketing plays a significant role in shaping customer experience and influencing purchase intention.
Python-based bibliometric tools prove effective in revealing the structural patterns of research trends and themes.
The findings serve as a foundation for future empirical studies and responsible innovation in AI-powered customer engagement strategies.
This study is limited to publications from two databases and Englishlanguage articles.
Future work could expand to include grey literature and apply text mining for full-text analysis.
Furthermore, future research should explore experimental designs and cross-cultural validation of AI adoption REFERENCES