With the digital transformation process, marketing communication tools have evolved radically, and data-driven strategies have come to the fore in marketing and advertising
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. This transformation has caused traditional media planning to be replaced by big data-based, automated, and real-time applications. Programmatic purchasing methods, especially in the field of digital advertising, stand out with their ability to deliver advertising content suitable for the target audience at the right time and place, allowing businesses to reach their target audiences more effectively and efficiently
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. Thanks to big data technologies, brands can perform micro-level customer segmentation by analyzing structured and unstructured data including user behavior, demographic information, social media interactions, and purchase history
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. This data allows marketing campaigns to be personalized and the return on advertising spending to be increased. The use of artificial intelligence and machine learning-based algorithms in the segmentation process offers significant advantages in terms of accurate targeting and meaningful insight generation
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. Programmatic purchasing, on the other hand, optimizes processes and provides efficiency in resource use by automating the purchase of advertising inventory by combining these analyses with real-time data
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. One of the key elements of success in programmatic advertising is the accuracy of big data-supported customer segmentation. Correctly determining the target audience and presenting content appropriate to these segments directly affects the efficiency of programmatic purchasing strategies
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. At this point, chatbot technologies, which have become an important tool in enriching the digital customer experience, come into play. Chatbots are interaction tools developed with natural language processing (NLP) and machine learning techniques that help determine customer expectations, behavioral patterns and interests by communicating with users in real time
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. These tools play a strategic role not only in customer service but also in data collection, analysis and personalization processes.
The data obtained through chatbots makes customer segmentation processes more dynamic and up-to-date, while contributing to the optimization of programmatic purchasing strategies
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. Thanks to chatbot-based user interactions, retargeting campaigns supported by real-time data can be created, which enables ads to work with higher conversion rates
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This study examines the impact of big data-based customer segmentation on programmatic purchasing efficiency in a multidimensional manner
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. In particular, the integration of chatbot-supported data collection and interaction mechanisms into these processes is analyzed, and the potential to increase the effectiveness of data-driven decision-making mechanisms in marketing communication processes is evaluated. In this context, the place of chatbot technologies in strategic marketing processes is addressed in a holistic manner by adopting a methodological approach based on literature review, case studies and data-based application analysis
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2. Big Data and Segmentation
Big data is an analytical approach that enables the production of meaningful information by processing high-volume, fast and diverse data sets
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. In the context of marketing, big data enables more precise structuring of customer segmentation with dynamic and behavioral elements
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Programmatic purchasing is the automatic purchase of digital advertising spaces through software-based systems
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. In these systems, algorithms ensure that advertisements are displayed in accordance with the digital profile of the target audience
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Chatbots are digital assistants that interact with users in writing or by voice using artificial intelligence and natural language processing (NLP) techniques
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. They are widely used especially in e-commerce, banking and customer service
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. Chatbots offer significant contributions in terms of real-time collection and analysis of customer data
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3. The Effect of Big Data-Supported Segmentation on Programmatic Purchasing
The technological transformation experienced in the field of digital marketing has made the concept of big data a strategic element in the processes of collecting, analyzing and transforming consumer data into action
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. Since programmatic purchasing systems, in particular, aim to present the most appropriate content to the target audience at the right time and on the right platform, segmentation quality plays a decisive role in the success of these systems
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. While traditional segmentation models are generally shaped by limited demographic data, customer profiling has become more comprehensive thanks to big data, including behavioral, temporal, contextual and emotional dimensions. In this regard, the impact of big data-supported segmentation on programmatic purchasing processes can be evaluated in two main dimensions: deepening segmentation and dynamic segmentation with real-time data
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3.1. Deepening the Segmentation
Big data technologies make it possible to analyze customer data more comprehensively, not only in terms of volume but also variety and speed
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. Thanks to these technologies, many parameters related to the consumer
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—such as browsing history, device usage, social media interactions, location information, purchase frequency, interests, and even sentiment analysis—are evaluated in an integrated manner
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. Multi-dimensional segmentation can be performed. Thus, large audiences that were previously considered homogeneous can now be separated at a micro level, and specific behavioral patterns of each sub-segment can be identified
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. Deepened segmentation enables marketing communications to be made more personal, relevant, and contextual. For example, analyzing users who are active on e-commerce sites but have not made a purchase, and creating an “undecided consumer” segment makes it possible to develop persuasive and transformative messages for this group
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. Such strategic micro-segments allow for more efficient advertising spending in programmatic purchasing systems. Because deep segmentation increases conversion rates by ensuring that ad impressions are focused not only on large audiences but also on narrow groups that are more likely to purchase
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. In addition, updating these segments over time through machine learning algorithms increases the flexibility of marketing strategies against changes in consumer behavior. This shows that segmentation deepened with big data is not static, but has a learning and evolving structure
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3.2. Dynamic Segmentation with Real-Time Data
One of the most important advantages of programmatic advertising is that campaigns can be optimized in real time thanks to instant data flow. However, for this function to work effectively, segmentation structures must also be sensitive to real-time data
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. Dynamic segmentation not only analyzes user behavior based on history; it also allows redefining which segment users will be included in by taking into account instant interactions, online activities and contextual factors (e.g. time, location, season, device type)
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Thanks to this structure, it is possible to target different advertising content at different times of the day by recognizing that a user may be searching for information in the morning and tend to shop in the afternoon. This flexibility directly increases not only the visibility of the advertisement but also its level of impact. Because presenting the user with the “right message, at the right time, in the right context”; maximizes the effectiveness of marketing communications
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Another advantage of real-time dynamic segmentation is that it reduces “intra-segment behavior variance”. In other words, while users who exhibit similar behaviors in different contexts are included in the same segment, this variance can be eliminated with instant data
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. For example, if a user who is inclined to shop on weekends interacts with different content on weekdays, the advertising content to be presented to this user should be shaped according to instant behavior. Dynamic segmentation makes this possible, thus increasing the effectiveness of advertising investments
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Segmentation systems that work with real-time data work in accordance with the nature of programmatic advertising; they optimize ad display not only according to the user profile, but also according to contextual awareness. This situation both improves the user experience and increases the commercial return of marketing activities
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. Big data-supported segmentation is the cornerstone of programmatic purchasing strategies. Segmentation structures that gain depth and become dynamic increase both the accuracy rate and flexibility of marketing strategies; thus, contributing to both the cost-effectiveness and customer satisfaction of digital advertising. In this context, segmentation should no longer be considered only as an analysis tool, but also as a strategic competence that creates competitive advantage
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4. The Role of Chatbot-Based Interactions
Artificial intelligence-based applications are increasingly playing critical roles in the data-driven transformation of digital marketing. In this transformation process, chatbot technologies stand out as an active component not only for the purpose of automating customer services, but also in the processes of collecting, analyzing, and personalizing marketing strategies
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. Chatbots, thanks to their ability to communicate with customers in two-way and real-time, both increase operational efficiency and strengthen the knowledge base of strategic marketing decisions. In this context, the contributions of chatbots to marketing processes can be addressed under three main headings: data collection and enrichment, customer intent prediction and segment development, personalized offers, and retargeting
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4.1. Data Collection and Enrichment
Customer data, which is difficult to obtain with traditional methods, can be collected more naturally and voluntarily thanks to chatbots
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. Requests, questions, complaints and behavioral responses conveyed by users during the chat provide rich insights that can be directly integrated into data analytics processes
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. These interactions produce both structured and unstructured data; this data can be processed with natural language processing (NLP) techniques and transformed into meaningful information sets. The constant activity of chatbots in digital channels ensures that data is collected instantly and marketing databases are kept up to date
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Chatbots also have the capacity to collect a wide range of contextual data (such as the user’s location, device type, and previous interaction history)
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. Such contextual data allows for more nuanced groupings in segmentation processes. Thus, customer profiling becomes more accurate and actionable; and marketing campaigns have the opportunity for more effective targeting
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4.2. Prediction of Customer Intention and Segment Development
Chatbots produce interactive data that indirectly reveals customers’ interests, doubts, satisfaction levels, and purchase tendencies regarding products or services. When this data is analyzed with machine learning algorithms, it is possible to make strong predictions about customer intention
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. For example, a user who frequently inquires about certain products, researches campaign conditions or compares prices can be classified as a high-potential purchase candidate
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. In this context, chatbots not only provide data to existing segmentation models; they also contribute to the creation of dynamic and behavioral segments
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. The evolution of segmentation from a static structure to a structure that is constantly updated in a way that is sensitive to user intent and context enables programmatic purchasing systems to target more accurately. Thus, marketing strategies gain a predictive and personalized structure
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4.3. Personalized Offers and Retargeting
Data and insight-based interactions provided by chatbots provide a significant resource for personalizing marketing campaigns
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. User-specific offers, recommendations based on past interactions, and personalized campaign content can be delivered directly through chatbots. This improves the customer experience, strengthens brand loyalty, and increases conversion rates
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. Chatbots also serve a strategic function in terms of retargeting strategies. For example, if a user interacts with a chatbot and receives product information but has not completed the purchase, this information can be used to present the user with a personalized reminder message or a discount offer
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. Such personalized follow-up strategies enable the evaluation of potential interactions that do not convert into sales. The integration of chatbots with retargeting algorithms is particularly well-suited to the dynamic nature of programmatic purchasing systems
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5. Strategic Implementations and Implementation Recommendations
A multidimensional approach has been taken to address the impact of big data-based customer segmentation on programmatic purchasing efficiency in the data-based transformation process of digital marketing and how chatbot-based interactions contribute to this process
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. Research findings indicate that in today’s digital marketing ecosystem, in order for businesses to gain competitive advantage, they must move away from the traditional understanding that focuses only on data collection and reach a maturity level that includes extracting meaning from data and taking action
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Achieving data-based marketing maturity is possible not only with technical infrastructure investments of organizations, but also by internalizing the culture of analytical thinking
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. In this context, the integration of large data warehouses with chatbot systems enables real-time analysis of customer data, making strategic decision processes more accurate
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. Not only collecting data, but also interpreting it and transforming it into customer insights directly contributes to the personalization of marketing activities.
The interactive data sources provided by chatbots increase the accuracy of customer segmentation and provide data production at every stage of users’ digital journeys. Beyond this, the fact that chatbots assume analytical roles enables segmentation processes to become more in-depth and dynamic
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. Thus, chatbots are not only tools that automate operational processes, but also function as part of decision support systems
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The success of programmatic advertising is closely related to the direct integration of data obtained from customer segmentation into purchasing systems
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. In this context, the integration of insights obtained from big data analytics and chatbot interactions into the programmatic ecosystem directly supports the success of omnichannel marketing strategies
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. Data-driven integration processes increase the effectiveness and return on investment (ROI) of advertising expenditures, as well as ensuring the continuity of the customer experience
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. The integration of big data-supported customer segmentation, chatbot-based customer interactions and programmatic purchasing systems reveals the necessity of developing a holistic strategy in digital marketing applications. This integrative approach will not only increase operational efficiency but also customer satisfaction and brand loyalty
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. In future studies, examining this relationship network within the framework of interaction models in different sectors and user groups will provide valuable contributions to the academic literature and the application world
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Considering chatbots not only as customer service tools but also as analytical elements increases the quality of segmentation processes
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Integrating the results of chatbot-based interactions into programmatic systems with big data analytics directly affects the success of omnichannel marketing strategies
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6. Method
In this study, a qualitative research design was adopted to examine the relationship between big data-based customer segmentation and productivity increase in programmatic purchasing processes and to evaluate the integration of chatbot-based customer interactions into this process. Two main methods were used in the research: systematic literature review and multiple case analysis.
Literature Review Strategy
The literature review was conducted based on academic publications, industry reports and technical documents published between 2019 and 2025. Articles in peer-reviewed journals included in indexes such as Scopus, Web of Science, Google Scholar and ScienceDirect were taken into consideration during the review process. The keyword combinations are as follows:
As inclusion criteria:
Priority was given to studies reporting the relationship between 1) big data applications in direct digital marketing processes,
2) use of chatbot technologies in marketing interactions,
3) programmatic media investments and ROI (Return on Investment).
As a result of the literature review, some studies were examined in detail.
7. Limitations
Although the findings presented in this study are supported by sectoral reports and secondary data, some limitations should be emphasized in order to evaluate the scope of the study more accurately:
Lack of Empirical Tests
No original experimental or longitudinal dataset was used in this study. While secondary data and case studies are sufficient to show general trends, they are limited in terms of directly proving the causal relationship between segmentation quality and programmatic efficiency.
Data Privacy and Access Limitations
Many businesses keep their customer segmentation algorithms and chatbot interaction data secret. This restricts access to comprehensive datasets and can cause publication bias in published case studies. In addition, legal regulations such as KVKK, GDPR, CCPA limit the scope of individual tracking and profiling applications.
Sectoral Differences
The effectiveness of big data segmentation and chatbot technology varies by sector. For example, e-commerce and finance sectors benefit from these technologies highly, while their impact in areas such as public services or education is limited or has not yet been measured.
Technological Maturity Level and Implementation Barriers
The success of chatbot-based segmentation depends on the digital infrastructure of the business. Small and medium-sized businesses may have difficulty achieving this integration, which may reduce the homogeneity of the results.
Stating these limitations clearly increases the reliability of the study and provides a methodological basis for future research.
8. Conclusions and Recommendations
Big data-supported customer segmentation provides higher accuracy and efficiency in programmatic purchasing processes. Supporting this process with chatbot technologies increases data quality and enables the development of personalized communication strategies. The place of chatbots in this ecosystem is becoming increasingly important in terms of both customer experience and return on advertising investments. In future studies, it is recommended to conduct research on sector-based application examples and user perceptions of chatbots in different cultural contexts.
With the impact of digitalization, consumer behavior has become more complex and dynamic, making it necessary to reshape marketing strategies within the framework of data-based approaches. In this context, big data has become central to decision-making processes in customer-focused marketing applications; it has become a fundamental source, especially in analyses related to customer segmentation. The findings obtained within the scope of the study reveal that big data-based customer segmentation provides significant contributions in terms of both effectiveness and efficiency in programmatic purchasing processes.
The implementation of segmentation not only through demographic variables but also through behavioral, psychographic and interactional data enables advertising content to reach the right person at the right time, through the right channel, thus, increasing the return on advertising expenditures. When programmatic purchasing systems are integrated with these advanced segmentation models, they work in harmony with the personalized and real-time nature of digital advertising and produce results that directly affect brand performance. Chatbot-based interactions, which constitute another important dimension of the study, stand out as versatile tools that contribute not only to service delivery but also to data production in digital communication channels established with the customer. User data obtained through chatbots allows for a deeper understanding of customer profiles; increases the success of applications such as sentiment analysis, interest detection and personalized recommendation systems. Thanks to these interactions, dynamic and constantly updated segmentation models can be created, thus contributing to the agility and accuracy of programmatic purchasing strategies. As a result, the holistic evaluation of big data-based customer segmentation and chatbot-supported interactions creates a critical synergy that enables sustainable success in digital marketing strategies. Big data-based customer segmentation plays a significant role in increasing the breadth of programmatic purchasing services; while the integration of chatbot technologies during this period improves the acceleration and depth of communication. However, more empirical tests and sectoral comparative components are needed for this benefit to become general transitions. Future units are essential to reveal these relationships more concretely with regular and long-term data analyses. The findings of this study are instructive for marketing managers and digital advertising experts in terms of developing a data-driven decision-making culture and strategic use of artificial intelligence-based applications. In future studies, a more in-depth examination of this relationship with application examples specific to different sectors and cultural contexts will strengthen the interdisciplinary dimension of the subject and increase the potential to produce practical outputs.
References
Lv, Z. (2023) Generative Artificial Intelligence in the Metaverse Era. Cognitive Robotics, 3, 208-217. >https://doi.org/10.1016/j.cogr.2023.06.001
Boston Consulting Group (2023) Generative AI. >https://www.bcg.com/publications/2023/maximizing-the-potential-of-generative-ai
Mukherjee, A., Mukherjee, G., Chatterjee, A. and Tudu, B. (2024) The Ultimate Solution in Customer Engagement Using ChatGPT. In: Bansal, R., Ed., Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement, IGI Global, 28-54. >https://doi.org/10.4018/979-8-3693-0815-8.ch003
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M. and Yang, D. (2023) Is ChatGPT a General-Purpose Natural Language Processing Task Solver? In: Houda, B., Juan, P. and Kalika, B., Eds., Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 1399-1384. >https://doi.org/10.18653/v1/2023.emnlp-main.85
Meyer, C. and Schwager, A. (2007) Understanding Customer Experience. Harvard. Business Publishing.
Verhoef, P.C., Lemon, K.N., Parasuraman, A., Roggeveen, A., Tsiros, M. and Schlesinger, L.A. (2009) Customer Experience Creation: Determinants, Dynamics and Management Strategies. Journal of Retailing, 85, 31-41. >https://doi.org/10.1016/j.jretai.2008.11.001
Chi, D.T.P., Van Thu, P., Van Hoa, N. and Tuan, N.A. (2024) Customer Experience Management in Retail Business a Theoretical Debate. International Journal of Advanced Multidisciplinary Research and Studies, 4, 854-863.
Peruchini, M., da Silva, G.M. and Teixeira, J.M. (2024) Between Artificial Intelligence and Customer Experience: A Literature Review on the Intersection. Discover Artificial Intelligence, 4, Article No. 4. >https://doi.org/10.1007/s44163-024-00105-8
Ooi, K., Tan, G.W., Al-Emran, M., Al-Sharafi, M.A., Capatina, A., Chakraborty, A., et al. (2023) The Potential of Generative Artificial Intelligence across Disciplines: Perspectives and Future Directions. Journal of Computer Information Systems, 65, 76-107. >https://doi.org/10.1080/08874417.2023.2261010
Rese, A., Baier, D., Geyer-Schulz, A. and Schreiber, S. (2017) How Augmented Reality Apps Are Accepted by Consumers: A Comparative Analysis Using Scales and Opinions. Technological Forecasting and Social Change, 124, 306-319. >https://doi.org/10.1016/j.techfore.2016.10.010
Martin, J., Mortimer, G. and Andrews, L. (2015) Re-Examining Online Customer Experience to Include Purchase Frequency and Perceived Risk. Journal of Retailing and Consumer Services, 25, 81-95. >https://doi.org/10.1016/j.jretconser.2015.03.008
Laney, D. (2001) 3D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note.
Wedel, M. and Kannan, P.K. (2016) Marketing Analytics for Data-Rich Environments. Journal of Marketing, 80, 97-121. >https://doi.org/10.1509/jm.15.0413
Gao, Y. and Liu, H. (2022) Artificial Intelligence-Enabled Personalization in Interactive Marketing: A Customer Journey Perspective. Journal of Research in Interactive Marketing, 17, 663-680. >https://doi.org/10.1108/jrim-01-2022-0023
Chaffey, D. (2019) Digital Marketing: Strategy, Implementation and Practice. Pearson Education.
Yalamati, S. (2023) Revolutionizing Digital Banking: Unleashing the Power of Artificial Intelligence for Enhanced Customer Acquisition, Retention, and Engagement. International Journal of Management Education for Sustainable Development, 6, 1-20.
Vorobeva, D., Costa Pinto, D., António, N. and Mattila, A.S. (2023) The Augmentation Effect of Artificial Intelligence: Can AI Framing Shape Customer Acceptance of Ai-Based Services? Current Issues in Tourism, 27, 1551-1571. >https://doi.org/10.1080/13683500.2023.2214353
Adamopoulou, E. and Moussiades, L. (2020) An Overview of Chatbot Technology. In: Maglogiannis, I., Iliadis, L. and Pimenidis, E., Eds., IFIP Advances in Information and Communication Technology, Springer International Publishing, 373-383. >https://doi.org/10.1007/978-3-030-49186-4_31
Sinha P., Shastri A. and Lorimer S. (2023) How Generative AI Willchange Sales. >https://hbr.org/2023/03/how-generative-ai-will-change-sales
Huang, M. and Rust, R.T. (2018) Artificial Intelligence in Service. Journal of Service Research, 21, 155-172. >https://doi.org/10.1177/1094670517752459
Chen, H., Chiang, P.H.L. and Storey, V.C. (2012) Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36, 1165-1188. >https://doi.org/10.2307/41703503
Dhankhar, S. (2024) In-Depth Outlook on the Use of ChatGPT. In: Bansal, R., Ngah, A. H., Chakir, A. And Pruthi, N., Eds., Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement, IGI Global, 156-176. >https://doi.org/10.4018/979-8-3693-0815-8.ch009
Gandhe, A. and Karmore, S. (2020) Artificial Intelligence in Customer Engagement: Opportunities and Challenges. Proceedings of the International Conference on Computational Intelligence and Data Science (ICCIDS), Chennai, 20-22 February 2020, 1-6.
Mansour, B.S.A. and Basal, M. (2024) The Relationship between Digital Content Marketing Success and Sustainable Consumption Behavior. American Journal of Industrial and Business Management, 14, 392-407. >https://doi.org/10.4236/ajibm.2024.144020
Syam, N. and Sharma, A. (2018) Waiting for a Sales Renaissance in the Fourth Industrial Revolution: Machine Learning and Artificial Intelligence in Sales Research and Practice. Industrial Marketing Management, 69, 135-146. >https://doi.org/10.1016/j.indmarman.2017.12.019
Patil, D., Rane, N.L. and Rane, J. (2024) The Future of Customer Loyalty: How ChatGPT and Generative Artificial Intelligence Are Transforming Customer Engagement, Personalization, and Satisfaction. In: The Future Impact of ChatGPT on Several Business Sectors, Deep Science Publishing, 48-106. >https://doi.org/10.70593/978-81-981367-8-7_2
Basal, M. and Suzen, E. (2023) Consumer Cynicism toward Management’s Handling of Uncertainties a Study in the Aviation Sector. E3S Web of Conferences, 376, Article 05027. >https://doi.org/10.1051/e3sconf/202337605027
Davenport, T. H., Barth, P.,&Bean, R. (2012) How ‘Big Data’ Is Different. MIT Sloan Management Review, 54, 22-24.
Carvalho, I. and Ivanov, S. (2023) ChatGPT for Tourism: Applications, Benefits and Risks. Tourism Review, 79, 290-303. >https://doi.org/10.1108/tr-02-2023-0088
Deng, J. and Lin, Y. (2023) The Benefits and Challenges of ChatGPT: An Overview. Frontiers in Computing and Intelligent Systems, 2, 81-83. >https://doi.org/10.54097/fcis.v2i2.4465
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. and Galstyan, A. (2021) A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54, 1-35. >https://doi.org/10.1145/3457607
Romero, R.A. and Young, S.D. (2021) Ethical Perspectives in Sharing Digital Data for Public Health Surveillance before and Shortly after the Onset of the Covid-19 Pandemic. Ethics&Behavior, 32, 22-31. >https://doi.org/10.1080/10508422.2021.1884079
Yao, X. and Huang, J. (2019) Beyond the Hype: A Survey of Business Models in Artificial Intelligence. Big Data Analytics, 4, 1-10.
Garima, and Bansal, R. (2024) Unveiling the Potential of ChatGPT for Enhancing Customer Engagement. In: Bansal, R., Ngah, A.H., Chakir, A. And Pruthi, N., Eds., Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement, IGI Global, 111-128. >https://doi.org/10.4018/979-8-3693-0815-8.ch007
Bansal, R., Pruthi, N. and Singh, R. (2022) Developing Customer Engagement through Artificial Intelligence Tools. In: Bansal, R., Pruthi, N. and Singh, R., Eds., Advances in Marketing, Customer Relationship Management, and E-Services, IGI Global, 130-145. >https://doi.org/10.4018/978-1-6684-4496-2.ch008
Bapat, G., Mahindru, R., Kumar, A., Rroy, A.D., Bhoyar, S. and Vaz, S. (2024) Leveraging ChatGPT for Empowering MSMEs: A Paradigm Shift in Problem Solving. Engineering Proceedings, 59, Article 197. >https://doi.org/10.3390/engproc2023059197
Truly, A. (2023) Is ChatGPT Safe? Here Are the Risks to Consider before Using It. >https://www.digitaltrends.com/computing/is-chatgpt-safe/
Brandtzaeg, P.B. and Følstad, A. (2018) Chatbots: Changing User Needs and Motivations. Interactions, 25, 38-43. >https://doi.org/10.1145/3236669
Gabrielson, A.T., Odisho, A.Y. and Canes, D. (2023) Harnessing Generative Artificial Intelligence to Improve Efficiency among Urologists: Welcome ChatGPT. Journal of Urology, 209, 827-829. >https://doi.org/10.1097/ju.0000000000003383
Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., et al. (2023) Opinion Paper: “So What If ChatGPT Wrote It?” Multidisciplinary Perspectives on Opportunities, Challenges and Implications of Generative Conversational AI for Research, Practice and Policy. International Journal of Information Management, 71, Article 102642. >https://doi.org/10.1016/j.ijinfomgt.2023.102642
Ngai, E.W.T., Xiu, L. and Chau, D.C.K. (2009) Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification. Expert Systems with Applications, 36, 2592-2602. >https://doi.org/10.1016/j.eswa.2008.02.021
Harmeling, C.M., Moffett, J.W., Arnold, M.J. and Carlson, B.D. (2017) Toward a Theory of Customer Engagement Marketing. Journal of the Academy of Marketing Science, 45, 312-335. >https://doi.org/10.1007/s11747-016-0509-2
Damaševičius, R. and Zailskaitė-Jakštė, L. (2024) Transforming Consumer Experience through ChatGPT. In: Bansal, R., Ngah, A.H., Chakir, A. and Pruthi, N., Eds., Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement, IGI Global, 129-155. >https://doi.org/10.4018/979-8-3693-0815-8.ch008
Singh, V. and Singh, A. (2024) Revolutionizing the Hospitality Industry. In: Singh, A.K., Singh, V. and Kumar, N., Eds., Advances in Business Information Systems and Analytics, IGI Global, 192-203. >https://doi.org/10.4018/979-8-3693-0815-8.ch011
Bansal, R., Shukla, G., Gupta, A., Singh, A. and Pruthi, N. (2022) Optimizing Augmented Reality and Virtual Reality for Customer Engagement. In: Gupta, M., Jindal, P. and Bansal, S., Eds., Promoting Consumer Engagement Through Emotional Branding and Sensory Marketing, IGI Global, 24-35. >https://doi.org/10.4018/978-1-6684-5897-6.ch003
Bahrini, A., Khamoshifar, M., Abbasimehr, H., Riggs, R.J., Esmaeili, M., Majdabadkohne, R.M., et al. (2023) ChatGPT: Applications, Opportunities, and Threats. 2023 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, 27-28 April 2023, 274-279. >https://doi.org/10.1109/sieds58326.2023.10137850