Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships
Source abstract
Abstract
Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in Service Interactions · Digital Marketing and Social Media · Blockchain Technology Applications and Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia
Putra Business School, Putra Malaysia University, Seri Kembangan 43400, Selangor, Malaysia
National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
References (73)
Show source-provided references
1. Artificial Intelligence and Marketing: Pitfalls and Opportunities10.1016/j.intmar.2020.04.007 ↗
2. Hello marketing, what can artificial intelligence help you with?10.1177/1470785318776841 ↗
3. Kotler, P., and Keller, K.L.B. (2016). Marketing Management, Pearson. [15th ed.].
4. Artificial intelligence in marketing: Systematic review and future research direction
5. Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing10.1177/0008125619859317 ↗
6. How artificial intelligence will change the future of marketing10.1007/s11747-019-00696-0 ↗
7. The future of marketing10.1016/j.ijresmar.2019.08.002 ↗
8. (2021, June 22). AI: Built to Scale. Available online: http://www.accenture.com/gb-en/insights/artificial-intelligence/ai-investments.
9. (2021, June 24). Professional Services Firms See Huge Potential in Machine Learning. Available online: https://www.technologyreview.com/2018/11/02/139216/professional-services-firms-see-huge-potential-in-machine-learning/.
10. Marketing and Artificial Intelligence10.18267/j.cebr.213 ↗
11. Artificial Intelligence Marketing: An application of a novel Lightly Trained Support Vector Data Description
12. Exploring the impact of brand image on customer loyalty and commitment in China10.1108/17468770910964993 ↗
13. Customer Engagement Behavior: Theoretical Foundations and Research Directions10.1177/1094670510375599 ↗
14. Consumer Trust, Value, and Loyalty in Relational Exchanges10.1509/jmkg.66.1.15.18449 ↗
15. Modeling choices among alternative responses to dissatisfaction
16. Relationships between providers and users of market research: The dynamics of trust within and between organizations10.1177/002224379202900303 ↗
17. Oliver, R.L. (2010). Satisfaction: A Behavioral Perspective on the Consumer: A Behavioral Perspective on the Consumer, Taylor & Francis Group. [2nd ed.].
18. Social media? Get serious! Understanding the functional building blocks of social media10.1016/j.bushor.2011.01.005 ↗
19. Adoption of the Internet of things technologies in business procurement: Impact on organizational buying behaviour10.1108/JBIM-10-2015-0190 ↗
20. Contemporary perspectives on the strategic role of information in internet of things-driven industrial services10.1108/JBIM-06-2017-0153 ↗
21. (2021, June 24). IBM Watson is AI for Business. Available online: https://www.ibm.com/watson.
22. (2021, June 24). Thanking All Involved in the Alpine. AI Journey. Available online: https://marchick.medium.com/thanking-all-involved-in-the-alpine-ai-journey-793190a9165c.
23. (2021, June 24). KFC Launches First AI-Enabled Outlet in Beijing. Available online: https://retail.economictimes.indiatimes.com/news/food-entertainment/food-services/kfc-launches-first-ai-enabled-outlet-in-beijing/56177544.
24. (2021, June 24). Shiseido Launches Internet of Things Skincare System. Available online: https://fortune.com/2019/07/02/shiseido-optune-internet-of-things-ai-and-ar-skincare-system/.
25. (2021, June 24). A.I. in the Beauty Industry: How the Pandemic Finally Made Consumers Care About It. Available online: https://fortune.com/2021/01/11/ai-artificial-intelligence-personalized-beauty-cosmetics-brainstorm-reinvent/.
26. (2021, June 24). The Amazing Ways eBay Is Using Artificial Intelligence to Boost Business Success. Available online: https://www.forbes.com/sites/bernardmarr/2019/04/26/the-amazing-ways-ebay-is-using-artificial-intelligence-to-boost-business-success/?sh=71f19fcc2c2e.
27. (2021, June 24). FashionAI: Revamping the Fashion and E-Commerce Industry Through Artificial Intelligence. Available online: https://www.alibabacloud.com/blog/fashionai-revamping-the-fashion-and-e-commerce-industry-through-artificial-intelligence_595413.
28. (2021, June 24). Artificial Intelligence Powered Digital Assistants. Available online: https://medium.com/voice-tech-podcast/artificial-intelligence-powered-digital-assistants-1e0bdf108641.
29. (2021, June 24). Are Alexa and Siri Considered AI?. Available online: https://bernardmarr.com/default.asp?contentID=1830.
30. (2021, June 24). Wearable Tech Company StretchSense Makes a Hollywood Comeback. Available online: https://www.wearable-technologies.com/2020/11/wearable-tech-company-stretchsense-makes-a-hollywood-comeback/.
31. (2021, June 24). Naver to Test AI-Based Product Suggestion Service for Personalized Online Shopping Experience. Available online: https://www.ajudaily.com/view/20201223075926232.
32. (2021, June 24). The Evolution of the Watson IoT Platform: Three Key Things You Should Know. Available online: https://www.ibm.com/blogs/internet-of-things/iot-the-watson-iot-platform-three-key-things-you-should-know/.
33. (2021, June 24). How Artificial Intelligence Is Used to Make Beer. Available online: https://www.forbes.com/sites/bernardmarr/2019/02/01/how-artificial-intelligence-is-used-to-make-beer/?sh=d3cd71670cf4.
34. (2021, June 24). The Amazing Ways Spotify Uses Big Data, AI and Machine Learning to Drive Business Success. Available online: https://bernardmarr.com/default.asp?contentID=1201.
35. (2021, June 24). Developing & Implementing Artificial Intelligence & Machine Learning for the Benefit of Business and Society. Available online: http://eait.com/.
36. (2021, June 24). Monteloeder Digital Nutraceutical for You: A New Strategy Is Possible. Available online: https://www.monteloeder.com/monteloeder-digital-nutraceutical-for-you-a-new-strategy-is-possible/.
37. (2021, June 24). AI Works Behind the Scenes to Truly Personalize Your Banking Experience. Available online: https://impactcee.com/tag/ing-bank-slaski/.
38. Artificial intelligence (AI) and its implications for market knowledge in B2B marketing10.1108/JBIM-10-2018-0295 ↗
39. The Singularity Is Near: When Humans Transcend Biology
40. Deep Reinforcement Learning for Traffic Signal Control: A Review10.1109/ACCESS.2020.3034141 ↗
41. Artificial intelligence (AI) in strategic marketing decision-making: A research agenda10.1108/BL-03-2020-0022 ↗
42. Knowledge co-creation in Open Innovation Digital Platforms: Processes, Tools and Services10.1108/JBIM-09-2018-0276 ↗
43. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance10.1016/j.indmarman.2020.12.001 ↗
44. The role of users and customers in digital innovation: Insights from B2B manufacturing firms10.1016/j.im.2015.12.005 ↗
45. A multi-layer perceptron approach for customer churn prediction10.14257/ijmue.2015.10.7.22 ↗
46. Modelling and evaluating customer loyalty using neural networks: Evidence from startup insurance companies10.1016/j.fbj.2016.04.001 ↗
47. A Guide to Convolutional Neural Networks for Computer Vision
48. Valentin, J., and Reutterer, T. (2019, January 20–22). From RFM to LSTM: Predicting customer future with autoregressive neural networks. Proceedings of the INFORMS Marketing Science Conference, Rome, Italy.
49. Sarkar, M., and De Bruyn, A. (2019, January 20–22). Predicting customer behaviour with LSTM neural networks. Proceedings of the INFORMS Marketing Science Conference, Rome, Italy.
50. Dean, D.J., Ranchal, R., Gu, Y., Sailer, A., Khan, S., Beaty, K., Bakthavachalam, S., Yu, Y., Ruan, Y., and Bastide, P. (2017, January 25–30). Engineering scalable, secure, multi-tenant cloud for healthcare data. Proceedings of the IEEE 13th World Congress on Services, Honolulu, HI, USA.10.1109/SERVICES.2017.13 ↗
51. Aarabi, P., Manashirov, B., Phung, E., and Lee, K.M. (2015, January 14–16). Precise Skin-Tone and Under-Tone Estimation by Large Photo Set Information Fusion. Proceedings of the 2015 IEEE International Symposium on Multimedia (ISM), Miami, FL, USA.10.1109/ISM.2015.61 ↗
52. Arsenijevic, U., and Jovic, M. (October, January 30). Artificial intelligence marketing: Chatbots. Proceedings of the International Conference on Artificial Intelligence: Applications and Innovations, Belgrade, Serbia.
53. (2021, June 24). Schnuck Markets Rolls Out Shelf-Scanning Robots to Over Half of Store Base. Available online: https://www.supermarketnews.com/technology/schnuck-markets-rolls-out-shelf-scanning-robots-over-half-store-base.
54. (2021, June 24). How Sephora Used Chatbots to Compliment In-Store Service. Available online: https://www.indigo9digital.com/blog/learn-how-sephora-is-using-chatbots-to-create-a-better-customer-experience.
55. (2021, June 24). Chatbot Use Cases that Actually Work. Available online: https://emerj.com/ai-sector-overviews/7-chatbot-use-cases-that-actually-work/.
56. Bridge analysis in a Social Internetworking Scenario10.1016/j.ins.2012.10.021 ↗
57. Moving from social networks to social internetworking scenarios: The crawling perspective10.1016/j.ins.2013.08.046 ↗
58. Comparing Twitter and Facebook user behavior: Privacy and other aspects10.1016/j.chb.2015.05.045 ↗
59. Defining and detecting k-bridges in a social network: The Yelp case, and more10.1016/j.knosys.2020.105721 ↗
60. Wang, N., Wang, H., Jia, Y., and Yin, Y. (2019, January 8–12). Explainable recommendation via multitask learning in opinionated text data. Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.10.1145/3209978.3210010 ↗
61. Chen, X., Qin, Z., Zhang, Y., and Xu, T. (2016, January 17–21). Learning to rank features for recommendation over multiple categories. Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval, Pisa, Italy.10.1145/2911451.2911549 ↗
62. A Self-Learning Information Diffusion Model for Smart Social Networks10.1109/TNSE.2019.2935905 ↗
63. Applications of machine learning models on Yelp data
64. Learning representations from heterogeneous network for sentiment classification of product reviews10.1016/j.knosys.2017.02.030 ↗
65. Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis10.1162/tacl_a_00002 ↗
66. A social network analysis based approach to extracting knowledge patterns about innovation geography from patent databases
67. A well-tailored centrality measure for evaluating patents and their citations10.1108/JD-10-2018-0168 ↗
68. Opinion–Aspect Relations in Cognizing Customer Feelings via Reviews10.1109/ACCESS.2018.2797224 ↗
69. (2021, June 24). What Does the Future of Customer Experience Look Like?. Available online: https://www.ama.org/publications/MarketingNews/Pages/what-does-future-customer-experience-look-like.aspx.
70. Resistance to Medical Artificial Intelligence10.1093/jcr/ucz013 ↗
71. Machines versus humans: The impact of AI chatbot disclosure on customer purchases
72. Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13–17). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939778 ↗
73. (2021, June 24). Dan Brown on God and Artificial Intelligence in His New Thriller. Available online: https://www.cbsnews.com/news/dan-brown-on-god-and-artificial-intelligence-in-his-new-thriller-origin/.