The analysis of sales information, is a vital step in designing an effective marketing strategy. This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns. An extended version of the Multi-Cluster Overlapping k-Means Extension (MCOKE) algorithm with weighted k-Means algorithm is utilized to map customers to the garments of interest. The age & gender traits of the customer; the time spent and the expressions exhibited while selecting garments for purchase, are utilized to associate a customer or a group of customers to a garments they are interested in. Such study on the customer base of a retail business, may help in inferring the products of interest of their consumers, and enable them in developing effective business strategies, thus ensuring customer satisfaction, loyalty, increased sales and profits.

This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential within the banking sector. This research addresses this specific gap by providing a systematic review and strategic analysis of AI and NLP applications in bank marketing, focusing on their integration across the customer journey and operational excellence. Employing the PRISMA methodology, this study systematically reviews existing literature to assess the current landscape of AI and NLP in bank marketing. Additionally, it incorporates semantic mapping using Sentence Transformers and UMAP for strategic gap analysis to identify underexplored areas and opportunities for future research. The systematic review reveals limited research specifically focused on NLP applications in bank marketing. The strategic gap analysis identifies key areas where NLP can further enhance marketing strategies, including customer-centric applications like acquisition, retention, and personalized engagement, offering valuable insights for both academic research and practical implementation. This research contributes to the field of bank marketing by mapping the current state of AI and NLP applications and identifying strategic gaps. The findings provide actionable insights for developing NLP-driven growth and innovation frameworks and highlight the role of NLP in improving operational efficiency and regulatory compliance. This work has broader implications for enhancing customer experience, profitability, and innovation in the banking industry.

Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.

Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is thoroughly analyzed and discussed in this paper. Because of the case properties a robust and innovative approach to both data handling and analytical processes is required. The study led to a sound proposal for customer segmentation. The highlights of the proposal include a convenient data partition to decompose the problem, an adaptive distance function definition and its optimization through genetic algorithms. These comprehensive data handling strategies not only enhance the dataset reliability for segmentation analysis but also support the operational efficiency and marketing strategies of sports centers, ultimately improving the customer experience.

Marketing is one of the high-cost activities for any online platform. With the increase in the number of customers, it is crucial to understand customers based on their dynamic behaviors to design effective marketing strategies. Customer segmentation is a widely used approach to group customers into different categories and design the marketing strategy targeting each group individually. Therefore, in this paper, we propose an end-to-end pipeline RE-RFME for segmenting customers into 4 groups: high value, promising, need attention, and need activation. Concretely, we propose a novel RFME (Recency, Frequency, Monetary and Engagement) model to track behavioral features of customers and segment them into different categories. Finally, we train the K-means clustering algorithm to cluster the user into one of the 4 categories. We show the effectiveness of the proposed approach on real-world Housing.com datasets for both website and mobile application users.

What you would do next if you were planning to get AI to create that book entirely is to do more detailed outlines for each chapter and work through those to build the content. The thing about ChatGPT is that it's a conversation rather than individual tasks knotted together. The software should remember what you're working on and factor that in, though, as we shall see in a bit, it does sometimes go off the tracks which can be frustrating. Now that the genie is well and truly out of the bottle, everyone is into this in the tech world. Microsoft has put some 10 billion into resourcing ChatGPT and there's a pretty feral gold rush-style race going on for dominance. It's not going to stop here and this is only the beginning. Google initially said that they would penalise AI-generated content on their search engine but did practically a total about-face at the beginning of this year when they realised what was happening.

Market research gathers and analyzes information about a market, product, or service to understand consumer behavior, preferences, and trends. It is a crucial step for any business looking to succeed in today's competitive marketplace. Fortunately, artificial intelligence (AI) has revolutionized the market research industry. ChatGPT is a tool that has transformed how businesses gather and analyze consumer data. It offers a language model that can be used to gather insights from consumers and analyze data in real time.

Groove AI is a shortcut to a One Man Army or a very smart, experienced personal assistant. The Rise of AI --- has made things so much easier --- we can develop and enhance our own creativity --- everything can be accomplished with AI. From a business or entrepreneurial perspective, or if you are just getting started in a hobby, or in a side hustle, most certainly AI can help! All you need to do is... become good at Prompt Engineering! To learn more about the amazing opportunities AI offers check out AI videos on YouTube, connect with AI personalities on Twitter, read articles on Medium, join groups on LinkedIn and Facebook, or ask me.

ERP (Enterprise Resource Planning) is the brain of your organization's technology apparatus. The brain coordinates the activities of your body. It is responsible for telling the body what it should do. A well-planned Enterprise Resource Planning system is essential for any organization to function. But things will change over time. Digital transformation is an important driving force in today's business world. Businesses that want to make the most of Industry 4.0's technological advances will need them. Enterprise services that are efficient and error-free make it possible to use machine learning and artificial Intelligence technologies in real time and automate operations. This is a significant influence on digital transformation. One of the significant impacts of ML is the potential enhancement of Enterprise resource plan (ERP) applications.

In today's data-driven world, the role of marketing in businesses has become more complex than ever before. To succeed, businesses need to harness the power of Machine learning, Data Science, Deep Learning, and Artificial Intelligence to create marketing strategies that are targeted, efficient, and effective to capture the target audience. This article will explore how Artificial Intelligence and marketing can collaborate to create a marketing strategy that drives revenue growth and customer engagement. We will also discuss various machine learning techniques and tools that can be used to analyze customer data to find patterns in the data, optimize marketing campaigns for cost-effectiveness and customer acquisition, and personalize customer experiences based on those recommendations. In this article, we will dive deep into AI Marketing.