In e-commerce, user representations are essential for various applications. Existing methods often use deep learning techniques to convert customer behaviors into implicit embeddings. However, these embeddings are difficult to understand and integrate with external knowledge, limiting the effectiveness of applications such as customer segmentation, search navigation, and product recommendations. To address this, our paper introduces the concept of the customer persona. Condensed from a customer's numerous purchasing histories, a customer persona provides a multi-faceted and human-readable characterization of specific purchase behaviors and preferences, such as Busy Parents or Bargain Hunters. This work then focuses on representing each customer by multiple personas from a predefined set, achieving readable and informative explicit user representations. To this end, we propose an effective and efficient solution GPLR. To ensure effectiveness, GPLR leverages pre-trained LLMs to infer personas for customers. To reduce overhead, GPLR applies LLM-based labeling to only a fraction of users and utilizes a random walk technique to predict personas for the remaining customers. We further propose RevAff, which provides an absolute error $ε$ guarantee while improving the time complexity of the exact solution by a factor of at least $O(\frac{ε\cdot|E|N}{|E|+N\log N})$, where $N$ represents the number of customers and products, and $E$ represents the interactions between them. We evaluate the performance of our persona-based representation in terms of accuracy and robustness for recommendation and customer segmentation tasks using three real-world e-commerce datasets. Most notably, we find that integrating customer persona representations improves the state-of-the-art graph convolution-based recommendation model by up to 12% in terms of NDCG@K and F1-Score@K.

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.

Recently, peoples awareness of online purchases has significantly risen. This has given rise to online retail platforms and the need for a better understanding of customer purchasing behaviour. Retail companies are pressed with the need to deal with a high volume of customer purchases, which requires sophisticated approaches to perform more accurate and efficient customer segmentation. Customer segmentation is a marketing analytical tool that aids customer-centric service and thus enhances profitability. In this paper, we aim to develop a customer segmentation model to improve decision-making processes in the retail market industry. To achieve this, we employed a UK-based online retail dataset obtained from the UCI machine learning repository. The retail dataset consists of 541,909 customer records and eight features. Our study adopted the RFM (recency, frequency, and monetary) framework to quantify customer values. Thereafter, we compared several state-of-the-art (SOTA) clustering algorithms, namely, K-means clustering, the Gaussian mixture model (GMM), density-based spatial clustering of applications with noise (DBSCAN), agglomerative clustering, and balanced iterative reducing and clustering using hierarchies (BIRCH). The results showed the GMM outperformed other approaches, with a Silhouette Score of 0.80.

Different learning methods and patterns are generally associated with the human mind. The visual, auditory, kinesthetic, and reading/writing methods of learning are widely recognized as the four primary methods by which humans learn. The utility of these learning methods varies from person to person. While Jack may learn more effectively by reading a book and writing key points from what he has learned, Jill may learn more effectively by doing and putting what she has learned into action, which is the kinesthetic form of learning. Machine learning models, like humans, can learn patterns in data in a variety of ways.

Today I will talk about the basic principles that a data analyst/data scientist uses when handling a job. While doing this, I will give examples using the work I did in the VBO Bootcamp/Miuul finish project. Our titles here will be as follows. The first questions we should ask ourselves or when starting a job in the institution we work for are "What is the problem we are trying to solve? Or what will be the contribution of this work?"

In Asia, the wealth of affluent and mass-affluent customer segments is growing rapidly, bringing about new opportunities and growth prospects for banks and wealth managers alike in the region. But to tap into this opportunity, services providers will need to embrace technology and digital platforms to not only provide customers the services they expect, but also gain in productivity and efficiency, a new report by global consultancy McKinsey says. The report, titled Digital and AI-enabled wealth management: the big potential in Asia and released on February 02, looks at the region's fast growing household wealth and shares how wealth managers can capture this opportunity by embracing data analytics and artificial intelligence (AI) to reduce costs, increase access for their clients and improve customer experience across the entire lifecycle. In 2021, the wealth pool of households with investable assets of US$100,000 to US$1 million in Asia totaled US$2.7 trillion. That sum is projected to soar to US$4.7 trillion by 2026 as incomes continue to rise across the region, the report says.

In an increasingly customer-centric business environment, effective communication between marketing and senior management is crucial for success. With the rise of globalization and increased competition, utilizing new data mining techniques to identify potential customers is essential for direct marketing efforts. This paper proposes a data mining preprocessing method for developing a customer profiling system to improve sales performance, including customer equity estimation and customer action prediction. The RFM-analysis methodology is used to evaluate client capital and a boosting tree for prediction. The study highlights the importance of customer segmentation methods and algorithms to increase the accuracy of the prediction. The main result of this study is the creation of a customer profile and forecast for the sale of goods.

Most marketers know that good marketing software is the most important tool in any successful campaign. However, adding an extra dimension to the strategy and using AI-powered marketing tools can make your job easier and better. In recent years, many new startups have switched to AI and machine learning to offer better products aligned with the AI fever that has beaten all of us. And thanks to these advancements and the emergence of startups providing AI solutions, the way we work is fundamentally changing. Also, customers' needs are constantly changing. Digital marketers need to be on the cutting edge so they can effectively tackle new tasks. If you're thinking of starting a business, do it in 2023.

If you thought that Artificial Intelligence was only used for playing chess or analyzing data, think again. AI is quickly becoming a staple in sales teams across the globe as companies attempt to increase efficiency and close more deals. This blog post will explore a few ways companies use AI for more efficient sales. From lead generation to customer segmentation, AI is changing the sales landscape as we know it. So if you're curious about how AI can help your sales team, read on! Sales intelligence is the term given to the data and information gathered about potential customers during the sales process.