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"Consumer profiling -- Data processing"
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The age of surveillance capitalism : the fight for a human future at the new frontier of power
\"Shoshana Zuboff, named \"the true prophet of the information age\" by the Financial Times, has always been ahead of her time. Her seminal book In the Age of the Smart Machine foresaw the consequences of a then-unfolding era of computer technology. Now, three decades later she asks why the once-celebrated miracle of digital is turning into a nightmare. Zuboff tackles the social, political, business, personal, and technological meaning of \"surveillance capitalism\" as an unprecedented new market form. It is not simply about tracking us and selling ads, it is the business model for an ominous new marketplace that aims at nothing less than predicting and modifying our everyday behavior--where we go, what we do, what we say, how we feel, who we're with. The consequences of surveillance capitalism for us as individuals and as a society vividly come to life in The Age of Surveillance Capitalism's pathbreaking analysis of power. The threat has shifted from a totalitarian \"big brother\" state to a universal global architecture of automatic sensors and smart capabilities: A \"big other\" that imposes a fundamentally new form of power and unprecedented concentrations of knowledge in private companies--free from democratic oversight and control\"-- Provided by publisher.
Book
Social media data mining and analytics
Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data.Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Social Media Data Mining and Analytics shows analysts.
eBook
Predictive analytics and data mining : concepts and practice with RapidMiner
Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool.
eBook
Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting
User profile is a summary of a consumer’s interests and preferences revealed through the consumer’s online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company view online user profiling as one of the promising opportunities companies should take advantage of to unlock “big data’s” potential. This paper proposes a modeling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when limited information is available. The approach is easily parallelized and scales well for processing massive records of user online activity. We demonstrate application of our approach to customer-base analysis and display advertising. Our empirical analysis uncovers easy-to-interpret behavior profiles and describes the distribution of such profiles. Furthermore, it reveals that even for information-rich online firms profile inference that is based solely on their internal data may produce biased results. We find that although search engines cover smaller portions of consumer Web visits than major advertising networks, their data is of higher quality. Thus, even with the smaller information set, search engines can effectively recover consumer behavioral profiles. We also show that temporal limitations imposed on individual-level tracking abilities are likely to have a differential impact across major online businesses, and that our approach is particularly effective for temporally limited data. Using economic simulation we demonstrate potential gains the proposed model may offer a firm if used in individual-level targeting of display ads.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mksc.2015.0956
.
Journal Article
Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review
In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research.
Journal Article
Deep Learning and User Consumption Trends Classification and Analysis Based on Shopping Behavior
Driven by the wave of digitalization, the booming development of the e-commerce industry urgently requires in-depth analysis of user shopping behavior to improve service experience. In view of the limitations of traditional models in dealing with complex shopping scenarios, this study innovatively proposes a deep learning model: the VATA model (a combination of variational autoencoder, transformer, and attention mechanism). Through this model, the authors strive to classify and analyze user shopping behavior more accurately and intelligently. Variational autoencoder (VAE) can learn the potential representation of user personalized historical data, capture the implicit characteristics of shopping behavior, and improve the ability to deal with actual shopping situations. Transformer models can more comprehensively capture the dependencies between shopping behaviors and understand shopping. The overall structure of behavior plays an important role.
Journal Article
Segmenting the organic food market in Lebanon: an application of k-means cluster analysis
Purpose
The purpose of this paper is to discover profiles of organic food consumers in Lebanon by performing a market segmentation based on lifestyle and attitude variables and thus be able to propose appropriate marketing strategies for each market segment.
Design/methodology/approach
A survey, based on the use of closed-ended questionnaire, was addressed to 320 organic food consumers in the capital Beirut, in February and March 2014. Descriptive analysis, principal component analysis and cluster analysis (k-means method) were performed upon collected data.
Findings
Four clusters were obtained and labelled based on psychographic characteristics and willingness to pay for the most purchased organic products. “Localist” and “Health conscious” clusters were the largest proportion of the selected sample, thus these were the most critical to be addressed by specific marketing strategies, emphasising the combination of local and organic food and the healthy properties of organic products. “Rational” and “Irregular” cluster were relatively small groups, addressed by pricing and promotional strategies.
Originality/value
This is the first study attempting to segment organic food consumers into different categories in a developing country as Lebanon.
Journal Article
Big Data in Practice
The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. * Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers * Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more * Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking * Develop your own big data strategy by accessing additional reading materials at the end of each chapter
eBook
Advancing Canning Quality in Common Beans: An Integrated Farm‐to‐Can Framework Combining Breeding, Processing, and Artificial Intelligence
Common beans ( Phaseolus vulgaris L.) are essential raw material for the canning industry. This article reviews recent advances in assessing canning quality and the integration of artificial intelligence (AI) into breeding methodologies aimed at developing genotypes with superior yield and canning‐quality traits. Cultivars destined for canning must consistently meet strict quality standards in addition to high agronomic performance. Conventional phenotypic quality parameters, such as washed drained weight, processing quality index, sensory properties, and texture, are central to predicting canning performance. However, their assessment is labor‐intensive, costly, and often limited to advanced filial generations, making early selection challenging. Recent progress in artificial intelligence, imaging, and data analysis provides new opportunities to evaluate canning traits at early stages of breeding, complementing conventional sensory and laboratory evaluations. These innovations enable breeders to optimize selection pipelines, reduce dependency on external facilities, and accelerate the release of superior cultivars. The review highlights the potential of AI coupled with nondestructive imaging to transform canning‐quality assessment by offering high‐throughput, cost‐effective, and scalable tools that improve prediction accuracy. Future directions include harmonizing evaluation protocols, developing cultivars that combine nutritional enrichment with drought tolerance and canning quality, and expanding genotype testing across multiple environments. The integration of AI with traditional breeding strategies offers a promising pathway to enhance both the efficiency and sustainability of dry bean improvement programs, ensuring cultivars that align with market requirements and consumer expectations.
Journal Article