Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency

Real-Time Purchase Prediction Using Retail Video Analytics

The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape

小売店舗における消費者行動の柔軟な認識システムに関する研究

Tohoku University博士（情報科学）thesi

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Tecnología para Tiendas Inteligentes

Trabajo de Fin de Grado en Doble Grado en Ingeniería Informática y Matemáticas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021Smart stores technologies exemplify how Artificial Intelligence and Internet of Things can effectively join forces to shape the future of retailing. With an increasing number of companies proposing and implementing their own smart store concepts, such as Amazon Go or Tao Cafe, a new field is clearly emerging. Since the technologies used to build their infrastructure offer significant competitive advantages, companies are not publicly sharing their own designs. For this reason, this work presents a new smart store model named Mercury, which aims to take the edge off of the lack of public and accessible information and research documents in this field. We do not only introduce a comprehensive smart store model, but also work-through a feasible detailed implementation so that anyone can build their own system upon it.Las tecnologías utilizadas en las tiendas inteligentes ejemplifican cómo la Inteligencia Artificial y el Internet de las Cosas pueden unir, de manera efectiva, fuerzas para transformar el futuro de la venta al por menor. Con un creciente número de empresas proponiendo e implementando sus propios conceptos de tiendas inteligentes, como Amazon Go o Tao Cafe, un nuevo campo está claramente emergiendo. Debido a que las tecnologías utilizadas para construir sus infraestructuras ofrecen una importante ventaja competitiva, las empresas no están compartiendo públicamente sus diseños. Por esta razón, este trabajo presenta un nuevo modelo de tienda inteligente llamado Mercury, que tiene como objetivo mitigar la falta de información pública y accesible en este campo. No solo introduciremos un modelo general y completo de tienda inteligente, sino que también proponemos una implementación detallada y concreta para que cualquier persona pueda construir su propia tienda inteligente siguiendo nuestro modelo.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Factors and their Manners of Impulsive Buying Behavior in Retail Apparel Industry

To begin with, the topic of impulsive buying behavior factors appear to be vital as the mass market keeps increasing rapidly, consumers tend to be more experienced and keep learning quickly. Therefore, marketers encounter the necessity to adjust to the constantly changing customer needs and wants. The research problem of the thesis is “What are the factors inducing people to buy on impulse?”, and two research questions are the following: “What are the most influential factors inducing people for impulse buying at clothing stores?” and “How do these most influential factors induce people for impulsive buying at clothing stores?”. These research problem and questions were primarily shaped to direct the authors to the precise information collection from secondary data sources. After that, the general overview of the literature found was carried out with the aim of creating an outline for further data gathering and analysis. Consequently, it was decided to select the most suitable research approach for the study problem and questions, which is semi-structured interview one. Therefore, the entire research is the qualitative one enabling the researchers to gain more detailed and profound responses. The research was provided with 24 initial factors, the influence on the impulsive buying of which, had to be studied. Ten individuals were interviewed composing a group of female students aged from 20 to 30 years old. The overall results have revealed 9 factors inducing people to purchase on impulse in the retail apparel stores. Moreover, the semi-structured interviews assisted in revealing how these most influential factors impel consumers to acquire products impulsively

Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach

With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance

Greater Space Means More Service: Leveraging the innovative power of architecture and design

Organizational structures certainly are of great importance in order to determine employees’ behaviour and performance. On the other hand, physical structures also significantly influence the way staff and customers view any company and interact with it. In service based activity, such as in retailing, banking, hospitality, and so, firms and institutions are competing thanks to innovations in products/services, delivery processes, and management styles. Innovative approaches may also materialize into the design of facilities. Service providers are in a position to significantly improve convenience, productivity, and attractiveness by designing space and defining appropriate layout carefully. This pattern also has to include identification of the meanings, characterization of size and qualification of the process by which any service facility delivers messages. In the last session of the paper, we address a particular type of service facilities, namely the buildings of institutions for higher education in management. The objective is then to analyze how facilities have evolved in order to cope with the change affecting business education.Service; innovation; architecture; working place; corporate symbols