121,449 research outputs found

    Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

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    Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

    Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path

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    Product placement of top-selling items in highly visible aisles inside supermarkets plays a crucial role in enhancing customer shopping experience. Moreover, it is important for retailers to assure that their customers can effortlessly navigate the store and locate the items they are searching for in a timely manner. The research proposes a novel and effective approach that combines two methods; the SARIMAX model for forecasting sales of each product based on historical data; by using the predicted result, placing the most demanding item in highly visible aisles. And the use of Graph Database Management Systems (GDBMS) such as Neo4j to find the shortest path for consumers to navigate throughout the store to finish the shopping as per their shopping list. By leveraging the power of data analytics and machine learning, retailers can make data-driven decisions that result in improved sales andcustomer satisfaction. Retailers investing in these technologies and strategies will likely see a significant increase in customer satisfaction and sales

    A Novel Approach to Predict the Helpfulness of Online Reviews

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    Online reviews help consumers reduce uncertainty and risks faced in purchase decision making by providing information about products and services. However, the overwhelming amount of data continually being produced in online review platforms introduce a challenge for customers to read and judge the reviews. This research addresses the problem of misleading and overloaded information by developing a novel approach to predict the helpfulness of online reviews. The proposed approach in this study, first, clusters reviews using reviewer-related, and temporal factors. It then uses review-related factors to predict online review helpfulness in each cluster. Using a sample of Amazon.com reviews, the empirical findings offer strong support to the proposed approach and show its superior predictions of review helpfulness compared to earlier approaches. The outcomes of this study help customers in online shopping and assist online retailers in reducing information overload to improve their customers’ experience

    Understanding consumer responses to special event entertainment (SEE) in shopping centres

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    This paper reviews the literature on the use of entertainment in shopping centres and outlines the constructs believed to impact upon consumer’s responses to special event entertainment. Special event entertainment (SEE) refers to entertainment events or activities that are offered on an occasional, temporary or discontinued basis in shopping centres. Examples of SEE include school holiday entertainment and fashion shows (Parsons, 2003; Sit, Merrilees, & Birch, 2003). Using SEE, shopping centre management seeks to entice consumer patronage, increase patron traffic or promote the shopping centre brand. Despite the popularity of SEE in shopping centres, very little academic research (e.g. Parsons, 2003; Sit, Merrilees, & Birch, 2003) has either conceptually or empirically examined how consumers perceive or respond to SEE. This research presents a conceptual model that examines the determinants and outcomes of consumer responses to SEE, In particular, consumer responses to SEE are represented by SEE proneness and overall appreciation of SEE. These SEE responses are proposed to be determined by sensation-seeking tendencies and perceived value of SEE. Eight propositions are presented to explain the relationships of SEE responses with their determinants and outcomes. These relationships will be empirically tested in future research. Research implications of the conceptual model are also presented

    Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

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    Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application

    A ghost tour in Rouge = éŠé­‚ă€Šèƒ­è„‚æ‰Łă€‹

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    This paper discusses how Fleur\u27s association to some of the worldwide ghost tours insinuates the haunting quality of the past, time, and the cityscape; in other words, it discusses how the tragic love story between Fleur and Twelfth Master can also be allegorically read as a tragic cultural story of Hong Kong. This discussion allegorically reads Fleur, Twelfth Master and Yuen as figures hauled by modernity

    The effects of store atmosphere on shopping behaviour - A literature review.

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    This paper provides an insight into how the atmospherics of a retail environment influence shopping behaviour. Its objective is to support researchers and practitioners by summarizing the current state of knowledge and identifying gaps and avenues for future research. The scope covers studies in retail marketing and environmental psychology published during the last 35 years. It has been shown that environmental cues (music, scent etc.) have an effect on the emotional state of the consumer, which in turn causes behavioural changes, both positive (approach, buy more, stay longer etc.) and negative (not approach, buy less, leave earlier etc.). Most studies make reference to the PAD model, which proposes that the relevant emotions in this process can be measured along three dimensions Pleasure, Arousal and Dominance (Mehrabian, A. & Russell, J.A.,1974, An approach to environmental psychology, Cambridge, MA.: MIT Press). Since then, significant advances have been made to understand the effect of individual cues, their interaction, as well as the role of moderators, such as gender, age, or shopping motivation. However, there are a number of opportunities for further research. Too little is known about the moderating effects of Arousal and Dominance and how they interact with each other and with Pleasure dimension. Also a number of other moderators, such as gender and culture, should be integrated into the model
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