Embodiment in 3D virtual retail environments: exploring perceptions of the virtual shopping experience

The customer can now easily create, and customize, their own personal three dimensional (3D) virtual bodies in a variety of virtual environments; could you, by becoming a virtual body, actually enhance your online shopping and buying experiences or, would this potentially inhibit the pure visceral pleasure of retail therapy? "Second Life allows you to be a celebrity in your own lunchtime, .…you can design the body you've always wanted, and indulge your fashionista fetish for very little money. You can be the most attractive, best-dressed version of yourself you can imagine." This paper investigates online shopping in Second Life, through the experience of being avatars. We will discuss the possibilities of using avatars as brand new consumer identities for personalised and customised fashion shopping within the 3D multi user virtual environment, and question the influences and effects of these developments on the traditional high street shopping trip. The hyper un-realistic and non-sensory interface of online shopping for clothes has been hotly debated over the last decade; through the media, the industry and most importantly by the buying public. The customer’s inability to try on and experience the product has been the main inhibitor to shopping on-line, and the high levels of product returns in home shopping dramatically reflect this reality. Faster broadband connections and improved 2D web sites are making clothes shopping on the web more accessible, and for important customer groups, such as young professional females, and plus-size teenagers, virtual 3D technologies offer freedom of choice in any location. Retailers are now confidently providing different shopping experiences by combining 2D and 3D interactive visualisation technologies with advanced marketing techniques, to create virtual retail environments that attempt to actualise the true essence of shopping; by browsing, socialising, trying-on before buying and, in a new twist, leaving the store proudly wearing the item just purchased. American Apparel, Bershka, L’Oreal, Calvin Klein, Reebok, Sears, Nike and Adidas are pioneering virtual mega stores, and all offer newly innovative, and alternative shopping experiences inside 3D multi user virtual environments. An experiential and exploratory approach will be used to investigate fashion brands, and their virtual 3D stores in Second Life. As 3D avatars, we will record a range of customer perceptions and attempt to map their shopping patterns in this massively popular virtual world. The qualitative data gathered will inform discussions about the value of the virtual shopping experience for the customer and the retailer. Conclusions will also question the possibility of using avatars in a virtual shopping environment to acquire accurate body specifications for better fit and the collection of personal details for use in the future development of alternative shopping experiences

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

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

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future