Multi Clustering Recommendation System for Fashion Retail

Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers’ profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users’ profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online.

Impact of Artificial Intelligence on Customer Loyalty in the Indian Retail Industry

Artificial intelligence has led to the automation of traditional manufacturing and industrial processes and practices. The use of artificial intelligence improves customer experience and it's a proven fact that consumers who enjoy their shopping experience end up making more purchases. Retailing is one of the sectors that has seen drastic changes after the inception of artificial intelligence. This transformation can be seen in the supermarkets like Amazon Go store, Alibaba Hema store, IKEA, and many others. The objective of this chapter is to study the impact of artificial intelligence on Indian retail customers. Primary survey was conducted for the study and it was found that retail organizations emphasizing store design/layout and adoption of technological innovation to ease the consumer buying process were more successful in creating loyal customers for their stores. It can be concluded that India is still lacking in the adoption of IT systems in the retail sector and serious efforts are required in this direction.

FlexFashion: E-Commerce with Advance Features

The aim of this website is to enhance the shopping experience for customers using an advance feature of recommending matching outfits using the colorgram module. Flex Fashion is an interactive e-commerce solution providing users with an excellent fashion platform. The e - commerce platform displays an order cut-off time and a delivery window for the products selected by the consumer. The e - commerce platform does not settle with the user's credit supplier until the item chosen by the consumer is picked from inventory but before it is delivered. As a result, the buyer can make adjustments to the purchase online. There are different categories on the home page available to filter the products based on your style and needs. In addition to the apparels for both men and women one will find flexible variety of accessories and daily essential products. The e - commerce platform does not settle with the user's credit supplier until the item chosen by the consumer is picked from inventory but before it is delivered. As a result, the buyer can make adjustments to the purchase online. Once the customer decides to submit a purchase order, there is a track us page where just by adding ordered and email the user can track there order. This is to facilitate all people who are busy with their work and have no time to get their desired apparels. We are here to provide user with all the best and suitable clothing for sale. If once register into our site, then anyone can avail the benefit with our latest updates of the sale. Keywords: Colorgram, Tracker

SmartLOC

On-demand delivery is a rapidly developing business worldwide, where meals and groceries are delivered door to door from merchants to customers by the couriers. Couriers' real-time localization plays a key role in on-demand delivery for all parties like the platform's order dispatching, merchants' order preparing, couriers' navigation, and customers' shopping experience. Although GPS has well solved outdoor localization, indoor localization is still challenging due to the lack of large-coverage, low-cost anchors. Given the high penetration of smartphones in merchants and frequent rendezvous between merchants and couriers, we employ merchants' smartphones as indoor anchors for a new sensing opportunity. In this paper, we design, implement and evaluate SmartLOC, a map-free localization system that employs merchants' smartphones as anchors to obtain couriers' real-time locations. Specifically, we design a rendezvous detection module based on Bluetooth Low Energy (BLE), build indoor shop graphs for each mall, and adopt graph embedding to extract indoor shops' topology. To guarantee anchors' accuracy and privacy, we build a mutual localization module to iteratively infer merchants' state (in-shop or not) and couriers' locations with transformer models. We implement SmartLOC in a large on-demand delivery platform and deploy the system in 566 malls in Shanghai, China. We evaluate SmartLOC in two multi-floor malls in Shanghai and show that it can improve the accuracy of couriers' travel time estimation by 24%, 43%, 70%, and 76% compared with a straightforward graph solution, GPS, Wi-Fi, and TransLoc.

Research on Online Shopping Demand of Chinese Elderly under the Background of Aging

Firstly, this paper analyzes the main characteristics of China's elderly consumer market. Secondly, it analyzes the changes of the elderly consumer market from environmental and psychological factors. Thirdly, it analyzes the problems existing in China's online shopping market for the elderly. The study found that there are many problems in China's elderly market, including lack of pertinence of market products, complex operation, chaotic market order, many after-sales problems and so on. Finally, the following countermeasures and suggestions are put forward to promote the healthy development of the elderly consumer market: by expanding the development scope, creating the elderly model of online shopping platform, strengthening supervision and broadening sales channels, we can bring a more comfortable online shopping experience for the elderly group.

Customer fairness perceptions in augmented reality-based online services

PurposeAn increasing number of retailers is trying to stimulate customers by embedding augmented reality (AR) features such as video try-on into the online shopping experience. As such AR-based online services require customers to actively participate in the service provision, this paper aims at investigating fairness perceptions and customer responses associated with AR-enabled customer participation.Design/methodology/approachThe conceptual framework of this study is based on equity theory. To compare customer responses after an in-store service encounter as opposed to AR-enabled customer participation involving video try-on, this study contains a between-subject online experiment. The effective sample comprises N = 215 participants.FindingsThe data analysis demonstrates that AR-enabled customer participation leads to significantly lower levels of distributive, procedural and price fairness as well as lower engagement intentions than in-store service encounters. Simultaneously, participants in the video try-on scenario report higher negative word-of-mouth (WOM) intentions than in the in-store scenario.Research limitations/implicationsThe extra mile customers go when using AR-based online services is reflected in less favorable fairness evaluations.Practical implicationsService managers should design AR applications in a manner that requires minimum customer participation.Originality/valueThis study contributes to service research by linking AR-enabled customer participation to evaluations of distributive, procedural and price fairness and their outcomes. This is vital to fully exploit the potential of AR in services.