The Future of Retail Operations

Retailing consists of all the activities associated with the selling of goods to the final consumer. In this article, we review the research on retail operations published in Manufacturing & Service Operations Research (M&SOM) since 1999. We then discuss the current retail landscape and the new research directions it offers, in which M&SOM can play a prominent role

Optimizing Franchisee Sales and Business Performance in Retail Food Sector

This paper aims at identifying attributes of players in franchising process that contribute in delivering satisfaction in purchasing and operating the outlets in Mexico. The discussion also focuses the impact of cultural diversities in franchisee selection, outlet management and achieving high performance. Franchisee relationship has been evaluated in reference to principal determinants attributing to the enhancement of satisfaction and strengthening franchisor-franchisee ties. It has been observed in the study that performance of franchisee outlets is a function of outlet attraction, supply and manufacturing management, quality, price, and promotional strategies as functional factors. Besides, relational variables including personalized customer services, leisure support and customer convenience also influence the performance of outlets.Franchising, performance measurement, market demand, sales management, retailing, store organization, pricing, promotional strategies, customer value and business growth

Big Data Applications in Digital Marketing

Every year, a set of new trends arise that change the course of the digital marketing process and make it easier for marketers to do their work and save time continuously. One of the most critical new trends that have greatly influenced digital marketing and are expected to sustain its impact in the future is Big Data. This article aimed to outline the role of big data in digital marketing by discussing its various applications in digital marketing operations. This article was based on the systematic review methodology by reviewing the previous literature in the study area. The results obtained from the literature showed various applications of big data analytics in digital marketing, including (improving customer experience, measuring and analyzing competitors, innovation and product development....etc.). The article also discovered that companies regularly employ big data to improve the accuracy of different marketing decisions, such as enhancing customer knowledge, providing highly customized promotional content, increasing sales, and measuring the effectiveness of digital marketing campaigns. This article will provide a theoretical base for future researchers to conduct a field study on Turkish companies to examine to what extent they are using big data analytics in digital marketing

A framework for personalized dynamic cross-selling in e-commerce retailing

Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and rating differences across users. The retail setting differs in that there are only records of transactions (in period X, customer Y purchased product Z). Instead of a range of explicit rating scores, transactions form binary datasets; 1-purchased and 0-not-purchased. This makes it a one-class collaborative filtering (OCCF) problem. Notwithstanding the existence of wider application domains of such an OCCF problem, very little work has been done in the retail setting. This research addresses this gap by developing an effective framework for dynamic cross-selling for online retailing. In the first part of the research, we propose an effective yet intuitive approach to integrate temporal information regarding a product\u27s lifecycle (i.e., the non-stationary nature of the sales history) in the form of a weight component into latent-factor-based OCCF models, improving the quality of personalized product recommendations. To improve the scalability of large product catalogs with transaction sparsity typical in online retailing, the approach relies on product catalog hierarchy and segments (rather than individual SKUs) for collaborative filtering. In the second part of the work, we propose effective bundle discount policies, which estimate a specific customer\u27s interest in potential cross-selling products (identified using the proposed OCCF methods) and calibrate the discount to strike an effective balance between the probability of the offer acceptance and the size of the discount. We also developed a highly effective simulation platform for generation of e-retailer transactions under various settings and test and validate the proposed methods. To the best of our knowledge, this is the first study to address the topic of real-time personalized dynamic cross-selling with discounting. The proposed techniques are applicable to cross-selling, up-selling, and personalized and targeted selling within the e-retail business domain. Through extensive analysis of various market scenario setups, we also provide a number of managerial insights on the performance of cross-selling strategies

Investigating the Impacts of AR, AI, and Website Optimization on Ecommerce Sales Growth

E-commerce has evolved into a vital element of modern life by giving customers a quick and easy way to buy products and services online. Businesses increasingly focus on building their online presence in order to remain competitive, which represents a huge change as a result of the growth of e-commerce. Utilizing artificial intelligence (AI), augmented reality (AR), and website optimization is one of the primary ways firms are aiming to improve their e-commerce operations at the moment. While AR can improve product recommendations and the visual component of online shopping by giving customers a more immersive experience, AI can be used to tailor the user experience and boost personalization. On the other side, website optimization can assist companies in enhancing the user experience and raising conversion rates. Businesses can make better choices about how to implement these variables into their operations by knowing how they affect e-commerce sales. This study used data from 190 global e-commerce sites to empirically examine the effects of using AI, AR, and website optimization on the increase of e-commerce sales. The study used a multiple regression analysis to look at how these factors and the rise of e-commerce relate to one another. The study's findings demonstrated that every element had a favorable and significant impact on the increase of e-commerce sales. This suggests that companies investing in artificial intelligence, augmented reality, and website optimization can anticipate a comparable rise in revenue. These results suggest that companies wishing to enhance their e-commerce operations should think about investing in AI, AR, and website optimization. They may improve client satisfaction this way, boost conversion rates, and eventually boost sales. &nbsp