A Qualitative study on Innovation and Dimensional aspects of Omnichannel Retail Business Model

The purpose of the study is to elucidate linkage of Omnichannel retail business model with innovation and technological advancements. The study is exploratory and qualitative in nature, based on primary and secondary data sources collected from varied retail sectors such as fashion, furniture, eyecare and electronics . The study has used Business Model Canvass (BMC) as a tool for strategic analysis. The study presents findings about business model and strategies in Omnichannel context from Indian retailers. The findings of the study posits four main dimensions resultant of digitalization and technological advancements in Omnichannel retail, namely Omnichannel Intensity, Organizational Structure Integration, Operations and Supply Chain Management Innovation, Data Analytics and Intelligence. Cross-channel Integration and Data Analytics & Intelligence have been found to be contributing enormously towards the strategic growth of Omnichannel retailers, thus emerging as the prominent managerial implications of the study.

AI and Customer Experience in the Fashion Industry

This chapter portrays artificial intelligence with the improvement of customer experience on online and offline fashion purchases. The presence of AI in the retail business is turning into a vital part of the customer experience. AI attempts to disclose approaches to interpret with the end goal for organizations to make a genuine use out of them, either online or offline. Thus, with this research, the authors additionally give knowledge on how this experience of AI could be improved in later fashion purchases and how it will undoubtedly be part of the everyday customer experience.

Competitive Advantages in the Turkish Retail Industry

Retail is one of the most important industries that is continuously improving around the world and in particular in Turkey. Competitive advantages owned by firms keep businesses one step ahead of their competitors. The main objectives of this chapter are determination of the main competitive advantages and capabilities for firm performance, determination of competitive advantages and capabilities which are relatively important for firm performance, and determination of competitive advantages and capabilities that are mainly used under the effects of environmental dynamism for firm performance in the Turkish retail industry. In this chapter, retailers are evaluated into two groups as organized and traditional. Totally, 50 competitive advantages and capabilities were identified in the research. The results of the study would be beneficial both for retail literature and retail business life.

Customers’ Perception towards CRM Practices in reference to Organized Retail in India

Retailing being one of the fastest growing sectors is facing tremendous competition from domestic as well as foreign players. Customer retention is an area of serious concern for online as well as offline retailers in the Indian economy, driving players in the Indian business scenario to bring radical change in their CRM practices which affects their customers perception. Retailers are highly concerned in identifying the key drivers of service execution that model customer shopping satisfaction and enhance loyalty. Customer satisfaction & loyalty is an outcome of sound customer relationship management practices. Thus, to ensure the prolong survival in the highly aggressive retail business environment customer centric approach is hour’s need. This paper critically analyses Customer’s perception towards CRM practices with reference to organized retail in India. Major parameters such as reliability, product attributes, customer service, convenience and ambience are identified to evaluate the customer perception towards CRM practices. The relevance of each parameter is analyzed with special reference to both the formats of organized retailing i.e., online & offline. For in-depth analysis of each parameter in both the formats of retail, they are further divided into sub-parameters. A survey is conducted for collecting data about customers perception towards these parameters. A statistical tool two-way ANOVA is used to analyze the variation in customer perception between online and offline retail. Based on analysis and major findings of research a conceptual model is also being proposed to get a pictorial view of how these parameters are affecting customers perception towards online & offline retail.

Marketing Investigation: Customer Relationship Management and Innovation to Improve Competitive Advantage and Business Performance

The purpose of this study is to analyze the relationship between customer relationship management (CRM) and competitive advantage and to analyze the role of innovation capabilities in mediating CRM with a competitive advantage. In addition, it examines the role of competitive advantage on retail business performance. The study was conducted on retail companies in Indonesia using a quantitative approach, and responses from 255 retail owners/managers were obtained via the random sampling method. The analysis technique used is structural equation modeling (SEM) using SmartPLS software. The research findings reveal that retail competitive advantage can be achieved through the implementation of customer-oriented CRM. CRM is also able to support innovation capabilities, which will further increase competitive advantage. It is important for retail companies to strengthen the implementation of customer relationship management in order to achieve business performance because CRM is proven to increase the ability of innovation and competitive advantage of retail companies. Research on a broader type of company is needed to provide a better level of generalization.

Sistem Pendukung Keputusan Pemilihan Kain Terbaik Di Toko Crown Textile & Tailor Dengan Metode Simple Additive Weighting (SAW)

The retail business continues to grow rapidly along with the development of science and technology in the era of globalization, but the purchase of fabric is still done manually and not computerized, such as in choosing fabrics, only looking at the shape of the color and the price tag, if it is suitable and likes it is taken immediately without thinking what type of fabric to use. used for these clothes, when they have been made into clothes or clothes, do not match the criteria desired by consumers and many ready-made clothes are returned because the color becomes dull or faded, and does not match the desired motif. The problem in this study is to determine the selection of the best fabric using SPK with the Simple Additive Weighting (SAW) method in selecting the best fabric at Crown Textile Tailor built using PHP MyAdamin programming. The decision support system method used is Simple Additive Weighting (SAW), because it can determine the weight value for each attribute, through a ranking process selecting the best alternative from a number of alternative data, the results obtained are Viscose Rayon Fabrics worth 0.933, (Rank 1), Brukat Lace Fabrics are worth 0 .9 (Rank 2), Toyobo Cotton Fabric is worth 0.716 (Rank 3), Japanese Cotton Fabric is worth 0.700 (Rank 4).

Proposed Model of Business Retail Continuity Process during Pandemic Covid-19 Based on Risk identification and Response

Recognizing competitive strategies, the retail industry must anticipate business risks. Business risk is related to managerial risk, and it depends on its ability to adapt its policies to unforeseen events and changes. Currently, the whole world is facing the Covid-19 outbreak. In facing the challenges and risks from Covid-19, retail businesses must manage and respond to these risks so that their business can recover and business continuity is realized. This study seeks to identify the risks experienced by retail businesses in Indonesia during the COVID-19 pandemic and what risk management strategies are appropriate for business continuity and retail business recovery. Previous studies have explained the impact of Covid-19 on the retail industry. However, there is still a lack of research that tries to identify the risks and how to overcome them. This research will try to fill that gap. Semi-systematic or narrative review techniques were conducted in this study. The study’s result is a framework related to risk management in retail industries. This study contributes to knowledge and practical implication regarding the risk that the retail industry is experiencing during the COVID-19 pandemic.

A Survey on Hybrid Recommendation Systems

As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms such as Amazon and Flipkart provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems which uses customer’s data like interests, purchase history, ratings to suggest new products which they may like. Recommendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the customer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online platforms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering and Content-Based Filtering.