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Today artificial intelligence has come out of the realm of science fiction and has entered our homes. There are several web technologies used today in everyday life especially in the ecommerce world. AI (Artificial Intelligence) is viewed mostly as robotics but it has much larger technology range such as machine learning, natural language processing (NLP), learning systems, gaming systems and object detection(Greenberg, 2017). Today AI is touching us in all areas such as online shopping, health care and fraud detection. AI has the power to attract customers with personalization and interaction. This can then enhance the revenue of e-retailers. Today, marketers need to understand the huge potential impact of AI tools on online customer experience (OCE) and e-satisfaction. Several big e-commerce companies such as Amazon, Flipkart and Walmart have realized that mere web presence is not enough to retain their customers.
AI enables machines to achieve difficult and repetitive tasks so that humans can utilize their time and energy for more thinking tasks. AI has enhanced the entire ecommerce shopping experience by adding a touch and feel experience. Slowly AI is bridging the advantage gap between brick and motor stores and online stores. With the integration of AI technologies such as machine learning, deep learning, augmented reality, Virtual try-ons, Avatars and Chatbots in a website, the e-retailers are achieving the objective of providing convenience to customers and improving their online customer experience. This in turns helps the retailer to bring down returns an increase overall revenue generation. The objective of this article is to outline the different AI based technologies which impact online fashion retail.
Artificial Intelligence
Artificial intelligence has been around for a long time but has recently shot into prominence with the advent of the internet. In the area of marketing not much research has been conducted related to AI. Research in AI till now has dealt with the more technical aspects of AI systems. Not much research is done about the effect AI techniques on customer experience. Artificial intelligence (AI) is the concept of intelligent machines being able to carry out tasks and enhance the tasks by learning from experience on their own Geisel (2018). They provide valuable automated solutions to complex problems Crittenden, Biel & Lovely (2019). Virtual try-ons like Lenskrafters, Virtual fit applications like NIKE FIT are some of the AI techniques which improve the customer experience in ecommerce.
Methodology
In this paper a literature review focused on the area of marketing with respect to AI technologies has been conducted. The focus of the search is the customer experience and e-satisfaction derived from these tools. At the end of the literature review we should be able to specify a specific research questions which can then enlarge the existing body of research further.
Literature Review
The paper attempts an analysis of studies which use different types of technologies such as machine learning, recommendation engines, Fit intelligence services, virtual try-ons, personalization services such as chatbots and their impact on online customer satisfaction i.e. e-satisfaction.
The literature review has been conducted using keyword searches in pairs in the electronic database of Scopus using the following words: (1) e-satisfaction (2) Artificial Intelligence; (3) online retailing (4) online apparel retailing (4) Customer experience (5) virtual try-ons, (6) customer personalization through recommender systems (7) Augmented Reality, (8) Chatbots
The inclusion criteria for these keywords were in different combinations on research studies done in the last ten years. After checking all the abstracts of the articles and complete works, 45 articles that were relevant to this topic are chosen. These articles have been discussed and cited in the entire paper. For the literature review conducted
Figure 1. Literature review process used
For more technical topics such as recommendation engines, augmented reality and chatbots separate searches were done on Web of Science with different inclusion criteria. This will allow us to specify the different research gaps and questions emerging from this study (Tranfield & Denyer, 2003).