Artificial Intelligence in Retail Industry - Part 1

 

Retailing has always been called a combination of art and science but for much of its existence there’s usually been too much of the former and not enough of the latter.

Traditional retailers' business models arc facing disruption by new entrants who can deliver greater value to customers more efficiently. In recent years, authors have argued that the traditional value chain drives inefficiencies and that the value chain is shortening as manufacturers, third parties and customers are increasingly engaging with customers directly. These inefficiencies combined with the inability to adapt to a changing competitive landscape leaves traditional retailers vulnerable to disruption from market entrants. To remain competitive and survive in an ever-changing and diversified customer market, retailers need to become leaner , more agile, and innovate their value chain by adopting new technologies.

Of the new technologies that are impacting the retail industry, AI has been earmarked as the most transformative. Yet while there is great excitement about artificial intelligence (Al), it has yet to fully deliver on its promise and academics and practitioners are in the early stages of understanding the application of AI in Retail industry.

The whole idea of better understanding the customer experience has allowed retailers to use research and predictive analytics to both better deal with their shoppers and position their stores, products and brands to become more successful.

The rise of AI has posed opportunities and challenges for businesses across the different sectors, including retail, and pushed them to embrace the change or step aside in favour of the AI-vested entrants. Platforms like Amazon have not only created new business but have also replaced brick and mortar retail sales.

Consequently, a significant part of AI’s disruption will contribute to a change in productivity between competitors. Furthermore, without AI, a marketer would find it too difficult to collect and process vast amounts of data from a variety of sources such as websites, mobile app interactions, purchases and customer reviews. Those who are reluctant to accept AI will be competitively disadvantaged, since they will not be able to predict their customers on time, precisely and profitably. Thus, we define AI-based retail innovation as using new AI-powered technologies to profitably and sustainably improve individual customers’ shopping experiences across retail’s different channels.

Using data to run a business has always been a holy grail of retailing but the problem was converting abstracts into specific actionable activities. Now new third party companies have found ways to use numbers and statistics to create more customer-centric merchandising and products. They are even using analytics to help select and train retail employees.

Retail data include data on purchases, online browsing data, social media data, data on mobile usage and data on customer satisfaction. Walmart, for example, collects data around 1 million transactions per hour, leading to 2.5 terabytes of data. AI systems have been trained on big datasets and retailing is considered as a fertile ground for using and growing the AI systems. Retailers invest in several AI applications to exploit these burgeoning results.

AI technologies have been integrated into retail and marketing by using big data analytics to develop customer-specific profiles and their anticipated buying habits. Knowing and forecasting consumer’s demand across interconnected supply chains is more important than ever, and AI technology is expected as an essential component. Retailers use real-time cloud technology to assess several factors for forecasting an outcome. 

Such a practice will provide stores with the power to predict behaviour and then customize the shopping experience of a customer. Most online retailers use machine learning to make customized product recommendations and experience a tremendous increase in their product’s sales. For example, Amazon has generated 35% of its revenue by using a recommendation engine. The practical algorithms analyze past behaviour of a customer (viewed items, frequency of orders, history of searches) and other behaviours of similar shoppers.

Nonetheless, recent technology advances, e.g., quantum computing, could accelerate the evolution of Artificial Intelligence in retail industry. In part 2 of the article, we will review specific examples of how AI has helped some big names in the retail industry optimize their sales process.