How Small and Mid Size Retail Companies can Leverage AI?

17Aug - by aiuniverse - 0 - In AI-ONE

Source: indianretailer.com

Companies are constantly reinventing themselves, resulting in creative-disruption or creation of new ecosystems. New innovative business models are springing up, inspired by Uber, Ola and Airbnb. The ability to create waves of innovation or ride them is what marks the difference between successful companies and those that are not.

Artificial Intelligence (AI) is one technology that is both the cause as well as the effect of such transformations, and across all industries. However, AI is not just for big companies; even smaller companies create waves or ride them, eventually forming trends. Let’s take one specific industry where the potential of AI is enormous, like in retail, and examine the top five ways how companies can benefit.

Neither AI nor forecasting is new, but applying AI Deep learning for forecasting demand is powerful. According to a McKinsey study, grocery retailers who use AI systems to forecast sales of fruit and vegetables can increase their profit margin (based on total business) by 1 to 2 percentage points. The accuracy of AI-based forecasts for internet sales enabled one trader to reduce his inventory by 20 percent. Combined with the power of AI-based pricing, promotions and campaigns can be personalized resulting in increased sales by as much as 4 to 6% in grocery retail and much more in fashion retail.

It is the ability to crunch in enormous data and scalability in AI that makes it possible to realize leaner inventory and order back office management.

According to a PWC report, “…45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety with increased personalization, attractiveness and affordability over time”.

The online retail store recommended items based on personal buying patterns and others’ shopping carts, made possible thru AI. Now, AI predicts a user’s needs and scours the internet for the best bargains and deals.

Take another example of a personalized service, pioneered by Stitch Fix – an online but personal styling service company, based in California, USA – heavily dependent on AI for its business. It is all eCommerce, but the model does away with a traditional shopping cart, instead relying on a set of style choices, social data feeds and trends which will is used by AI to predict and decide the results. Flexibility in the model allows users to return, free-of-charge, items which are not liked. An important feature of this process is the results are fed back into the model, thus making it better with time. Increased customer experience leads to increased royalty, social marketing and enhanced revenues.

Loyalty towards stores fare better than loyalty towards brands, so customer experience in stores and online are getting traction. This has given rise to a concept called “experiential stores” where customer experience is given more importance as much as the products themselves, where they come and experience the shopping.

77% OF RETAILERS CONSIDER SOCIAL OR EXPERIENTIAL ENVIRONMENTS FOR CUSTOMERS AN IMPORTANT OR CRITICAL AND STRATEGIC PART OF THEIR IN-STORE APPEAL; 55% OF RETAILERS USE AUGMENTED REALITY FOR THIS PURPOSE.

Imagine the scale of such data inter-relationships at play; this is huge, even a mid-sized retail with hundreds of thousands of data points. But we do not need to look beyond AI to create the insights required for providing the rich but personal experiences at these experiential stores. AI also bridges the gap between online and physical stores, bringing in personalization at scale for delivering the much-needed customer engagement in its interactions. Amazon-Go is a case in point – conceptually it is akin to a driverless-car in retail. In effect, it combines the best of both worlds – physical shopping and online, since the biggest pain point in physical shopping is waiting in line to pay your bill.  On this concept (of Amazon-Go), according to Wharton marketing professor Peter Fader: “To the extent that it revolutionizes retail, the idea here is knowing who is buying without relying on loyalty programs. But in addition to knowing who is looking at what, who is picking an item off the shelf and in what sequence — that idea of really seeing everything could have dramatic implications.” It could change the way stores are laid out, he notes, and it could change where a concierge person comes in. “I think that the data part of it could be the big breakthrough, but at this point it’s still icing on the cake.”

One of the most common shortcomings in AI-based business applications, at least in the emergent phases, is its lack of transparency and complexity to use. However, due to advances like Natural Language Generation – NLG , it is possible to develop better interfaces.  Natural Language Generation simply turns data into plain English which translates well for end consumers. This helps build user interfaces in mobiles and desktops to deliver personalized and easy-to-use information, useful for quick and informed decision-making, and reducing costs in running them.

The rise of chatbots has also given way to enriched conversational interfaces. Imagine generating insights when all “intents” work together for a company! The data can be used to design new products, unearth customer service issues and a lot more.

Furthermore, according to a report from KPMG, “…by 2020 an estimated 80 percent of business-to-customer conversations will be conducted by machines. That will have enormous implications for all organizations both in terms of business processes and also future staffing needs”.

AI takes a lot of skill and this is one reason why it is expensive to develop and complex to build. The AI stack also looks a lot different than the traditional software stack. Large or tech-driven companies can handle this, but how do we reach the small and mid-sized outfits? The answer is AI on the cloud. Hosting AI applications as a set of services on the cloud can largely mitigate both the issues. Products and services available in the SaaS model make them modular and affordable as users pay only for what and how much they use. However in some cases there could be a small upfront fee but it is quite affordable on long range plans.To conclude, even small and mid-sized retailers can ride the AI wave and benefit from the trends that are happening in business & technology.

The article has been penned down by Kishore Rajgopal,CEO & Founder of NextOrbit

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