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This blog is published bySethuraman Janardhanan
Artificial Intelligence (AI) has taken the Business world by storm. The sheer market size of AI software and systems has already become big enough to be taken seriously. In 2015, the global artificial intelligence market was worth US$126.2 Billion and is projected to reach a value of US$3,061.35 Billion by the end of 2024. The market is estimated to spurt to an exponential 36.10 percent CAGR between 2016 and 2024.
There is hardly any industry where it isn’t going to be a game changer and by no means, retail will be an exception. Let us briefly look at the vast potential Artificial intelligence throws up to let the retail industry evolve into something we may not have imagined a few years ago.
AI is capable of providing significant benefits to both the shoppers and the sellers in retail. Some of the key retail areas where AI is making big strides are:
1. Store Foot Print Optimization – Whether and where to open a new store has always been a difficult call to make for retailers. As per some estimates, this decision alone could be responsible for savings/revenues of several millions (depending on the size and format of the store). AI is being used to figure out what can be the best location for opening a new store. Smart algorithms take into account historical data like sales, demographics, distance from competitors, nearby events and current data like weather patterns to decide whether to open a new store and where to open it. These algorithms can provide the key drivers for the new stores success.
2. Staffing – Bad staffing not only leads to lost sales but also dents the brand image because of poor customer experience. Predictive modeling using historical data like foot falls, sales, marketing campaigns can allow dynamic staffing which can prevent these issues leading to higher sales, better customer experience and higher customer retention.
3. Product Mix Optimization – Sometimes, extraneous variables like climate change can have dramatic effect on retail companies. For example, extended summers in North America led to increased purchase of summer clothing, reduced purchase of winter clothing and increased in-store shopping as compared to online shopping. Retailers with advanced AI algorithms that could correlate environmental variables with product mix were able to stock the right inventory, create better in-store offers and thereby sell more.
4. Optimizing the Supply Chain – The single most important factor that affects the bottom line in retail is supply chain management. Excess inventory increases costs and stock outs dent the reputation and lead to customer attrition. Prescriptive demand models based on past sales for different products, events, marketing campaigns, seasonality etc. can ensure accurate demand and supply forecasting so that these problems don’t occur. They can ensure optimized logistics and effective utilization of operational funds.
5. Improved Marketing and Hiring – Prescriptive and predictive modeling based on historical sales, marketing campaigns, website discounts, events and competitor events related data, can make the marketing campaigns much more effective by clearly figuring out what worked and what will work. This will help the company grow, engage and convert audience.
Advanced predictive AI apps can use variables like historical employee performance and attributes (i.e. background, previous sales experience, previous jobs, focus, etc.) to create the ideal profile of employees more inclined to stick longer with the company. This will reduce attrition, hiring costs and the effort.
AI is making progress in some other areas that will have large-scale implications for the retail businesses
6. Intelligent Assistance / Conversational Commerce / Personal Shopping Assistants – Consumer experience is revolutionized by the use of Big data along with natural language interfaces and machine learning. This will create a near human interaction. The digital intelligent assistants can be designed with the brand personality in mind and they can be much quicker and accurate when it comes to assisted shopping. North Face is experimenting with AI through its Fluid Expert Personal Shopper, powered by IBMs Watson cognitive computing technology. It gives the users, a more intuitive search experience using natural language capabilities. Sephoras Chabot launched on Kik and the personal shopping assistant launched by shopping app Spring are the other examples.
7. Virtual Mirrors – Decision making inside the stores, especially in apparel and accessories, has been a nightmare for the customer. The single question, what suits me? or what looks the best on me?, keeps bothering the customer before every purchase. AI has come up with the solution in the form of Virtual Mirrors. A virtual Mirror helps the customer try various options before buying one. It consists of a life size mirror which overlays the image of the buyer with the pictures of the clothing and accessories that they select. This is done using the gesture and touch based interfaces. It allows the shoppers to mix and match outfits and accessories and make the right buying decision. In fact when the company helps the buyer buy right, it improves customer retention, revenues and through this technology also gains useful data about consumer demographics, body types and preferences.
Magic mirrors are a new spin on the virtual mirrors with even more functionalities included. Rebecca Minkoff stores in New York, San Francisco and Los Angeles feature interactive mirrors that allow consumers to adjust lighting, browse virtual racks, request a different size, and send items directly to checkout. Memory mirrors are another variation to the same concept. At select Neiman Marcus stores, these mirrors give shoppers a unique perspective in the fitting room as well. The mirror records an eight second video of each outfit as shoppers give their potential looks a twirl. Shoppers can virtually try on an item in different colors, share with friends and compare multiple videos side by side, to make final purchase decisions.
8. Gesture Recognition – Information hunting inside the store is another chore customers hate. AI has come up with in-store advertising and Gesture walls have revolutionized information search. These walls are used to promote offers and tell the customers about the store and what all it has. The customer can search offers, check merchandise, like and purchase stuff using these gesture walls at the flick of their hands from a distance. These walls can be made part of the store windows to attract customers and to ensure that the business happens even when the store is closed. Customers can search merchandise, purchase it and make payments through these walls even when the store is closed.
9. Visual Listening – AI algorithms are being used to analyze pictures on platforms like Instagram to understand what is it that the consumers are sharing about a brand.
10. Personalization – Matching algorithms will be used to provide buying suggestions to the consumer. This will be based on real time analysis of purchase history, age group, demography, ethnicity, season and many more such variables.
11. Omni channel shopping – In store and online selling efforts will be synchronized to sell more by knowing more about the customer. To analyze an example, the moment a shopper enters a high street store; the store staff will get information about all the products reviewed by the customer online. This will help the retailers to make better sales pitches and offer better consumer choices inside the store.
12. Robots – Some experts predict that 90 per cent of call centers will be replaced by AI in five years time, and Googles Rat Kurzweil predicts robots will reach human levels of intelligence by 2029. Robots are already doing a lot of jobs that humans used to do in stores like clean floors, stack shelves, retrieve items in warehouses, package goods and operate forklifts. The world’s first humanoid robot Pepper developed by Japans Softbank is, however, many steps ahead of the traditional robots because of the fact that it has emotions. It is already replacing information kiosks in retail and is powered by IBM’s Watson. Future developments will enable Pepper to analyze data, make personal recommendations, understand human language/emotion, and tap into data such as social media, videos, images and text.
Like any evolving technology, the actual extent of the change that AI is going to infuse into retail, will be realized only in the times to come as this trend is going through its evolutionary phase. One thing, however, is certain. AI will change the face of retail irrevocably throughout the retail value chain.
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Sethuraman (mailto: [email protected]) manages Retail, Travel and Online Analytics at Happiest Minds. Sethu’s primary areas of expertise include marketing, risk & web analytics like Finance, Retail, Travel and Technology. Sethu holds a PhD in Systems & Finance from Indian Institute of Management Calcutta and is regular guest speaker on Analytics and Strategy.
Sethuraman Janardhanan Sethuraman (mailto: [email protected]) manages Retail, Travel and Online Analytics at Happiest Minds. Sethu’s primary areas of expertise include marketing, risk & web analytics like Finance, Retail, Travel and Technology. Sethu holds a PhD in Systems & Finance from Indian Institute of Management Calcutta and is regular guest speaker on Analytics and Strategy.
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