AI can help offline retailers catch up with agile and data-driven online players through accurate insights about store performance.
Metrics like conversion rate, cart abandonment, page views and so on are a given in the online shopping world. So even if the conversion rate is between 2-4% and on an average 75% of shoppers abandon their carts, e-commerce players are able to retarget these low hanging fruits and nudge them back into the system. The same is, however, not true for offline players who continue to grapple with falling sales and intense competition from their online peers. But artificial intelligence (AI) can prove to be a game-changer for bricks-and-mortar stores in terms of getting real-time insights about shopper demographics, store traffic and dwell time.
Globally, several large brands are incorporating AI-based solutions in offline retail. Alibaba, for instance, launched its FashionAI assistant last year in select stores during the Single’s Day festival. The response, it says, was overwhelming. FashionAI recommends products to shoppers on the basis of what they bring into the trial room, thus, increasing conversion rates and basket size. It is not just Alibaba which is seeking to digitise offline retail. Intel, for instance, has been piloting its responsive retail platform with Levi Strauss for some of its stores. Intel also showcased the pilot with specialist confectionery retailer Lolli & Pops at NRF expo this year (see box).
“The key trend emerging in offline retail is a shift from intuitive or instinctive decision making towards more nuanced financial decision making. Offline players are looking beyond basic automation through POS software and retail ERPs to do more with technologies such as AI, machine vision and video analytics, and VR/AR,” says Saurabh Uboweja, brand expert and CEO, Brands of Desire. He further adds that offline stores will try to mimic more of how web stores serve customers and AI can be rather beneficial in that.
Matching online experiences
Offline stores are increasingly looking to make a shift from static brand touchpoints to smart brand interfaces. This leads to intelligent conversational commerce where a store manager is familiar with how a customer is likely to behave in the store.
Artificial intelligence can also help in the formulation of refined custom made products, which include unique traits and characteristics, says Shahnaz Husain, chairperson and MD, Shahnaz Husain Group of Companies. The company plans to start virtual forms, in which the online customer will fill in various details and then look for the ideal solution or product. “The system will be programmed according to various categories of products, relating them to individual traits, characteristics, various influences and needs,” she adds.
Capillary Technologies which is working with VF brands — Lee and Wrangler — in India is digitising in-store interactions to understand what consumers are looking for.
“We are doing speech and image processing, and applying AI on top of it. The plan is to make the offline store as competitive as an online site through user data,” informs Aneesh Reddy, co-founder and CEO, Capillary Technologies. The conversion rates at online sites are only about 3-4% compared to offline stores where they are 7-8%. Still, e-commerce sites have rich information about customers which is used for retargeting. “We are replicating similar technologies in offline such as traffic flow, identifying products with higher fall through, etc along with the demography of customers walking in to help marketing teams identify the right TG and devise a communication plan,” he adds.
So far, many recommendations in offline stores are linked to past transactions. But more than 50% customers who walk into a retail store are new customers. To address this, Capillary Technologies is building a recommendation tool for new customers by doing a quick visual profile of the customer walking in and provide the kind of products store staff can recommend to such a customer. “This happens in real time. The store staff gets a notification on an app through a device which can be worn on the arm,” informs Reddy.
Around 400 stores of VF brands are currently using Capillary’s VisitorMatrix offering to improve store productivity and profitability. Its other offering, Store Sense, is currently live in three stores of VF brands and provides insights into consumer behaviour to understand what she is looking for.
Then there’s Ittiam, which has two products in retail visual analytics and advanced video encoding, and uses computer vision and machine learning to arm bricks-and-mortar stores to understand customer behaviour. It is working with Coffee Day in deploying computer vision and deep learning technology. “At Coffee Day, we are providing solutions such as analysing dwell time, people counting, shopper window analysis, zone covering and customer demography,” says Mukund Srinivasan, CBO, Ittiam.
Bricks-and-mortar stores typically lack these consumers insights about what the shopper did at the store. By deploying AI such as computer vision and deep learning, retailers can gather insights into consumer behaviour to bring operational efficiency and improve customer experience.
What’s the RoI?
According to Capillary Technologies, by adopting VisitorMatrix, VF reported a 5% increase in conversions through optimised staff efficiency based on store traffic trends and a 15% increase in conversions during certain ‘power hours’ where most conversions took place. Apart from an increase in sales and bigger basket size, AI can also help a retailer to train its staff quickly.
“With churn in offline stores being very high — approximately 30-35% staff churn every quarter — technology can be used to train staff quickly in identifying potential customers and giving them timely recommendations,” says Reddy, adding, “AI can analyse the communication between store staff and customers, and use it to build a coach for store staff too.”
In fact, a McKinsey white paper, Artificial Intelligence — The Next Digital Frontier, says early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and can expect the performance gap with other firms to widen in the future. So can AI offer a quick fix to flagging sales issues? Maybe not. But early adopters can develop strong competitive advantages for long-term sustainability. “Applications like limiting shoplifting, inventory accuracy and focussing on high performing products on the shelf will offer the easiest way to calculate RoI,” says Uboweja.
So while AI can enable retailers to increase both the number of customers and the average amount they spend by creating personal and convenient shopping experiences, can it reduce the divide between data-driven e-commerce players and bricks-and-mortar stores? “AI is about picking up data on consumer behaviour in-store and at point of sale. The real challenge is using the data for commercial benefit. AI will do what you want it to do. But are you asking the right questions?” says Harminder Sahni, founder and MD, Wazir Advisors.
The spectrum of AI application is wide. So whether you are using it in a hypermarket to ensure the shelf is updated or in a fashion store to sell ‘looks’, having people who know how to use data for analysis is more important than having the data, he sums up.
When retail gets personal
How retailers internationally got their stores AI-ready to delight customers
Lolli & Pops, a retailer of gourmet candies, uses computer vision and AI for a personalised customer experience. Through computer vision, its ‘Magic Makers’ recognises loyalty members in real time as they enter the store. The retailer accesses members’ preferences and makes personalised product recommendations. This Proof of Concept was shown in the Intel Booth at the NRF 2018 conference
in January. The company plans to roll out this solution to Lolli & Pops in the first half of 2018.
Global travel retailer Dufry is making the dream of walking into a store built just for you, a reality by personalising its in-store experience for its customers. As you enter the store, signs and kiosks adjust to guide you on a more tailored journey — complete with music, illuminated floors and sound effects.
ShopRite supermarket stores provide customised recommendations to shoppers based on demographics. The technology uses facial analytics to offer personalised content to shoppers based on their age, gender and location — ensuring they spend more time in-store and feel like the store has their best interests in mind.
Neiman Marcus is using smart mirrors at its beauty counters. The Intel-based MemoMi Memory Makeover mirror records the make-up artist’s application process step-by-step and sends it to the customer’s phone. This customised tutorial details what products were used and how they were used. Customers walk away with their own personalised makeover and retailers gain better insights into what customers want.
Hershey’s AWM Smart Shelf uses smart shelves that benefit both the buyer and seller.
Every time a customer walks down the aisle, they can find exactly what they want. AWM Smart Shelf can guide products toward individuals based on customer data and can keep retailers in the loop on shrinking inventory and shopper statistics, simultaneously.