The retail industry is under pressure like never before. Shopping has gone from a communal activity to something you can do on your own, at home. It’s transformed from an in-person experience to a digital one, thanks to cutting-edge technologies like artificial intelligence (AI) and machine learning (ML), retailers can offer their customers a more personalized and omnichannel shopping experience. Businesses must now contend with new challenges like omnichannel shopping, POS systems, ERP and CRM integration, and more. AI and machine learning are helping retailers boost revenue, enhance staff efficiency, and automate store operations.

It’s not hard to imagine a world where you walk into a store, pick up the items you want, and promptly leave without waiting in line. Or, in another scenario: You’re running late for a party and want to buy a party-themed dress, but you’re confused about its color, fit, or style. It is too tiring for you to try all of them in the trial room, so you are offered the option of a virtual fitting room instead. With interactive mirrors, you can visualize how the clothes look and feel in real-time with 3D clothing, find a suitable fit according to your size, swap color options, and get recommendations that fit the party theme best.

These are both examples of artificial intelligence at work. As we’ll explore here, AI & ML are transforming retail that provides customers with a seamless experience regardless of how they choose to shop (online, in-store, or via mobile).

Once simply thought of as science fiction, today’s emerging AI tech is changing the way retailers interact with customers. Read on to learn more about how it works behind the scenes and how it has changed our shopping experience forever!

Point of Sale (POS)

To stay competitive, retailers need to adopt an omnichannel approach, which means providing a seamless shopping experience across all channels, including in-store, online, and mobile. And at the heart of this omnichannel strategy is POS (point-of-sale) systems that can connect all channels and provide a single view of the customer.

AI and machine learning are being used to develop next-generation POS systems that provide real-time insights into customer behavior. This gives an upper hand to improve the overall shopping experience and boost sales. In addition to selling products in brick-and-mortar stores, many storefronts are now offering ‘buy online pick up in-store (BOPIS)’ services, same-day delivery, and online shipping. With so many new ways to order goods, the POS (point-of-sale) system needs to be updated intelligently. When a guest orders a product online, how does that affect the store’s sales floor quantity in the database? If a product is on hold for a pickup order, does that reduce the count on the sales floor? These are a few of the questions, among many, that a modern POS system must be able to answer.

Use data to your advantage

POS, ERP, and CRM systems are providing more than just simple predictions; they’re helping retailers make decisions that can improve their bottom line. By using data to their advantage, retailers can transform the way they do business.

In the early phases, deep learning techniques have been used to gain insights into sales figures or predict customer behavior. Today, however, we’re seeing companies use deep learning for making more informed decisions about stock levels, promotions, sentiment analysis, emotion detection, visual search, personalization, and recommendations – all use cases where deep learning can add huge amounts of value. For example, a fashion retailer might use it to recommend clothes based on the color scheme of an outfit you’re currently wearing (or not wearing). Or a grocery chain could use it to decide how many bananas it needs to stock at each location based on real-time analysis from customers as they shop for bananas—and then adjust supply accordingly so there’s never any shortage of this vital ingredient of banana bread.

Predictive analytics

Predictive analytics is the use of historical data to predict future events, trends, needs, customer behavior, and preferences. It’s typically used to help retailers make better-informed business decisions by predicting what customers want before they ask for it—or even realize they want it.

For example, You’re shopping on Shoppersstop.com when you get an email telling you that a new product has just been released in your favourite color and style. The email also includes a coupon code valid only today that saves 20% off your purchase at Shoppers stop (and permits them to keep sending those emails). The retailer’s predictive analytics software analyzed the purchasing habits of other customers who purchased similar products in the past and determined that many of them returned to buy more items from the same brand or category within two weeks after making their initial purchase; therefore, an algorithm was created that triggers an email marketing campaign when there’s a spike in purchases from this particular vendor or category on their site—before potential buyers even know what they need or have time to think about buying it themselves.

Visual search

Visual search is one of the most popular applications for AI and machine learning in retail. It works by identifying objects in an image, then allowing you to search for similar items or products. The technology can be used to improve staff efficiency and customer experience, as well as sales.

The image below shows how visual search works: you upload a photo of an item (such as a pair of earrings), then use your phone camera to point it at other products that could match those earrings. You’ll get a list of related items along with pricing info from the store where you took the picture—and as you move around different areas within the store, the visual search will update itself with new matches based on what else is around you!

Faster checkouts

• AI-powered shopping carts are all the rage these days, but shoppers can also expect to see some of these technologies at their local grocery store or pharmacy very soon:
• Facial recognition scanners that let you pay for your items without having to take out your wallet or phone.
• Predictive analytics tell you which product you’re likely going to buy next based on what items are currently in your cart and what products have been added recently.

For example, if one day a person only buys milk, eggs, and bread—and then suddenly gets all fancy by picking up some sea white prawns after their usual trip—the system will know they’re planning a special -dinner and will recommend other items for starters or entire. This technology could be especially useful for those who hate shopping—but still love eating!

As retailers get more creative about how they use advancements in AI and machine learning technology throughout the business (from supply chain management to marketing), one thing is certain: we can expect our future grocery shopping experiences to be unlike anything we’ve ever seen before!

Location-based marketing

Location-based marketing is one way that companies can use machine learning to collect data on customers. It involves using a person’s location to provide them with relevant information and offers, such as coupons or promotions. This technology has been used for years in the form of advertisements, but it’s becoming more sophisticated. For example, if you’re near a coffee shop that sells your favorite brand of coffee, you might start seeing ads for their products on your phone or laptop when you’re browsing the internet at home.

Mapping technology can also help store owners target customers geographically. Stores usually have long lines during peak shopping hours like Black Friday sales or BOGO promotions—so why not put some machine learning into play? Retailers use this type of software to analyze where their most loyal customers live relative to their retail locations (and vice versa), then strategically place stores in areas where they’ll get the most foot traffic and revenue over time by showing those locations on their maps so shoppers know where they are located before, they head out for shopping in person.”

Conclusion

The future of retail is here, AI and ML are changing the retail landscape by making it easier for retailers to boost revenue, enhance staff efficiency, and automate store operations. These technologies are also making it easier for retailers to provide a personalized shopping experience for their customers. This is allowing businesses to automate store operations, create intelligent planograms and predict customer behavior. To stay competitive and keep customers coming back, you need to get ahead of this trend before your competition does.

Author: Nidhi Tiwari a Senior Executive at iE3 Innovations Pvt. Ltd., a technology firm.