The Business of Automation and AI-Powered Retail Predictions

Automation and artificial intelligence explained

Automation is the use of technology, such as computers, business equipment and robotics, to complete tasks normally performed by humans. The key benefits of automation are that it reduces the risk of human error, simplifies running your business, increases your efficiency and cuts costs.

Artificial intelligence (AI) is the use of technology to mimic human intelligence, specifically reasoning and problem-solving. AI adjusts its actions according to what it learned, making it far smarter and more flexible than rules-based models. David Curtis, chief technology officer of AI-powered supply chain management software RobobAI, is excited about the future. He told business.com that “AI and automation are set to dramatically transform the retail industry across both the front-end consumer buying experience and back-end business operations in the coming years.”

Combining AI and automation can deliver significant benefits to your business. Let’s look first at how it can improve day-to-day operations:

• Manage your inventory levels: The technology can track and restock items automatically based on real-time revenue data and sales forecasting.
• Optimize your pricing: By adjusting your prices to match customer demand and real-time market conditions, you can stay ahead of market trends and your competitors.
• Personalize your marketing: Instead of sending out 10,000 identical pieces in an email marketing campaign, you can send out individual emails to each customer with content based on their preferences and behavior, boosting engagement and conversion rates.
• Improve your after-sales support: AI chatbots and virtual assistants can field questions quickly and accurately, leading to faster responses and happier customers. Human colleagues can spend their time resolving more serious issues. [Related article: How Digital Assistants Can Improve Workplace Productivity]
• Streamline your order fulfillment: As soon as an order comes into your online store, automation takes it through each stage from order processing, packing and shipping.

Thanks to AI’s ability to analyze data and solve problems, it can help retail management teams run their businesses better, too. Here are six examples:

• Predict your customer buying patterns: You’ll be able to forecast which products will likely be in most demand. This means you’ll know how many units you need to purchase, helping you with inventory planning.
• Improve your inventory allocation: You’ll know how much inventory you’ll need, how many of each item every individual physical store requires as well as what your online store needs.
• Boost your cross-selling revenue: You can analyze individual and company patterns to determine which products your existing customers will want. Base your stocking decisions on factors like average order value to make them affordable.
• Identify your VIPs: Find out which of your customers are likely to spend the most in the next three months so you can focus on sending personalized marketing campaigns and offering special incentives to drive sales.
• Target your future VIPs: The historical data you hold may be able to show you which of your customers are likely to be your future VIPs. You can create special promotions to help them on their journey.
• Segment your customers: Traditionally, segmenting customers has been a laborious task for a marketing team. With the power of AI, you can easily identify new segments you may not have been aware of before, too.

How AI-powered predictions are changing retail

Using AI improves the in-store and online experience for customers and staff. Here are some of the ways retailers can harness the predictive power of AI and machine learning.

Improve the in-store experience.

Retailers create better in-store experiences for shoppers with the following adaptations:

• Adaptive in-store displays: These displays show shoppers individual offers on wall-mounted in-store LED screens as they walk through the store, triggered by their near-field communication and Bluetooth-enabled phones.
• Locally tailored window displays: Squeeze maximum value from window displays by only showcasing items that AI apps predict local customers will most likely want throughout the year.
• Personalized customer experiences: Invite shoppers to take guided 60-minute shop tours. Make sure the items AI indicates that they’ll want are stocked and ready before they come in. The more personalized the customer service, the higher your chance of maximizing sales.
• Automated customized experiences: Retailers should fit changing room mirrors with sensors. If someone’s facial features suggest they’re unhappy with a garment they’re trying on, get AI to suggest alternatives on an adjacent screen right away. If they see something they want, a customer could press a button and a shop assistant could deliver the desired items directly to their cubicle. 
• In-store tablets: Invite a customer to use an in-store tablet if what they want is not in stock at that location. If they find something they do want from your full range of inventory, they could place the order there and then arrange for delivery to their home or the nearest store.
• Store layout: Changing a retail store’s layout is expensive and disruptive. Before committing to a new layout, get AI to model how people would move through the store based on different configurations and how that affects spending.
• Smart shelving: Smart shelves with sensors that use AI can keep stocking levels up to date. The inventory team will be notified when they need to order new products so there’s enough stock to cope with demand. 
• Voice-activated assistants: In-store voice-activated assistants will be there to provide customers with product information, recommendations or store directions. This will make for a smoother shopping experience and place less demand on salespeople’s time. 
• Cashless stores: As technology develops, the need for cashiers and tills will dwindle until stores all operate on self-serve checkouts taking away human interaction and replacing it with AI. We’ve seen this in action with Amazon Go stores.

Know which products to present to customers online.

AI can also drive online retail sales with the following features:

• Customized homepages: Use your homepage to show customers the products they’re most likely to want based on their browsing behavior and past purchases at similar times during previous years.
• Suggestion tools: Retailers should put “more like this” images and buttons on each product page to show customers something complimentary based on their preferences and what other clients bought. For example, a customer might see a pair of stylish shoes that complement the jacket they’re looking at.
• Personalized marketing: AI can create the best email marketing campaigns and text message marketing that contain individual messages for each customer. The content is based on their responses to previous campaigns and browsing/purchase history, which can increase conversion rates. Aniket Kumar, a digital marketing specialist at Kellton Tech Solutions Limited, remarked that AI-powered personalized shopping experiences are the key trend to watch. “Retailers will use AI to analyze customer data and deliver tailored product recommendations in real time, both online and in-store. Product recommendation engines, commonly used by large retailers, are now affordable for smaller businesses, allowing them to boost sales by suggesting the right products to customers at the right time.”
• AI chatbots: Imagine going to your favorite retailer and having a two-way conversation with a chatbot who can help you identify what you want, according to your answers and AI’s knowledge of what you’ve bought or asked about previously. 
• Changing room experience: With virtual changing rooms to augmented reality shopping, the future of online shopping will begin to look more futuristic and deliver the same experience as shopping in-store.

“The most significant trend in retail AI is the evolution of chatbots and related AI systems,” Paul Ferguson, AI consultant and founder of Clearlead AI Consulting said. “These systems are now beyond answering basic questions; they’re now capable of understanding customers’ needs and recommending products that precisely match those requirements. For example, a customer might ask, ‘I’m looking for a waterproof, lightweight hiking jacket under $50,’ and the AI can instantly provide a list of matching products, complete with real-time stock levels and pricing.”

Become a better retail employer.

Here’s how employees can benefit from the introduction of AI across the business:

• In-store staff can upsell: Sales clerks can upsell by showing customers what other people who have purchased a product also bought at the point of sale. AI could suggest other products based on an individual customer’s previous buys as well.
• Web designers have better roadmaps: AI keeps web designers and developers aware of where the sales process is breaking down. Based on interaction and engagement data, they can then create optimal, personalized and unique online experiences for customers. 
• Staffing levels meet demand: Give customers the high level of service they deserve by making sure there’s enough retail staff to serve them. AI can help retail companies predict staff needed in-store, in customer services and at their direct-to-consumer distribution warehouse.
• Faster problem resolution: AI-assisted tools allow employees to track live inventory levels, product details and customer history, enabling staff to resolve business issues quickly and effectively. 

“Contrary to popular belief, small and medium-sized retailers aren’t at a disadvantage when it comes to AI,” said Devarshan Patel, a co-founder of Koru Global. “In fact, they can adopt nimbler strategies than large enterprises weighed down by legacy systems. “The key for smaller players is leveraging AI for hyper-localized, personalized experiences — areas where giants like Amazon struggle due to their scale.”

Run a tighter, more efficient retail operation.

AI benefits the wider business, including the procurement and leadership teams with these advances:

• Trendspotting: Fashion retailers now use AI forecasting to decide styles, patterns, colors and fabrics for their collections by analyzing blog and social media content. All retailers can also use wider economic data to predict up-and-coming products as well as those on their way out.
• Sentiment analysis: Find out what consumers think about your brand and its products by analyzing real-time mentions on social media and discussion forums. Get to know user pain points to guide product development.
• Tighter supply chain management: Knowing what customers do and don’t want means procurement teams and product buyers can better specify what products they want from suppliers. “From a back-end business operations perspective, data and AI will play a critical role in supply chain management, enabling predictive analytics for demand forecasting, optimizing inventory levels and reducing stock outs or overstock situations,” said Curtis. “AI will also play a role in dynamic pricing and demand forecasting to align with market conditions and it will be imperative in supply chain risk management.”
• Better inventory management: Buyers can also predict how many of each item is needed and when they’ll need it. So, there’s less chance of running out of stock, which means greater revenues, higher profits and happier customers.
• Reactive pricing: Dynamically change your prices in-store and online to match or beat competitors’ pricing, the season (for example, higher prices for do-it-yourself products in the spring) and the weather (for example, running a day sale if rain is expected to increase footfall). [Related article: What is Dynamic Pricing and How Does It Affect E-commerce?]

Ferguson thinks “In the e-commerce space, AI can combine a customer’s entire digital footprint — from browsing patterns and purchase history to social media engagement to create bespoke shopping journeys. This manifests in hyperpersonalized product recommendations and dynamically optimized website layouts that spotlight items most likely to resonate with each unique visitor. Not only can these changes boost conversion rates, but they also help foster brand loyalty.”

Welcome to the new retail paradigm

The old methods of making predictions were time-consuming and prohibitively expensive. Data scientists had to construct a new data model for every prediction they were asked to make and each prediction required two months’ lead time.

Meanwhile, AI-powered predictions are affordable and can give you answers from an app within a day. Better still, many apps can check their own results to find ways to make better predictions in the future.

AI allows you to improve engagement, introduce deep customer personalization into your marketing in-store and online, increase sales and bolster customer loyalty — meaning you can be proactive instead of reactive in the face of a crisis.

When a customer adds an item to their basket online, the algorithms work hard to suggest other items the product should be. It may be a similar item or something that complements an item like a handbag to match a pair of shoes. AI technologies analyze browsing history and past purchases to suggest things to customers before they’ve even considered them, leading to a more personalized shopping experience. 

Machine learning and AI prediction tech represent the future face of modern business. If you want to get ahead, you’ll need to get there before your customers even realize where they’re headed.

 AI and automation challenges for retailers 

Integrating automation and AI will unlock many opportunities but it won’t be without its challenges. These include:

• Budget: Cost is a vital factor, especially for small and medium-sized enterprises (SMEs), said Ferguson. “This includes the initial investment and ongoing expenses, like maintenance and updates.” 
• Application. Retailers should start with well-defined business objectives for AI initiatives, such as improving customer personalization, optimizing supply chains or reducing operational costs,” according to Curtis. “Conducting pilot projects with measurable outcomes allows retailers to assess the potential ROI [return on investment] before scaling AI solutions across the business.”
• Resistance to change. Resistance to change is often cited as a major challenge in AI adoption, offered Ferguson. Although it is usually less of an issue in SMEs than in larger organizations. “I find that smaller retailers are generally more receptive to new technologies, often viewing them as a clear means of gaining a competitive edge in a market dominated by larger players,” he said.
• Implementation. Patel warns retailers not to make the “mistake of opting for ‘all-in-one’ AI solutions, which often turn out to be cumbersome and inflexible. The future lies in composable AI — modular systems that can be tailored to specific retail needs, rather than a one-size-fits-all approach. Retailers should prioritize flexibility over features. He suggested starting “with narrow, focused applications like AI-powered pricing engines or customer sentiment analysis and integrate gradually as the business evolves. This allows retailers to adapt more fluidly without getting trapped in an overpromised, underdelivered system.”

“For small and medium-sized retailers looking to compete with larger e-commerce players, practical applications of AI can level the playing field,” said Jason Sherman, CEO of AI chatbot provider Vengo AI. “Tools like customer data analytics and automated customer service solutions allow these retailers to deliver personalized experiences without the need for large-scale investments” but he cautioned that “when choosing an AI or automation system, it’s essential for retailers to select scalable solutions that align with their unique business goals.”

 Zac Johnson contributed to this article.