For 20 years, online retailers have wielded an advantage over their Main Street competitors because of the data they have on their customers and their ability to interpret it.
The vast majority of traditional retailers have now opened their own e-commerce stores to get back that market share. They’re building up their customer databases and using emerging artificial intelligence tech to become more efficient and to improve their revenues.
In this article, we explore what AI is. Then, we share 18 examples of how AI helps retailers cut costs, keep their customers happy, and compete with their online rivals.
Automation and artificial intelligence explained
The retail business is complicated. The stakes are so high that retail stores need automation and AI to compete. On many issues, it’s now too risky to rely on human decision-making alone.
Thanks to AI’s ability to analyze data and solve problems, it can answer the following questions quicker, cheaper and, over time, more accurately:
- What will customers want to buy in Q4 and in what volume?
- How many of these items do we need to hold in individual store inventories and for online customers?
- If a customer wants to buy X, what else would they be interested in purchasing?
- Who is likely to spend the most money on products in the next three months?
- Who is most likely to upgrade to a premium product?
- What group of customers will respond best to a new promotion?
How AI-powered predictions are changing retail
Using AI improves the in-store and online experience for customers and staff. Its predictions also make it possible to purchase just the right quantities of stock and raw materials, reducing costs and waste for retailers and their supply chain partners.
Investment in biometrics and facial recognition tech, which can detect emotion in shoppers as well as measure how long they spend in the store and which products they look at, is expected to reach more than $11 billion by 2030 from its current market size of $4.3 billion, according to Verified Market Research.
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– (NFC) 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. Get shoppers in for their own 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. Fit your 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 store layout is expensive and disruptive. Get AI to model how people would move through the store based on different configurations and how that affects spending before committing to a new layout.
Know which products to present to customers online.
AI can also drive online 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. Put “more like this” images and buttons on each product page to show them something complimentary based on what their preferences are and what other clients bought. For example, a customer might see a pair of stylish shoes that complements the jacket they’re looking at.
- Personalized marketing. AI can create email marketing campaigns and text marketing campaigns 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.
- AI chatbots. Businesses can offer an in-store experience, but online. Imagine going to your favorite store and having a two-way conversation with a chatbot who can help you identify what you really want, according to your answers and AI’s knowledge of what you’ve bought or asked about previously.
Do you conduct business internationally? The best live chat software can now provide support in over 100 languages, thanks to integration with Google Translate.
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 on top of where the sales process is breaking down. They can then create optimal, personalized and unique online experiences for customers based on interaction and engagement data.
- Staffing levels meet demand. Give customers the high level of service they deserve by making sure there’s enough staff to serve them. AI can help companies predict staff needed in-store, in customer services and at their D2C distribution warehouse.
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.
- 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 DIY products in the spring) and the weather (for example, running a day sale if rain is expected to increase footfall).
Welcome to the new retail paradigm
The old ways 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 come up with, and each prediction required two months’ lead time.
AI-powered predictions, on the other hand, 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.
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 customer even realizes where they’re headed.
Additional reporting by Zac Johnson.