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The Best Examples of AI in Retail

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The Best Examples of AI in Retail
AI or Artificial Intelligence has changed the way that retail businesses work, in most cases, for the better. Businesses historically beset by problems arising from mundane repetitive tasks, like restocking shelves and answering repetitive questions asked by customers, are now able to free the time of their human executives to focus on other tasks that add value to a business.
 
The article will explore some of the best examples of AI for retail business and how these have improved business operations.
 
 
Product Content And Inventory Management
 
For many retailers, logistical problems relating to products being out of stock can add up to hundreds of thousands of pounds, if not more, in lost revenue. People who would otherwise have made a purchase are instead frustrated when items are out of stock or otherwise unavailable. The problem is not confined to lost revenues however. Customers who want to purchase an item in-store, or online can find that it has been sold out or is simply not available and this in turn can diminish the reputation of that brand, who will, in many cases have been advertising a product they simply can’t deliver.
 
Homebase, for example was recently voted the worst online retailer in research by consumer rights powerhouse Which? magazine. One of the most significant criteria in identifying the worst retailers was how “up-to-date” a retailer website actually was. In the case of Homebase, it was found that products were habitually advertised for sale, despite them being out of stock. This, in turn, inconvenienced customers and wasted their time.
 
These kinds of problems experienced by retailers are being effectively addressed using AI powered systems, focused on product content and inventory management.
 
Particularly for large retailers, with products on sale in different countries, the application of AI within the sphere of product content management has fundamentally changed the way that product inventories are managed. This, in turn, impacts how supply chains and logistics are managed and how restocking is carried out. The overall result has meant greater efficiency for retailers, who can now analyse trends in historical data and point of sale data to gain a better insight into when restocking needs to be carried out, when products need to be classed as “out of stock” and when product supply needs to increase. This insight allows businesses to ensure that customers are not inconvenienced by products being “out of stock”, in addition to preventing or reducing the losses that businesses experience when they are faced with delay in being able to restock products that are selling well.
 
Some large retailers are going further than just monitoring their inventory using AI. Some are incorporating AI into the monitoring and physical restocking processes, for example by investing in robots that perform the analysis of inventory and logistics, in addition to performing the physical process of restocking. In 2018, Walmart used robots to restock their shelves. These robots are programmed to scan shelves and identify what needs to be replaced, before physically gathering suitable stock replacements and placing these on the appropriate shelves. It isn’t a completely perfect process yet, because humans are still needed to spot error that robots are not capable of spotting, for example the Walmart robots, mentioned above, scan the shelves and take pictures of stock. Human employees then check that the robot has made the correct assessment of the stock that needs to be replenished and the robot is allowed to continue the stock replenishing process.
 
On the other hand the overall error rate, when AI powered robotic operation is compared with human executive of the same tasks is much higher for humans. Humans repeating mundane tasks over and over again are far more vulnerable to errors compared to AI. As such, while the process of stock replenishment can’t yet be fully transferred to AI powered robots, the system which uses robots is much more effective and saves retailers the costs of paying human staff to perform these tasks. Other advantages are that robots don’t get tired and don’t get injured, so much more work can be expected from a robot compared to the average human employee. Again this results in efficiencies for the retailer, who can save money, in addition to reducing error and waste.
 
 
AI In Price Comparison
 
An age-old problem for businesses is the issue of price comparison. What happens if a competitor selling the same or similar products sets the price of those products lower? This invariably leads to lost revenues for many retailers. For years, retailers have addressed this problem by setting and adjusting their prices manually.
 
With the advent of AI however, came a better way to solve the problems surrounding losses as a result of competitor price adjustment. Sophisticated computer software packages can now scan the internet and provide retailers with a daily breakdown of how competitors are setting and adjusting their prices. Retailers can use this information to ensure they are not being “undercut” in terms of price by their competitors.
 
 
Point Of Sale Data Analysis
 
AI allows for point of sale data to be effectively analysed. This is extremely useful for retailers, particularly large retailers, because it allows them to forecast how sales for particular products increase or decrease over a set period of time. When this information is fed into related systems like inventory management, storage, logistics, ordering and advertising, it allows retailers to create widespread efficiencies and as a result increase their sales and revenues.
 
For example, if a retailer gets a “head’s up” that one product is selling particularly well (perhaps better than expected) in one country or region, it can take the decision to increase spending on advertising that particular product in that same region. These informed decisions then allow for retailers to “ride” on surges in demand and reap the associated  financial rewards. The same applies in reverse. If a product is doing much worse than expected, a retailer can take the decision to stop spending money on advertising, and perhaps even remove the product from its inventory altogether – making way for products that are more likely to sell well.
 
 
AI And Customer Service
 
AI is making customer service much easier for retailers to manage, because it removes many of the repetitive, and mundane tasks that human executives would traditionally have been responsible for – an obvious example being confirming opening hours of stores, or checking stock levels. Different retailers are thinking of increasingly innovative ways to deliver information to customers, for example some stores are using robots, holograms or large computer screens to deliver vital information to consumers about more “mundane” things like opening hours, stock levels, product colours and sizes and returns. This way, AI frees up the time of human executives to focus on other, more important tasks that cannot be performed by AI. Equally, retailers can reduce the number of human executives, who would normally have taken responsibility for these tasks and save money.
 
Chatbots are now visible on most websites of major brands. A chatbot will normally appear and engage with an online customer, who may not be willing to take the time to figure out how to contact the retailer and ask a question – thus the chatbot reduces the risk that a customer will simply abandon their visit to the site, without making a purchase. Chatbots provide a useful first point of contact with customers and a means through which more basic enquiries can be handled quickly and efficiently. The chatbot functionality can be dually powered by humans and AI, allowing for a system that filters out more mundane queries, and simultaneously passing along more complex queries to be dealt with by humans, answering the instant messages delivered through the chatbot system. The chatbot function has allowed for more convenience for customers, who can now solve customer services queries quickly and efficiently without needing to have a conversation over the phone. The chatbot, too offers convenience and efficiency to the retailer whose human executives aren’t tied up answering the more mundane, repetitive queries that customers will invariably have. The change in the way that customers can interact with retailers that has been brought about by chatbots improves the customer’s experience of the brand and makes it more likely that they will be willing to make a purchase, or a repeat purchase.
 
For some retailers, AI is being used to deliver increasingly complex information to consumers. The makeup retailer, Charlotte Tilbury provides a good example. Using an AI powered system, this brand has devised what is referred to as the “Magic Mirror”. Customers sit in front of the mirror in-store and they see an image of their face projected onto the screen. The image can be manipulated to display how certain products, shades and “looks” would look on the customer’s face. It dispenses with the need for a customer to apply and remove makeup before trying on another product. When the whole process is finished the mirror makes suggestions about what products would suit the customer. As such, the mirror is performing a complex “sales” function that would normally be reserved for human assistants.
 
 
Fitting Rooms And Systems
 
AI is being applied within the traditional dynamics of customers trying on and trying out products to see if they fit, or what they are like to use. The traditional processes whereby customers enter a store, pick out a product and then enter a changing room to try it on are beset by inefficiencies that are being removed by implementing AI.
 
Specsavers, for example, has devised a system whereby a customer can “try” on glasses by uploading a recent photo of their face. The system analyses the dimensions of the face and the glasses that are selected and displays an accurate picture of what they glasses look like “on”. This way customers can select the glasses they wish to purchase without spending time visiting the store, where that customer could find that glasses in their preferred colour options are out of stock.
 
Similarly, Adidas has designed a system to make the fitting process easier and more convenient for customers, shopping in their stores. The system uses large screens displayed in fitting rooms, which interact with customers who can ask sales assistants to get them different sizes or colour options.
 
 
AI In Manufacturing
 
AI has resonated deeply within the sphere of manufacturing. Since 2017, 3D printers, powered by robotics have largely taken over the manufacture of sports shoes for sports shoe manufacturer Adidas. Prior to the introduction of AI-powered production lines in 2017, it took Adidas up to 90 days to create one shoe from scratch. Now, this task can be completed in one day, in the new so-called Adidas Speedfactory. Competitors like Nike are hot on their heels. In 2015, Nike began working with supply chain specialists Flex, with the aim of doubling the speed of their production. Analysts have described this as a “race” between the two sports shoe manufacturers to fully automate their production lines.
 
Other competitors understand the benefits that robotics and AI can bring to production, but have yet to fully invest in an AI-powered production line. For example, since 2016, Under Armour has moved towards transitioning their production lines to rely on 3D printing and robotics. A 3200 square feet test facility, replete with 3D printers, body scanners and robotic assembly operators has been testing new systems since 2016. The transition to AI powered systems has proven to be much slower than that of Adidas and Nike, however as of 2019 some Under Armour product lines are being created using robotics and 3D printing.
 
Not only has the use of robotics and 3D printing on the production lines of Nike and Adidas led to a surge in manufacturing speed, these brands are also able to met more specific order details from customers. Nike customers can now personalise the exact type of sports shoe they want, using simple product building tools on the Nike website. This ability to customise sports shoes and equipment gives Nike an edge over competitors who are confined to offering for sale what has already been created. This change in the dynamics of manufacturing for Nike add up to big benefits in terms of their overall logistical operation. Shoes can be created, on demand, dispensing with the need to pre-manufacture, store and then sell shoes.  
 
 
Best Examples Of AI Within The Retail Industry
 
This article has selected some of the best examples of AI and discussed these in terms of how they have been applied within the sphere of retail business.
 
AI has changed the way that retail businesses handle product inventory – a complex process that can involve thousands of products for sale in different regions. Although seemingly mundane on the surface, it is clear that how retail businesses handle product inventory can spell either success or failure for many retailers. Product inventory management was, indeed, identified as one of the key criteria in selected the Which? list of worst online retailers. AI has allowed retailers to more successfully record and manage data relating to product inventory management, minimising instances where products are “sold out” and ensuring that customers are not disappointed by an advertised product that later turns out to be unavailable.
 
AI has also been instrumental within the manufacturing industry. Many manufacturers are choosing to change their production line facilities and rely on robotics and AI powered systems like 3D printers. Adidas is a case in point, and as we have seen discussed this has allowed the brand to achieve more streamlined and personalised manufacturing processes, cutting waste and maximising efficiencies of scale and production.
 
AI-powered data analysis also allows retailers to continuously monitor their pricing strategy to ensure that pricing is set, taking account of real-time fluctuations in competitor pricing strategy. As such, retailers are able to prevent sudden revenue losses by ensuring an appropriate pricing strategy is in place.
 
AI has also revolutionised the way that customers can try on or try out some types of products like glasses and apparel. Specsavers has developed a unique system whereby customers can upload a picture of themselves into a system which then uses AI to display how different types of glasses will look on their face. Very precise measuring is used and this dispenses with the need for customers to go to the store to try on glasses. This revolution in how customer “try on” products has also been applied within the apparel industry with high profile clothing brands like Adidas using AI to interact with customers within the changing room to allow for more convenient fitting.
 
Using AI to facilitate data collection, storage and analysis of point of sale data has been a gamechanger within the retail industry, with savvy retailers now able to generate accurate forecasting models which help them manage their logistics and inventory management. Data collected and stored in this way can then be used to target advertising at people who have already expressed an interest in certain products or services.
 
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