A major transition of retail to RetailTech and what it has led to
Technology has dramatically transformed the retail industry. Today retail stores are operating with a higher efficiency, while shoppers enjoy greater convenience and choice thanks to innovations enabled by technology. And this proliferation of technology is important to the industry since it can help retailers and merchandisers streamline their operations to provide a better shopping experience. By implementing and developing various solutions, business can improve its bottom line, stay ahead of the competition, and ultimately succeed in today’s fast-paced and rapidly developing retail environment.
It is the advances in technology that enabled retailers to collect and analyze vast amounts of data about their customers’ behaviors and preferences, opening up new opportunities for transformation. In this article, we will look at just a few examples of how technology is used in retail, how customer information is analyzed and used, and what this means for the retail industry.
The article is written by Dmitry Prusov, Head of data monetization at X5 Group
Retail Analytics
Example 1: Personalization and recommendations
The accumulated data on customers’ (users’) transactions and behavior can help determine their preferences — through the analysis of their past purchases, online behavior, and even their social media activity. This data can also be used to personalize the shopping experience for each customer, from personalized product recommendations to personalized promotions and discounts.
Amazon provides customers with personalized offerings based on their browsing and purchase history. Stitch Fix is an online personal styling service that uses data and machine learning to provide personalized recommendations to customers. Sephora Virtual Artist uses augmented reality (AR) to provide clients with personalized makeup recommendations.
Big retailers are known for personalizing their homepage in this way. Shoeline used this method to personalize their homepage, and earned click-through rates as high as 26% and a conversion rate of 18%.
These examples demonstrate how retailers can use personalization technologies and recommendation engines. Moreover, these recommendations are based not only on the collected transactional data, feedback from customers, but also on the input from professional consultants and experts in their fields, i.e., we are talking not only about data-based modeling, but also about expert-adjusted calibration.
Example 2. InStore experience
The use of various types of data and various processing technologies can provide valuable information about how customers behave in physical stores. This might include tools like heat maps and traffic analysis to understand how shoppers move through stores, what products they are most interested in, and where they spend the most of their time in the store. This information can be used to optimize store layouts and merchandise placement, resulting in increased sales and improved customer loyalty.
Nuremberg Institute for Market Decisions (NIMD) has developed a typology of shopping trips based on path-tracking data collected over one year in a German supermarket, using UWB antennas that monitor battery-operated tags implemented in shopping carts and baskets. Researchers identified eight different shopping trip types based on characteristics like distance covered on a shopping trip, speed, and the proportion of trips spent in specific areas of the store.
Smart vending machines. In summer 2020, fashion brand & Other Stories debuted a vending machine experience that allows customers to try out a range of products from its beauty and fragrance offering.
Walmart uses various sensors and cameras to track consumers traffic, control stock levels and analyze customer behavior. X5 Group has rolled out touch-free shopping via mobile app. Scan & GO is X5’s proprietary system, designed to increase the safety of store visitors, convenient and quick purchases without queues and with no need to use a regular checkout. Macy’s uses its modernized technology platform for analytics and to optimize its pricing strategies. The company relies on sensors and cameras to track how customers interact with various products and how prices affect their purchase decisions.
Big data and safe cloud computing along with the built-in support for artificial intelligence and machine learning allow retailers to use these data points to optimize production and operations, while providing customers with a first-class shopping experience.
Example 3. Omnichannel analytics
The number of touchpoints between a brand and its customers is increasing each year. Today the number of contact points can reach ten or even more: SMS, email newsletters, social networks, chat bots, messengers, reverse calls, companies’ web sites, mobile apps, marketplaces, and so on. Information at any “touch point” is important for manufacturers and retailers, striving to provide flawless shopping experience through multiple channels, including online, mobile devices and offline stores. These data can be used to create multi-channel customer experience with personalized recommendations and promo-activities that follow customers from one channel to another.
Starbucks: the company’s mobile application allows customers to order and pay for drinks in advance, skipping the queue upon arrival at the store. Best Buy, the leading retail seller of electronics, uses omnichannel concepts to improve the quality of customer service.
Target, a large retail chain, uses omnichannel analytics to optimize its inventory management, and relies on the data from its website, mobile application and offline purchases to track customer behavior and correctly adjust its stock levels. This helps Target guarantee the availability of the necessary goods in its warehouses at the right time, regardless of how customers prefer to make purchases.
However, the mere existence of multiple communications channels by itself doesn’t make such communications and analytics omnichannel. Here it is vital to understand the difference between the multichannel approach that just uses multiple channels, which do not constitute the unified ecosystem, and the omnichannel approach. The essence of omnichannel concept is not in the number of points of contact between the brand/retail chain and customers, but rather in the idea that consumers can seamlessly use all of these channels.
The development of technologies in retail allows retail chains to collect and analyze huge amounts of data on transactions, data from IoT devices (smart shelves that monitor the level of stocks in real time), the behavior and preferences of customers, opening up new opportunities for transformation.
Digital Transformation
It is also important to understand that there are always new emerging technologies that significantly transform all the aspects of retail process (the entire chain from producer to seller and to customer), and some of these technologies can become quite promising or even exciting and certainly affect the future of retail.
Example 1: AI-enhanced chat-bots
Chatbots have become increasingly popular in recent years, allowing retailers to provide personalized experience and 24/7 customer support. However, the next generation of chatbots will be powered by AI (GPT3/4, LaMDA, Macaw, etc.), which will allow them to understand customer requests and respond to them with greater accuracy and speed. For example, an AI-based chatbot can use natural language processing to understand a customer’s question, search the retailer’s database for relevant information, and provide the right response in seconds.
In February 2023, the French grocery chain Carrefour generated a video made with ChatGPT answering FAQs. The short video clip, a human avatar, with input from ChatGPT, answers a common question from customers about how to eat better and cheaper via its website.
H&M’s chatbot asks customers some questions about their style preferences and then recommends clothing and accessories based on their answers. Domino’s Pizza uses an AI-based chatbot that allows customers to place orders via Facebook Messenger. Chatbots can help customers set up their profile and orders, track delivery status and answer questions about prices and promotions.
Need more examples? X5 deployed Yandex SpeechKit telephony automation solution, which allowed retailer to offload its call center, with up to 50% of customer calls being resolved the robot, rather than human operator.
Example 2: Augmented reality (AR) and (VR) virtual reality
Augmented Reality (AR) and Virtual Reality (VR) technologies can change the very way people shop, as well as reduce retailers’ logistics costs (related to product returns). AR can be used to create immersive shopping experiences, allowing shoppers to “try on” clothes or see how furniture would look in their home. Virtual reality can be used to create virtual stores, allowing shoppers to view and purchase products from anywhere in the world. While these technologies were initially seen as a way to improve the shopping experience for consumers, it is becoming increasingly clear that companies are also finding them valuable. They start investing in technology to streamline operations, reduce costs and improve the overall shopping experience.
IKEA uses VR in its IKEA VR Experience app, giving customers an opportunity to place furniture in a virtual room. This allows customers to see what the furniture will look like in their own home before making a purchase. Home Depot uses AR, to help customers with their DIY projects. The company’s Project Color app allows customers to take a picture of a room and then see how different paint colors will look on the walls.
Lowe’s, another home improvement retailer, uses VR to train its employees. Lowe’s Holoroom app allows employees to practice their skills in home renovation through a virtual environment. Another example of using VR to improve employees’ proficiency is X5, which utilizes VR glasses (Oculus Go) to train its sales assistants, simulating interaction with customers at the counter.
AR Mirror to global Tommy Hilfiger retail stores. Customers will get an opportunity to experience high-quality virtual clothing try-on on the big screen and have fun while shopping.
Example 3: Robotics and process automation
Robotics and automation technology are already being used in warehouses and distribution centers, but they can also be deployed in physical stores. For example, robots can be used to replenish shelves or serve customers, freeing up human employees to focus on more complex or expert-related business development tasks.
Zara uses robots to automate its warehouse operations. Robotic manipulators can sort and pack garments for shipping, reducing the time and labor required to complete these tasks.
McDonald’s deploys robots to automate its kitchens. “Create Your Taste” kiosks allow customers to personalize their orders and have them cooked using a robotic kitchen system — this reduces the need for people to perform repetitive tasks (turning hamburgers, frying French fries). Autonomous inventory robots Tally can scan shelves and track inventory levels, allowing store employees quickly identify and replenish stocks of goods that are running low. These robots are already being used by the Carrefour network in the UAE.
These examples are just part of the trends in IT to be monitored by retailers in order to remain competitive in the coming years. If we are talking about technologies, it is impossible to imagine modern retail without AI and ML, which also massively transform the industry, allowing retailers to analyze large volumes of data, automate routine tasks and personalize the shopping experience for each customer.
AI&ML
Example 1: Detection and prevention of fraud
AI and ML technologies can be used to detect and prevent fraud in retail transactions by analyzing patterns and anomalies in data on payments and purchases. This can help reduce losses and protect the financial information of the customers.
Nordstrom uses AI-powered tools to detect and prevent online transaction fraud. Companies deploy machine learning to analyze customer behavior and transactions data, as well as to identify potential fraud in real time. Kroger (needs verification) uses technologies for detecting and preventing fraudulent activities to protect its customers from fraud with credit cards and theft of personal data.
Example 2: Inventory management and demand forecasting
AI and ML help retailers and manufacturers better manage their inventory levels and accurately forecast demand for their products. This means advanced technology helps predict how much a particular product will be needed and when it will be needed, to avoid excess or insufficient inventory and improve overall efficiency and profitability of the companies’ operations.
Moreover, this has already become a wide-spread technology, which is ubiquitously used by both food and non-food retailers (Kroger, Walmart, Target, H&M, Adidas, etc.) — when AI and ML are deployed to analyze sales data and customer preferences to predict which products will be in demand in the future. Retailers can then adjust their inventory levels and pricing strategy accordingly.
Example 3: Retail analytics for manufacturers
Most initiatives based on big data aim to optimize the company’s own business processes: improving promo-activities, optimizing assortment planning, and increasing the pricing efficiency. Algorithms become an element of the data-driven culture, when all decisions are made on the basis of data and advanced analytics.
Target’s Guest ID: the company uses a unique identifier, called a guest ID, to track purchases and analyze customer behavior. The company collects data on purchases, returns, and online browsing history, allowing it to create personalized marketing campaigns and tailor its in-store storefronts to match their customers’ shopping preferences.
Walmart’s Retail Link is a tool that allows suppliers to access sales and inventory level data in Walmart stores. Suppliers can use this data to optimize their production and distribution, as well as to identify opportunities to develop and market new products.
Dialog.X5 is a digital platform for suppliers. It is an ecosystem of services for automating business processes and retail data analytics. It provides the tools for performance analysis of various categories and evaluation of consumer behavior in order to create effective strategies for promoting products, increasing sales, optimizing the store assortment, and improving communications. It can be used to assess the completeness and time of delivery, evaluate inventory and availability of goods on the shelves, and to forecast procurement for comprehensive optimization of the supply chains. Besides, it allows to work with customers and analyze the promo campaigns to gain knowledge about their effectiveness and adjust the company’s media strategy.
In general, the data-driven collaboration between retailers and manufacturers is essential for them to remain competitive in today’s rapidly changing retail environment. By leveraging the power of data analytics, retailers and manufacturers can better understand their customers’ needs and preferences and develop more effective strategies to meet those needs. This can lead to increased customer loyalty, increased sales, and improved overall business performance.
Despite the wide adoption of AI and ML technologies, retail needs to create conditions for data confidentiality, developing ethical data consumption standards.
IT&Business
Retail today is primarily about technology, data and algorithms. RetailTech is a complex and constantly evolving area that requires collaboration between IT and business teams to succeed.
Example 1: Collaborative efforts to create IT solutions
Collaboration between IT and business teams can lead to innovative solutions that meet the needs of both parties. For example, IT teams can use their technical expertise to develop new tools and systems that improve business operations, while business units can provide expertise and information about the specific challenges and opportunities facing them.
Very often we see examples of external cooperation. For example, Walmart’s cooperation with Google. The two companies worked together to create voice-controlled shopping experience using Google Assistant. Customers can ask their Google Home devices to add various goods to their Walmart shopping carts simply by saying it out loud. This collaboration between Walmart’s business group and Google’s IT team led to a solution that made shopping more convenient for customers and helped Walmart increase its online sales.
Lego uses its platform solutions for collaborative creativity to interact with its customers and offer ideas for new products. “Lego Ideas” allows customers to suggest their own designs for new Lego sets and vote for projects submitted by others. If the design receives enough votes, it can be chosen by the company for production.
Example 2: Improving the quality of customer service
Such collaborations between IT and business units can help improve customer experience. By working together to analyze customer data and identify problem areas in the shopping process, companies can develop joint solutions that solve these cases, increasing customer satisfaction. For example, Nike improves the quality of its customer service with the Nike+ application, which allows its users to book goods online and try them on in the store, reducing waiting times and making the shopping process more convenient. In addition, Nike uses VR technology to create interactive displays in stores that allow customers to see products in different colors and styles.
CornerShop is a brand-new live store experience in London created by Capgemini, The Drum and SharpEnd to demonstrate the possibilities of advanced retail technology to consumers, brands and retailers alike.
Working together on the development and implementation of innovative solutions, retailer can be ahead of competitors and provide their customers with the best shopping experience, offering even wider and affordable choice at an attractive price.
Retail is undergoing a major transformation as technology integration becomes the driving force behind business processes. The transition to RetailTech has led to new shopping opportunities where customers can enjoy personalized services, efficient checkout systems, and seamless online and offline shopping. In fact, it is almost impossible to imagine retail without the participation of information technology and data specialists. And It’s pivotal for a new generation retail become digital, agile, analytical and innovative. As well, we believe so in X5 Group.
What do you think according to new technologies in retail? Your comments and thoughts are welcome!
The article is written by Dmitry Prusov, Head of data monetization at X5 Group
