The ML market value is forecasted to reach $209.91 billion by 2029, growing at a compound annual growth rate (CAGR) of 38.8%. ML is a technology that’s influencing numerous industries around the world. Some include healthcare, IT, automotive and transportation, media, and energy and utilities.
ML is changing retail, too. The benefits of employing ML technology in retail are multifold. Let’s review some of them in this article.
One of the secrets to why ML has such a profound effect on the retail industry is that companies of all sizes can adopt it. A small or medium-sized business can find as much value in ML as a giant like Amazon, Costco, or Walmart. Digitization within the industry and the rise of e-commerce are all contributing factors to why ML is and will continue to change retail.
ML involves leveraging data. The retail analytics market is set to hit $90 billion by 2032. Put those two together, and it’s evident that there will be a cosmic change at this intersection. The growth in retail analytics is driven by continued domination by the North American market and rapidly growing markets in India, China, Latin America, and the Middle East.
The COVID-19 pandemic catalyzed the adoption of e-commerce in emerging markets around the world. The spike in online retail during the pandemic was an irreversible shift in momentum. Only technologies like ML can keep up with the speeds required to remain competitive in an industry like retail. This is doubly true in a post-pandemic world.
The supply chain is the backbone of the retail industry. Any fractures in the supply chain will result in complications that will affect everything around it. Naturally, numerous retail companies have employed ML to optimize their supply chain management. A superior supply chain management system can help a company widen its competitive advantage gap.
ML can offer companies optimal routes for the delivery of goods, which helps save the two most important resources, time and money. Route optimization also directly contributes to a company’s sustainability initiatives by ensuring that its fleets expend only the actual number of resources required to complete essential tasks.
The capabilities of ML extend to predicting demand, meaning that it can let companies know how much of a particular product needs to be stocked at what time and where. ML will very soon help make “out of stock” a phrase of the past.
Due to the intricate details in the vast amount of data it consumes, ML systems can notice patterns and commonalities and clearly define customer segments. This would go beyond just demographic information like age, sex, and location. Instead, ML will excavate information from specific buying behaviors and online shopping habits.
This quality of AI-based customer segmentation will greatly help companies design and deploy customized marketing and advertising campaigns to target specific segments. ML will help companies build more meaningful relationships with their customers because those connections will be founded on actual data rather than guesswork and intuition.
Pricing can be one of the biggest headaches for companies to deal with. This is because the success of a product or service is entirely dependent on putting the right value on it. The second challenge is that, in the past, human intervention was needed to put a price tag on goods and services in digital environments.
ML helps retailers mitigate this challenge by studying data from the entire value chain architecture. By doing so, ML can track the whole journey of a particular product and compare it with similar offerings by other resellers to assess whether product prices make sense. ML can price products accurately, with business logic, and without human invention.
Reviews, feedback forms, and surveys are some of the most common ways to evaluate a customer’s satisfaction. However, unless that data is compiled and studied deeply, a lot of key information could go wasted. Leveraging customer feedback is the perfect activity for ML because the inputs are vast, diverse, and valuable. And the output could be transformative.
ML can help e-commerce organizations classify and categorize their customer reviews. This enables new customers by showing them relevant testimonials and guiding them toward products that align with their needs. It allows companies to constantly measure the quality and success of their products and service and assess what aspects need improvement.
The great advantage of a technology that can learn from common patterns is that it can also notice abnormalities. Abnormalities in data from the retail industry can often point toward fraudulent payments and transactions. This is an unfortunate and widespread scourge in the retail space.
Fraud can be on both the customer’s and the business’s end. It can be malicious or accidental. ML can help identify both. There are many kinds of ML, but they can be broadly categorized into unsupervised and supervised models. Both models can be used to detect Fraud.
There isn’t one single solution that will suit every company. Depending on its needs, a retail company can utilize ML uniquely to detect and prevent Fraud.
The last benefit in this list is the most important one, addressing the most significant challenge that modern enterprises face. Imperva’s The State of Security within eCommerce 2022 report reveals that distributed denial of service, credit card fraud, account hijacking, and API attacks account for 62% of security breaches for retail e-commerce companies.
These security incidents can be catastrophic for retail companies. ML is amongst the leading technologies being utilized to counter these targeted attacks. The watchdog eyes and memory of advanced AI and ML technology allow companies to constantly and effectively surveil their infrastructure to identify atypical activity, vulnerabilities, and potential security breaches.
ML and other AI-based technologies require significant investment. Therefore, affordability becomes the first challenge for many companies. ML requires robust infrastructure to function at its highest capacity. This includes advanced analytics tools, immense computing power, and a team of highly skilled professionals.
Then comes the challenge of data. Data is the raw material for ML, which makes its storage, security, privacy, or scarcity incredibly significant. Everything to do with high-volume data management is a major challenge. Not all data is equal, so retail companies must have the right amount and quality of data and a strategy to distill this data into actionable insights.
Companies need to be aware of these challenges before leveraging the powers of ML. The companies that utilize ML with care, consideration, and a keen eye on the future will undoubtedly be contenders to dominate the retail industry.
Amazon utilizes machine learning in multiple ways to upgrade its business operations and enrich the customer experience. For example, Amazon utilizes ML to anticipate product demand and adjust inventory levels, detect and eliminate fraudulent activity, optimize its supply chain processes, and even automate certain customer service functions.
However, one of the most profitable applications of ML is providing an optimized shopping experience. With the help of ML algorithms, Amazon personalizes each customer’s shopping journey, leading to an increased average order value. Its system employs algorithms that suggest products based on a shopper’s past searches, which helped to contribute to Amazon’s net sales revenue exceeding $500 billion in 2022 and securing its position as the world’s top marketplace.
Walmart and other retail giants like Costco and Target also use ML to their advantage. Walmart has an entire AI-powered store called the Intelligent Retail Lab, where a selection of cameras and sensors are set up to evaluate the quality and availability of products and buying behaviors of customers and support Walmart staff and associates who work there.
rinf.tech worked with a Global Online Survey and Insights Pure Play Company, which required a custom solution that would allow in-store activity supervision without breaching confidentiality. Our team innovated a solution called Vid.Supervisor, an ML model that scanned videos to identify and tag behaviors.
rinf.tech’s Vid.Supervisor ensured that their client didn’t have to spend human resources on codifying videos. The time required by the client to codify videos was reduced by 90%. The key features were people detection, facial recognition, and automatic video cutting and creation. This helped reduce thousands of hours of irrelevant video into two hours of relevant footage.
There are unlimited benefits of ML in the retail industry. But it isn’t going to be smooth sailing for all. Like any other technology, the success of ML integration is wholly dependent on the quality of its application. Only if ML is applied with strategy can it succeed at providing meaningful and actionable insights.
Tech partnership with leading AI solutions providers is highly recommended. The involvement of experts can never be understated. Having a team of specialists dedicated to crafting a custom solution to a company’s challenge can never be replaced by an off-the-shelf solution.
The advantage of experts goes beyond technical skill sets. The crème de la crème in AI and ML solutions will ensure that technological innovations align with a company’s overall business strategy and logic. The top ML innovations shouldn’t just be flashy additions to a portfolio. They should transform an entire business and elevate them to the next level.
Consider our Vid.Supervisor case study again. By utilizing the expertise of rinf.tech, the client received a solution that fits like a glove. Most importantly, that entire innovation was completed in 300 hours of work, 7000 lines of code, and a team of 3. The value of AI experts helps companies make rapid strides into the future and stay ahead of the pack.
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