If you’ve been paying attention to retail spending over the past few years, it won’t surprise you to learn that e-commerce in United States continues to gain traction at record speed. In fact, e-commerce is expected to “surpass 10% of total US retail sales for the first time in history.” By 2023, online spending by U.S. consumers is expected to grow to $970 billion (a 65% increase to this year’s volume) and global retail e-commerce sales are expected to balloon to $6.5 trillion in that same year. There are several key factors that contribute to this growth and consumers’ attraction to e-commerce. These factors include convenience of 24/7/365 accessibility, nearly limitless product selection, quick price comparisons, fast checkouts using one-touch purchase options, real-time updates of new product launches, exclusive promotions, same-day delivery, as well as enhanced personalization and customer service using artificial intelligence.
These days, retailers and e-tailers have access to tremendous amounts of data about their customers, competition, and the market at large. But, this collection of data isn’t easy to manage, growing in volume and complexity daily. Therefore, for online and brick-and-mortar merchants the challenge remains connecting and making sense of all of that data, making it actionable for either revenue growth or cost reduction. And that’s where machine learning steps in to power the future.
With AI and machine learning, companies can turn idle data into valuable insights. For example, using AI can automatically categorize products, make better recommendations, dynamically adjust pricing based on customer behavior and inventory levels, provide virtual assistance to support customer queries or concerns, and optimize supply chains like never before. Given the broad applicability of AI and its promise to level the playing field in an increasingly competitive industry, retailers worldwide are predicted to spend $7.3 billion on AI by 2022, up from $2 billion in 2018.
Depending on the data type, specific machine learning methods and models are used to get to the intended outcome. For instance, computer vision, a subfield of AI, is used to classify and contextualize the content of digital images and videos. Computer vision gives companies the ability to use machines to detect and label objects in images without having their personnel do the same. Companies can also unclutter their listings or filter out offensive images in this way. This is the same technology that gives consumers the ability to find their favorite products (or something similar) by simply using a picture of the item.
Other uses for computer vision include facial recognition, sentiment analysis, and logo detection. Merchants can use facial recognition and sentiment analysis to recognize repeat customers, personalize the customer experience, and in some cases, provide better security by identifying and monitoring high-risk individuals. Logo detection by machines are used to support marketing, identify counterfeits, and protect brands against pervasive infringement. Take things to the next level and you’ll get to fully automated, cashless stores where consumers can simply walk into a location, grab their favorite merchandise off the shelf, and walk out with those items without ever having to stop at the cashier to scan any item or pull out a credit card.
Natural language processing (NLP) is a subfield of AI focused on processing and analyzing natural human language or text data. Here, finding the right product becomes much easier because NLP can interpret the customer’s intent and the shopping context much better than traditional search systems that rely solely on exact “keyword” matching. This includes better performance even when the user requests involve typos or poor grammar. NLP can also help drastically improve customer service, both in terms of leveraging sentiment analysis capabilities to better support customer needs and using conversational chatbots to streamline the call center experience. Imagine, smarter systems where clunky “press 1,” “press 2” prompts are a way of the past, replaced by NLP-powered machines that can seamlessly answer questions and carry on natural conversations.
While AI is improving your shopping experience, it is also being employed to simplify the less visible aspects of the supply chain which are responsible for production and distribution of the products you can buy. Ultimately, better supply chain management means less waste, faster production cycles, and lower costs. Historically, data analysis in these areas has been performed using traditional data analytics but this is fast changing due the explosion of data volume and complexity. Companies are turning to the “predictive” power of advanced machine learning to optimize everything from manufacturing to warehousing to transportation and logistics. For example, studies indicate “that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime.” Predictive maintenance powered by AI is now delivering the required ROI by reducing unplanned downtime and improving asset efficiency.
As U.S. and global consumer demand for retail products continues to rise, it’s clear that the world’s reliance on advance AI and machine learning capabilities will continue to rise in lockstep. These new capabilities present new opportunities for retailers and e-tailers to deliver more personalized customer experience at greater scale than ever before.
For a quick refresher on key AI terminology, refer to our previous article defining 53 useful terms in the world of artificial intelligence.