Invisible AI: A Behind-the-Scenes Look at How Tech is Driving Foundational Commerce Experiences
The retail industry is no stranger to artificial intelligence (AI). According to Deloitte, over a third of major brand leaders use AI to improve their business. And while flashy applications like virtual reality often get all the hype, companies can actually get better return on investment (ROI) from the AI applications working behind the scenes. What's more, the less sexy AI use cases can have the biggest impact when it comes to delivering delightful customer experiences. With retail spend on AI expected to reach $7.3 billion by 2022, it's time to stop focusing on the hype and explore where the real value for AI in B-to-C retail really is.
Leverage Analytics to Stop Guesswork
Managing and tracking expansive product catalogs, customer habits and demands is cumbersome. More and more brands are beginning to embrace AI and machine learning for optimization and efficiency. Not only can brands use AI algorithms to learn from huge data sets, but they can also be used to predict and recommend where company resources need to go.
For example, trend forecasting algorithms, which comb data from social media posts and web browsing habits, allow brands to identify what’s getting the most engagement. Similar algorithms can also be used for sentiment analysis, uncovering the context in which a product is discussed online and if people feel positively or negatively towards a product. In many instances, AI can actually help companies accurately predict top-selling products across different categories. rue21 is one company that has successfully taken this approach by using advanced analytics to take the guesswork out of customers’ preferences for specific apparel, personalizing communications, and driving engagement across its sales and marketing channels.
Advanced analytics are also enabling companies to optimize customer service by having a big impact on reaching those who shop online as well as finding ways to encourage them to come back. By compiling data from previous online and offline interactions as well as social media and purchase history, businesses can create a 360-degree view of the customer. They can then use those insights to help customer service teams enrich the overall experience, which probably leads to better retention rates and average order values.
Decipher Supply Chain Management
Often overlooked, the application of AI in supply chain management can impact brand and consumer experiences in a very direct way. Brands that can improve efficiencies around producing and distributing goods reduce their own costs, but also reduce costs to shoppers.
Beyond cost efficiency solutions, companies are taking AI in supply chain management to the next level. Brands, like data-driven shopping platform Choosy, are using natural language processing (NLP) to determine product demand by interpreting comments left on Instagram photos, and then ranking specific apparel choices based on popularity and sentiment. Behind the scenes, AI enables the fashion company to figure out which styles, colors, sizes, etc., they need to speed up in their supply chain, as well as which products to cut. As more and more brands find ways to create sophisticated supply chains, retailers will be able to scale quickly based on real-time demand.
NLP is also used to uncover new insights about supply chain governance and can help with policy enforcement. Take for example multinational companies, like Apple and Amazon.com, which have hundreds of suppliers around the globe. In order to proactively monitor their network of vendors, brands are using NLP to translate news and commentary on social media about their suppliers into English to get a better sense of whether they're abiding by the terms of service. From there, the company can act to either make a policy change or drop a vendor altogether.
Discoverability is Fundamental
Search is seen as one of the areas ripe for integration with AI. It’s a powerful tool that we rely on for seamless commerce experiences, from tracking news and events to finding our favorite influencer on social media. When it comes to retail, search is incredibly important. If a shopper is on your site directly searching for what they need, one could assume they're close to a purchase decision. However, if they can’t find what they’re looking for, they won’t be able to buy it.
Advanced search engines, like Google, can, for the most part, point us in the direction that we need, but a lot of retailers’ online search mechanisms fall short. One way that brands are ensuring they keep conversions high and customers get what they need is by using search ranking algorithms. Taking into consideration factors like content, preferences and similar items can all play into providing the optimal search results. Using machine learning to pull information from customer purchase patterns to determine where products should rank have a direct impact on a brand’s success.
Whether you want to optimize your supply chain, use data to predict demand and drive conversions, or ensure that customers get exactly what they need, AI and machine learning represent huge opportunities to improve core business functionalities and make them more intelligent, but to also create the most enjoyable shopping experience for customers.
Anita Andrews is director of analytics practices at Magento, an open-source e-commerce platform.
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