Predictive analytics has been used in retail for decades, but it’s within the last few years that advances in technology — namely artificial intelligence — have supercharged the speed, scale and cost at which it is used. This AI-powered retail personalization revolution helps retailers transition into a world where consumers are always connected, more mobile, more social, and have more choices about where they shop.
Through AI innovations, retailers are starting to shift their businesses forward. While AI is continuously refining, expanding and improving its utility for retail, here are three more specific ways we are seeing it used in retail today.
Possibly the most readily used form of AI is automation. Machine learning has enabled computers to curate product recommendations without needing human intervention. Taking top players such as Amazon or Google, for instance, their automated systems can read digital user reviews, past searches or purchases, and present book or other product recommendations automatically. With little effort, retailers that utilize automated AI capabilities are likely to see an increase in customer engagement and sales simply by using the data already available to them. This can be done by implementing algorithms like collaborative filtering, which are relatively simple to deploy and efficient to run.
The “Segment Of One” Approach
Individual segmentation is the next step for retailers looking to create a super-personalized experience for their users. Like experts Pandora and Netflix (as well as the aforementioned Amazon and Google), hyper-granular behavioral profiles of individual customers’ shopping habits can be maintained at scale. For example, a retailer with millions of customers can make product recommendations based on each person’s unique shopping history from the last few years.
How is this different than automation? The segment of one uses micro-segments that target each customer individually, allowing retailers to convert visitors into long-term, high-value customers at high rates. Loyalty in tandem with sales will increase, improving brand engagement and growth.
Speed And Communication In Real-Time
Whereas before, data was analyzed and product offers were made days or weeks later, machine learning systems are streaming live data and curating products in real-time. For instance, using mobile geo-location capabilities allows retails to offer deals or promotions when customers walk into or near the store (not after they’ve paid at the checkout and departed). Mobile push notifications on company apps and other in-app or web-to-email technologies allow retailers to distribute messaging and track engagement the second it happens.
Given this rapid evolution, retailers have many choices on how to use AI in their marketing and sales strategies. As well, consumers are seeking a richer retail experience that both reduces friction and also captures the essence of who they are, what they like and how they prefer to consumer information. The sooner a retailer understands this and creates the best consumer experience they can, the sooner they will increase profitability and retention rates. I predict that this retail revolution will continue to unfold and expand over the next several years. But as the industry expands one thing is certain: in retail, personalization and the customer journey are key, regardless of how you get there.
Deep Learning in E-Commerce
E-commerce is a space with a lot of potential, in part because it’s such a data-rich industry, and, there’s some momentum around AI gathering already. What’s more, a lot of the AI techniques that are enjoying success in other applications are well-positioned to make serious impact on the space, streamlining retail processes and transforming the online experience into something more like talking to an experienced salesperson at a brick-and-mortar location.
Deep learning is a great example of this. It’s been the fuel for much of the recent success in applied AI, so it is not surprising that some of the first attempts at augmenting the shopping experience have been making use of the power of deep learning in classifying images. If you look at something like Pinterest’s visual search feature, you can see the beginnings of how deep learning fit snugly in a retail context.
Intelligent Product Searches
Another example is technologies that allow you to take pictures of things you see in stores, on your commute or even in an ad and make the items in those pictures shoppable. That can easily serve as the start of a shopping experience: You see something you like, but you don’t know the name or where to get it, or you just want something similar to, say, a pair of shoes you see in a shop window (e.g. CamFind).
But taking photos is not the only modality for shopping, and there are other areas in the shopping experience where AI can play a part. In fact, the e-commerce user experience has more or less stayed the same in the past 15 years. And that means that certain metrics, such as conversion rates, have stagnated.
An online shopper, who often knows what they are looking for, is faced with the task of coming up with the right search terms, or scrolling through many pages of inventory to find it. Attempts at augmenting the keyword search experience with natural language have not made a major difference yet, partly because of the fact that shopping, for most users, is a very visual experience.
Deep learning can be of help here, too! Auto-encoding features of images in an inventory based on similarities and differences brings about a rich model of what is available in the inventory, and the model is surprisingly close to how we as humans perceive shoppable items. The model alone, of course, is not enough: We need a way to understand a shopper’s preferences as they interact with the inventory.
Another AI technique, called online learning, can be of use here, where sites are able to analyze every click through an online inventory in real time to understand customer preferences and create a personalized shopping experience. Obviously, other non-visual aspects of shoppable content, such as price, size and match, must also be taken into account, helping to weight the visual models toward user preferences.
Already we’re seeing multiple, superior avenues for product discovery enabled by AI: You’ll be able to take pictures of items you like, search visually online and get personal recommendations based on an AI-generated model. But that’s just the start.
The Final “Word” – ChatBots
But again, there’s more. Chatbots, in the form of assistants and automated customer service reps, are becoming increasingly common across the industry. They have the potential to create a pleasant experience for the user, one that is directed at identifying exactly what best suits their needs, while promoting the brand identity through the chatbot persona itself. Many companies building conversational systems — such asViv — are banking on this brokering of intent to online services as their ultimate business model. It’s not hard to imagine extending this to shoppable content.