The Architectural Imperative for AI-Powered eCommerce – RTInsights
In the eCommerce landscape, Artificial Intelligence (AI) is reshaping the game. This blog series explores how AI’s intelligent algorithms are revolutionizing online businesses, from personalized product recommendations to efficient inventory management. Join us for insights on leveraging AI to enhance the digital shopping experience and overall success in eCommerce.
Remember, there is no AI without IA. Make sure that information architecture works and your AI will function appropriately.
At one time, eCommerce was simply about customers finding what they wanted to buy. Today, there are so many items available – and so much information available to customers – that eCommerce must be smarter than ever before. How is your eCommerce platform working for you? If it’s not living up to expectations, chances are the problem is not in the technology you’ve deployed on your site but in the architecture and data that supports it. Only when you get that right can you truly make your eCommerce smarter. A key to success is AI-powered eCommerce.
See also: Emotion AI Dives Deeper into Customer Interactions
Consider a few typical eCommerce scenarios that tap into artificial intelligence.
AI powers all of these scenarios: search, navigation, predictive offers, and shopping basket analysis. But they have more in common than that.
My analysis of numerous technology projects, both successes, and failures, has shown that the effectiveness of AI-powered features like these ultimately depends on a high level of discipline about data and architecture – and to a degree that some leaders may not realize.
The eCommerce customer experience is made up entirely of data. The quality of the underlying data determines the quality of the experience. While this sounds obvious, in practice, I’ve observed that many organizations have immature product information processes. When they onboard new products, they don’t manage product information in an adaptable, sustainable way. The result is dirty, incomplete, and inconsistent data that undermines the ability of AI to deliver the optimal experience.
The fuel for an intelligent eCommerce experience comes from two kinds of data: information related to products and customer data.
Start with products. Managing a selection of thousands or millions of products begins with the product hierarchy called a display taxonomy. Just as products in a physical store are arranged according to a logical set of aisles and shelves featuring similar products, the products in a virtual store need to be organized according to a logical set of categories and qualities suited to the unique needs of the business’s customers This is the product display taxonomy, and its design is just as important to an eCommerce site as the planogram of a physical store is to the shopping experience. Differentiation of that display taxonomy is one source of competitive advantage. If you know how your customer solves their problems and can arrange products in a more effective way than competitors, you will retain their business. If they can’t find what they need quickly and easily, they move on.
Product Information Management (PIM) systems hold the information about products, including their relationships. They know which products are accessories to other products and which ones are usually used together. But this data is effective only if the onboarding process for new products is sufficiently rigorous to always include such relationships.
In my experience, the design of the taxonomy of data and categories in the PIM is a subtle and challenging problem that many technology managers overlook. The more fine-tuned the taxonomy to unique customer needs, the more the site can offer suggestions that lift yields. But customized taxonomies do not always align with data interchange standards. That’s OK. Standardization is for efficiency, differentiation for competitive advantage. The design of the taxonomy and the product onboarding process is, therefore, a delicate balance between standardized and differentiated elements.
The other side of the challenge is customer data. Personas (like “first-time visitors” or “price-sensitive buyers”) allow sites to make sense of the diversity of users they encounter. Designers then use these personas to make taxonomy and customer experience decisions. They reflect audience attributes such as customer loyalty, impatience, or consciousness of value. Testing based on those attributes then allows the site design to refine its approach to particular types of customers with different needs.
There’s another unsuspected source of challenges in the data that supports AI in eCommerce: terminology. When serving multiple audiences, the same terminology can have multiple meanings and contexts (remember “mold stripping?”). Standardizing terminology is an essential element to making the product taxonomy and audience data usable and effective.
Despite the automation that seems inherent in customizing a site, in my experience, the design always begins with a very human, almost artisanal set of decisions. A marketing specialist who knows their customer starts by deciding what message or part of a message they think will resonate—and then tests it by iterating on a set of handcrafted variations. They then handcraft the message and try a variation, just as an artisan uses knowledge of their craft to create something that will engage with another human. The marketer will then try other variations and learn what other items might work and which ones do not.
Eventually, machine learning comes in, as AI-powered algorithms try the likely variations and optimize their combinations based on an ongoing process of testing and continual improvement. However, the structure of those messaging elements and combinations are defined by human experts.
How can you make sure your AI tools actually deliver the experience they promise? By beginning with the information architecture (IA). Reviewing commonalities from dozens of projects, I’ve observed that these are the key areas to concentrate on to ensure that the data on which AI-powered algorithms operate can actually enable a better, higher-yield experience:
None of these tasks are easy. In fact, your progress on foundational elements like these – especially the information architecture – will determine your site’s level of maturity for AI readiness. Auditing your progress on these challenges – and putting plans in place to improve them – will go a long way to making sure that your future site improvements can effectively use the artificial intelligence advances coming down the pike.
This is where you should be concentrating many of your efforts. Adding more AI-powered modules on top of a weak and inconsistent content and data architecture will end up costing you in the long run. Think more about the data and less about the bells and whistles, and you’ll be on a path to prepare your site for the technologies of the future. Remember, there is no AI-powered anything without IA. Make sure that information architecture works and your AI will function appropriately.
Seth Earley is the CEO of Earley Information Science and the author of the new book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable.
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