The key role of product taxonomy in AI-driven retail strategies

2024 has been the year of AI, and as we enter the second half of the year, brands are racing for any competitive edge they can gather from this exciting, emerging technology. 

Without the right foundation, however, any AI implementation can be a waste of time and resources. The reality is that for AI to deliver on its promise, it needs to be bolstered by a strong backbone of data. 

For retailers looking to leverage AI for better context, natural language search, predictive analytics, and hyper-personalization, these initiatives need to begin with a strong taxonomy structure for product catalogs.

Building a solid foundation of product data can seem like a daunting task, but it’s necessary to create AI-driven product experiences that fuel customer loyalty and retention efforts.

What is Product Taxonomy?

Much like the way we learn to classify the natural world in high school biology, product taxonomy looks to organize products in a hierarchy of attributes and relationships. This organizational structure is essential for managing product information in a logical, consistent, and easily navigable way. 

At its core, product taxonomy is about creating a common language between humans and machines, facilitating efficient search, discovery, and management of products.

A comprehensive product taxonomy includes multiple levels of classification, starting with the broadest category — furniture, for example — and then moving down to specific subcategories based on any number of criteria. In our furniture example, products could be classified by intended room, material, design style, color, type, and much more. 

The higher level of granularity ensures that products are accurately categorized correctly, which is vital for various retail processes, including inventory management, marketing, customer experience, and sales.

The Benefits of Robust Product Taxonomy

A well-structured product taxonomy offers a host of benefits that enhance customer experience and operational efficiency for brands and retailers; one of which is improved search accuracy. By ensuring that products are categorized and indexed correctly, customers can quickly find exactly what they need based on logical search queries rooted in natural language and being able to find the right products quickly is instrumental in higher conversion rates.

Enhanced navigation is another key benefit of a robust product taxonomy. Clear and intuitive categorization of products facilitates frictionless browsing and discoverability on eCommerce platforms. 

Customers can effortlessly explore different categories and subcategories to find items of interest, reducing frustration and enhancing the overall user experience. This streamlined navigation encourages customers to spend more time on the site, explore a broader range of products, and ultimately feel more confident in completing their purchases.

However, what really supercharges a solid base of product taxonomy is the integration of data with AI engines.

How Product Taxonomy and AI Work in Tandem

Having a foundation of robust product taxonomy can dramatically enhance customer experience when paired with AI. 

As mentioned, product taxonomy can lead to better site search and product discoverability; this process is bolstered by using AI to create more natural and intuitive connections between products and their attributes, as traditional keyword-based searches often fail to understand the context and intent behind a customer's query.

Using natural language processing (NLP), a customer can search more conversational inputs like "comfortable couches" and be shown items that fit that criteria, even if "comfortable" isn’t in the product name or meta description.

Discoverability is another area where the interplay between product taxonomy and AI shines. A well-structured taxonomy ensures that products are categorized in a way that aligns with how customers naturally think and shop. Making these intuitive connections among products can lead customers to products they may never have considered; it can also create more robust recommendation engines to cross-sell and add-on products, increasing sales.

Much like recommendation engines, the blend of AI and product taxonomy — particularly when coupled with a user’s purchase history or preferences — can create a hyper-personalized shopping experience. 

This includes not just product recommendations but also personalized content, promotions, and shopping interfaces. By analyzing a vast array of data points, AI can predict what products a customer is most likely to be interested in and present them in a manner that resonates personally.

Getting Started With Product Taxonomy

Building a system of product taxonomy means understanding your product mix as well as your customers. Going into the project with key stakeholders from multiple departments can ensure that every perspective is represented and there is consensus on product organization.

From there, it’s important to establish clear data governance policies to maintain consistency and accuracy, design the taxonomy with the customer experience in mind, and ensure scalability to accommodate growth. Knowing the role AI will play in product experience is also key in influencing how data is stored and organized.

It’s crucial to remember that a product taxonomy, and AI-based initiatives, are a process of refinement. Elicit feedback from customers and internal stakeholders to understand the system’s strengths and weaknesses and strive to continually improve. When done right, the foundation of a well-designed product taxonomy can harmonize flawlessly with AI to create the best product experience for your customers.