There are numerous ways that retailers have defined “customer intent” over the years, with the most simple and understandable being: customer intent is the true intention of a customer that underlies their reasons for interacting with your brand. The challenge for so many has been understanding just what that is, and how it can be successfully parsed, determined and made actionable in an ever-expanding world of retail choices, product SKUs and streams of digital data.

What is clear, and which is borne out in online shoppers’ tacit and stated preferences, is that we’re now living in a hyper-personalized world in which customers expect to be shown what they want online before even they know they want it. With ecommerce digital transformation having been rapidly accelerated by Covid, it’s nonetheless clear than many retailers are still failing to digitally transform in this regard and keep their customers – and their rapidly-changing expectations – at the center of the shopping experience. It’s as easy as counting the number of inaccurate and irrelevant results on most retailers’ site search pages to understand what’s missing.

Enter the first half of the customer intent equation: product attribution data. A single item in an online retail store can obviously generate multiple data points, all of which need to be managed consistently and efficiently. Without an ability to provide shoppers with exactly what they’re looking for in real time, product attribution taxonomies become muddled, and result in shoppers receiving, say, 75 wholly irrelevant results for a search that should have only provided 2 or 3 pinpointed, spot-on results.

Data points for a single dress, for example, could include:

• Product name and description

• Measurements, sortable by filters and facets

• Categories or occasions; for example, summer, formal, luxury

• Key features such as style and color (again sortable)

• Price

• Embellishments

• Fabric weight

• Unique catalog ID for inventory management

When such data is visible to the customer, as in product descriptions, it needs to be user-friendly and consistent. Where it is back-end data for inventory management or order processing, it needs to be clean and structured.

Done correctly, this data even has the ability to supercharge a retailer’s SEO and SEM results, providing search engines with relevant data and consumers with excellent, instantly-clickable results from their long-tail searches. The alternative is on-site and SEM searches that don’t convert and that frustrate shoppers, as well unsold inventory that needs to be dramatically marked down or that piles up in warehouses. Some might ungenerously call this the retail status quo.

Yet that’s only half of the reason that retailers are prioritizing customer intent for the 2022 retail year. See, this taxonomy of product attributes also produces affinities between them that can then be tied to an individual customer’s preferences and their psychographic profile. This is consumer intelligence, and it plays beautifully with product attribution data, or product intelligence. Understanding unique consumers in this same language of attributes is the key to understanding the why behind what they like and dislike.

For instance, the shopper who searches on a furniture website for “solid beige modular sofa and chaise” almost certainly has her own unique take on just what that means. The forward-looking, customer intent-obsessed retailer, by virtue of having an anonymized sense of her buying profile and emotional context, might understand that she prefers a minimalist look, and will present her with results and recommendations that give her a minimalist, solid, non-patterned modular sofa and chaise lounge set. Moreover, the retailer might also understand that she’s browsed and purchased similar home furnishings in clean, simple colors besides beige, and presents color alternatives to her in ways that may delight and surprise her, and result in a sale or upsell that otherwise might not have taken place.

This marrying of product and consumer intelligence is what retailers are striving for, and is something that allows them to take the ecommerce experience to a new level. Customer intent, once defined in this way, turns into a universal mathematical language that can be used to power all of the destination systems within the ecommerce stack. Sending this robust product and consumer intelligence to key destinations systems allows retailers to boost the effectiveness of their search engines, recommender systems, demand prediction models and item set-up processes.

It’s a level of long-desired granularity that retailers are counting on to boost conversion rates, reduce returns, and even to help to combat the supply chain crunch. Having a strong sense of what inventory will actually move within high-margin windows. or which proxy products to order based on product and consumer intelligence, is something that goes a long way toward ensuring that the products that are ordered are the products that will sell. This is what it means to truly uncover the customer intentions that underlie their reasons for interacting with your brand, and why homing in on this customer intent has become a front-and-center priority for retailers in 2022.

The author, Jay Hinman, is vice president of marketing at Lily AI.