Most have shaped their priorities for the year accordingly, looking to resolve supply-chain and delivery issues, for example, or improve their user-facing digital experience. Few fashion brands will have identified product attribution as an urgent priority, but ambitious projects can often fall flat due to the simple but pernicious problem of thin, inconsistent data.
Most of the major consulting firms and e-commerce industry heavyweights have weighed in on the lessons of 2020 and how they can be applied in 2021. The National Retail Federation called out automation and supply-chain transformation as two of its top trends for the year, Adobe’s director of strategy named fulfillment and personalization on his list, Shopify emphasized personalization and site performance and Deloitte’s Tech Trends 2021 focuses heavily on machine learning and the need to create individualized experiences.
These are all good and worthy things, and it’s hard to find fault with any of them. But above all, connecting with your customer effectively at the heart of all these e-commerce trends. And in order to do that, you may need to consider how you prioritize product attributes.
The Crucial Role of Product Attributes
Research from Salesforce shows that about 80% of buyers prioritize convenience over value or any other factor when choosing where to make their purchases. Another key driver of purchases — and an area where most companies fell short, according to respondents — was personalization. There’s a direct connection between the two, and it runs through your product catalog.
Convenience and personalization converge on your retail site. Your customers’ perception of convenience will be affected by things like fast page loads, multiple payment options and good search tools (and those things remain important), but their primary concern is easily, consistently finding a product that meets their needs in the moment.
Reaching that level of discoverability and personalization requires two things: rich data and the AI tools to make use of it. You can glean insights from a shopper’s actions on-site and how they interact with your products and their attached attributes. The deeper your product data of each item you sell (and not to mention the more accurate), the more you’ll be able to learn about their preferences and use that personalize their future experiences.
Detailed Product Attributes Build Rich Data Sets
How many attributes are currently assigned to each SKU? Four or five? It’s an important question, because each of those attributes (or “tags”) plays a role in discovery and personalization. They help shoppers find exactly what they’re searching for, and they help your recommendation algorithms make stronger “you might also like” suggestions across various touch points.
Spotting those trends in individual customer preferences requires lots of data points — the more, the better. So what happens if you increase the number of product attributes? The right algorithms can then also make sense of more data and do much better at analyzing what a given shopper really wants.
Even for something as simple as on-site search, instead of delivering a purchase-paralyzing grid with 4,000 red dresses, it might return just 10 or 12 that are consistent with a specific shopper’s preferences. Deeper data and product attributes makes it possible.
Using AI To Upgrade Your Product Attributes
Some level of basic information, such as category, color, size, and fabric, can usually be directly imported from your supplier or from the manufacturing department if you make your own product. Anything else you consider pertinent has to be entered manually. Instead, leave it AI.
Our machine-learning algorithms were honed on the world’s largest proprietary data training set. They’ll analyze your products in detail, creating a rich set of attributes for each individual item. Then our algorithm can draw correlations between garments that customers engage with, resulting in vastly more accurate search results, recommendations and personalization. With Lily AI you won’t just be able to see your customers’ choices and decisions; you’ll be able to zero in on the emotional and psychological underpinnings of those choices.
Objective and Subjective Attributes
The benefit of Lily AI’s deep tagging is that it takes you beyond objective attributes like size, color and fabric. Our algorithms can also identify subjective attributes, which are otherwise harder to quantify and apply, that reveal more about the emotional context behind their purchases.
Lily AI’s deep tagging lets you identify shoppers’ underlying preferences and motivations, even when they’re unconscious. The end result is that your shoppers will see products they want, each and every time, whether from their searches or your suggestions. That is the kind of meaningful personalization that creates an emotional connection with your brand, and results in tangible metrics like improved conversions and a higher Net Promoter Score.
Time to Up Your Game
A landmark 2010 study in the Harvard Business Review found that some companies flounder after recessions while others thrive. The difference? The companies that flourish after a crisis invest in upgrading their core functionality.
For online and omnichannel fashion retailers, it’s difficult to argue that personalization and discoverability are anything but core functions in 2021 and beyond. Upgrading your site with enriched product attributes will place your brand squarely among the leading-edge performers on those all-important competencies in a matter of just weeks.
Contact us today to learn how Lily AI can help your brand connect with customers at a deeper level, to your mutual benefit.
National Retail Federation: Retail in 2021: What Will Endure and What’s Going to Change?
Digital Commerce 360: 5 Predictions: How Things Will Change for Retailers in 2021
Shopify: 11 E-Commerce Trends You Need to Turn Your Attention To in 2021
Deloitte: Tech Trends 2021
ZDNet: Most Customers Expect Companies to Accelerate Digital Initiatives Due to COVID-19
Harvard Business Review: Roaring Out of Recession