When a human shopper browses your online store, they’ll often look at things like the layout and aesthetics of the page and respond to emotional triggers like storytelling or imagery. However, when an AI agent visits your store, it bypasses all these visuals and evaluates the data behind the scenes, such as inventory levels, product schema, pricing, trust signals, and more.
With more and more shoppers using AI agents to find products rather than searching themselves, ecommerce brands need to ensure this background data is high-quality, or they risk never appearing in AI search results and losing out on potential sales.
What is High-Quality Data?
Data is generally high-quality when it’s a good fit for its intended purpose, which means it’s trustworthy and able to be used and relied on to make decisions, run operations, or do anything else you need it to without causing problems.
For data to be considered high-quality, it should be:
- Accurate and error-free, with no typos or wrong digits
- Complete, with no missing values or blank fields
- Consistent, with data staying uniform across different sources and databases
- Timely and up-to-date
- Valid, and follows the established rules, formats, and value ranges of your business
- Unique, with no unwanted duplicates, irrelevant entries, or overlap
Why is Data Quality So Important for Ecommerce Brands Today?
High-quality product data is quickly becoming a requirement for ecommerce brands due to the rise of agentic commerce. This is when customers enlist the help of AI agents to research products, compare alternatives, and potentially even complete purchases, all without any human intervention.
These AI agents rely on your store’s data to learn about what you have to offer. They never open your product pages, look at photos or videos, or read about the emotional journey your brand has been on. It simply reads your product data and sees if it matches up with what the shopper is looking for.
If your data is high-quality and accurate, and matches the search intent of the shopper, you may appear in the results the agent sends back to them. However, if they don’t match, or the AI agent skips your product entirely due to inaccurate or incomplete data, you’ll be left off the list and miss out on a potential sale.
While plenty of people still shop manually, using AI is no longer uncommon, and more than 75% of consumers are open to agentic commerce. As a result, if your product data is lacking, you risk never showing up in these increasingly important AI search results.
This is especially bad for brands, as it’s not something you’ll instantly notice, like you would a sinking conversion rate. Instead, low-quality data just makes you invisible to AI and prevents you from even being considered among shoppers using these agents.
Increasing Your Data Quality
But despite the major importance of high-quality data as agentic commerce continues to grow, many brands don’t have confidence in their product data being ready for AI. With AI set to reshape product discovery, companies need to improve their data quality in order to succeed. Thankfully, there are plenty of ways they can do this.
Centralize Scattered Information
Instead of having all of your product data scattered across different folders, spreadsheets, and files, consider centralizing it into a dedicated Product Information Management (PIM) tool to keep titles, pricing, descriptions, and other data uniform. This gets rid of any data silos, eliminates confusion around duplicate files, and saves a ton of time and effort for brands.
Ensure Product Attributes and Details Are Machine-Readable
Next, brands need to ensure that their product details are machine-readable. For example, instead of using vague or subjective terms like “a compact design that fits anywhere”, consider using concrete and measurable figures and terms, such as “2-inch size that easily fits under most standard-sized desk drawers”.
AI agents struggle to understand many of the creative and emotional terms that get used when marketing to humans, so consider sticking with facts and terms that are easier to measure or verify.
Perform Routine Audits
To ensure your data continues to be high-quality as you add products and change details, you should perform regular checks and audits. This helps you catch mistakes, update stale data, fix formatting errors, and generally ensure your data remains accurate and complete.
Without performing these checks, you may never know anything is wrong with your product data and may be completely unaware that AI agents have stopped trusting and/or reading it.
Our Take
Data Quality Will Create the Early Winners of Agentic Commerce
As AI starts making more decisions in the shopping process, it’s not always going to be the brand with the best marketing, best story, or best design that walks away with the customer.
Because AI agents pass through all of that and get right into the data, the companies with the cleanest and most organized product data are likely to be the early winners of agentic commerce. As a result, all brands should be doing everything they can to ensure their data is accurate, complete, consistent, and ready for AI agents to analyze.
Of course, this doesn’t mean that your branding, marketing, and design don’t matter, but no matter how good they are, if your data isn’t also high-quality, you may never show up for sellers using AI to research and compare products.
