Why site search is only as good as your product data
Written by: Jessica Farrelly
It’s tempting to get creative with the product data and names on your online store. We’re not saying a “soft-touch body-warmer in crimson” doesn’t sound intriguing, but are your customers searching for that product? Or are they just looking for a red coat?
You can tweak your rules and implement redirects, but the fact is, your website’s search solution is only ever going to be as good as the product data that it has to work with. The cleaner and more structured your product data, the more accurate and intuitive the shopping experience will be.
So, what can you do to optimize the product data on your ecommerce store?
Consistent product data naming conventions
The relevancy algorithm behind a search solution is based on consistent naming conventions. Product names that don’t adhere to a standard English format cause a number of problems, particularly when it comes to determining the product type.
When a customer performs a search, your search tool attempts to identify which keywords represent the product and which, if any, are modifiers. For example, if a shopper searches for a “dress shirt”, then they are likely looking for shirts that are dressy or formal. However, if they search for a “shirt dress”, they’re looking for a dress in a shirt style. They’re two entirely different types of product – the word order matters.
Searchspring’s search solution understands the contextual difference between those two search terms, but the accuracy of your results is based on the naming conventions in your product data. For a “shirt dress”, the product is a dress and the modifier is shirt, but the opposite is true for “dress shirt”. It’s an important distinction that relies on consistent product names.
Colors can also be included in product names, just make sure you’re using natural language rather than a string of keywords. A “long-sleeved blue shirt” should be named just that, not “shirt, long-sleeved, blue”.
If you include specific color shades within your product names, make sure that the generic color name exists somewhere else in the data where it can be searched. You might have cobalt, turquoise, and cyan shirts for sale, but is that what your shoppers search for? Site search isn’t the only tool that’s impacted by your product data. Think about your navigation and filters – your facets are going to be a colorful mess if each of these shade variations are listed.
Whatever you do, don’t just input your manufacturer’s color data or you could end up with hundreds of variations that make no sense to the shopper and don’t reflect how they shop. Create color families and assign your sage products to the green family to overcome this issue. Crimson becomes red, lemon becomes yellow, and so on.
The perfect fit
Sizing conventions are another common source of confusion when it comes to the front end of your store. Even if your range is as simple as XS – XL, make sure you stick to it. As far as your facets are concerned, XS, X-S, Xtra-Small, and Extra Small are all different sizes.
For most apparel retailers, sizing isn’t that straightforward. You could stock sweaters in standard U.S. sizes, but jeans that are measured by length and waist. Maybe you’ve also got a line of dresses that comes in European sizes. Accurate size selection is already a headache for many online shoppers, don’t make it harder than it has to be.
Review your internal size codes and assess whether they make sense on the front end of your store. For products like jeans, choose one approach and standardize it across your entire line. Keep it simple and keep it consistent.
Where should you start with a product data clean-up?
The best approach for cleaning product data is going to be different for every business, but as a general rule, it’s usually helpful to work backwards.
First, decide what attributes you want shoppers to be able to search, filter, and sort by. Your site reporting will give you helpful insights into how your customers actually interact with your store and locate your products.
Then, ensure that your chosen data exists on the product level. Product type, color, and size are some of the obvious places to start, but what if you also want to enable shoppers to sort by the latest arrivals? Do you track the data that indicates when you published a new product? If not, it’s time to start adding it.
Cleaning up your naming conventions and adding the required data fields might seem daunting, but once you’ve put the groundwork in, adding new products is easy. And the impact it will have on the quality of your site search and navigation is worth the effort. Visit our knowledge base if you’d like to learn more about preparing your store’s data for Searchspring integration.
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