Product Matching in eCommerce—What Is It and How Can It Help You?
Updated · May 04, 2022
eCommerce is a fast-growing area, and one of the biggest challenges facing online retailers is the increasing complexity of managing the multitude of new products that come with this growth.
In this article, we will explore product matching in ecommerce, and how the new way of doing it, through AI, is helping businesses keep pace with an ever-evolving sphere.
What Is Product Matching?
Product matching is matching the same products within a sales platform or across different platforms.
Matching the same product within a platform is done for the purpose of inventory management and keeping information accurate and tidy.
When done across platforms the purposes is to set competitive pricing, copyright infringement protection, and optimization.
Using a product matching algorithm, therefore, has benefits for both ecommerce stores and their customers.
Let’s take a closer look.
Benefits of Product Matching for Stores
Product matching can be thought of as working for internal and external purposes.
Below we’ll illustrate the difference by discussing some benefits.
Product matching for inventory management is working for an internal purpose, namely to keep your database tidy.
With a big inventory sprawling hundreds of products, it’s easy to have duplicate listings that differ only in how they’re worded.
Without product matching software, these unchecked duplicates can lead to poor quality of data.
One listing may indicate that a product is out of stock when in fact, that product is in stock but linked to another listing. This could cause an inventory manager to unnecessarily order more stock or a customer to leave your store and go elsewhere.
Internal product matching through AI will show you duplicate listings, even if their wording differs wildly.
This is an external use for product matching.
It can show you how much your competitors are charging for products you also sell. With a product match, you can accurately see the prices of your competitors and can adjust your strategy accordingly.
You could do this manually, but it would take a lot of time and effort.
Competitors will have different wordings and site layouts, which will make it difficult to catch everything you need to see.
Copyright Infringement Detection
A slightly niche use of product matching is detecting copyright infringement.
If you sell a bespoke product, you can use matching to see who else is listing it.
This way, you can make sure that only approved sellers are carrying your items, and you can look out for sellers selling imitations.
By making use of product matches, you optimize your offerings.
You can see how others are listing the products you carry and how your listings differ. Perhaps their descriptions are more technical or contain better SEO practices.
You can refine your own listings to improve your conversion rates through the comparisons.
Benefits of Product Matching for Customers
The benefit of properly implemented product matching for customers is simple—a better customer experience all around.
You’ll be offering your customers the most competitive pricing, ensuring that everything they see is accurate, and maintaining a neat store that is easy for them to navigate.
Next, let’s discuss the various models used for product matching.
First, we’ll look at how it used to be done.
The Old Way of Products Matching
The old way of product matching uses a lot of the same signals as modern product matching, i.e. SKUs, brands, titles, and images, but does so in a very piecemeal fashion. This leads to a lot of false positives and mismatches.
Moreover, it’s very time-consuming.
One would have to go through each listing and detect discrepancies manually. Then they’d need to determine whether a discrepancy was because of human error, or if these two near-identical products are actually different.
Modern product matching makes use of AI and machine learning to better understand what products correspond with each other and does it all in minutes.
Models for Smart Matching
There are four main models that AI programs draw on in order to do product matching. Although individual ones can be used, the best AI systems draw on all of them to more accurately match products.
Remember, any single one could be inaccurate in a listing, and so the more considered, the more accurate a match will be.
The oldest and most commonly used of these is the title model. It’s very straightforward.
A product matching algorithm simply looks at the title of listed products on various sites.
For example, a title could be “Unisex Converse Much Love Chuck Taylor All Star CX Pop Bright High Top White” or “HP 14" Intel Pentium 4GB/64GB Chromebook - Silver 14a-na0131wm”. These are very specific titles. They include the brand, product name, and specs.
Of course, some stores are more generic with titles, and so these same items could be listed as “Converse High Tops” and HP Chromebook”. This shows why drawing on other models is necessary.
Remember, each time you find comparable product listings, compare them to your own. This is a chance to maximize your ranking in an ecommerce search algorithm. SEO research tools are vital support in this regard.
Price is another model that is considered.
Of course, the AI must already have an idea of what it’s looking for but then it can narrow down options by seeing what items have a similar price.
While this is a weaker model for actually finding products, in comparison to other models, it is crucial for the purpose of setting competitive prices.
This is a very granular approach.
By looking at attributes, the AI looks at the specifications of products and compares them to find a match.
Some examples of what it would consider are colors, size dimensions, technical specs, etc.
Lastly, we have images.
Here machine learning matching will look at the images being used on product pages and attempt to match products.
It’s not a one-to-one image match where it looks for exact images (same size, etc.), because this can miss a lot.
It takes a finer approach and looks at the actual content of the images in order to match them.
Role of AI in Product Matching
Now you know what product matching in ecommerce is, so we’ll end off by taking a closer look at AI because it is a defining aspect of modern product matching.
AI has been around for a while now, but over the last few years, it has begun making great strides.
It’s started developing well in two areas that are key to product matching: natural language processing and image recognition. These translate well to specific ecommerce machine learning uses, as you can see from the discussion above.
These two developments let AI create excellent outputs but also better “understand” what it’s looking at.
In other words, you get better analysis and the ability to find similarities in things that aren’t uniform on the surface.
In short, quicker and more accurate product matching saves you time and produces better data.
Product matching in ecommerce is a valuable tool.
It’s vital to good inventory management, staying competitive, and giving your customers the best experience possible.
All of this works to increase your conversion rate, and strengthen your online business as a whole.
Garan is a writer interested in how tech reshapes the environment, and how the environment reshapes tech. You'll usually find him inoculating against future shock and arguing with bots.