You want to compare your products with the products of your competitors for building your pricing strategy. This can be tougher than you think because you have thousands of products and so does your competitor. Due to no global unique identifiers out there, it becomes a herculean task to effectively compare products from two databases. Product matching might sound simple with only a few products. But when it comes to e-commerce, it becomes quite a challenge. You can find different titles, descriptions, images, and other attribute details on two separate websites. This is a challenge even on the same website. Product matching helps to compare the same product across different channels.
Two types of product matching methods exist. One is manual product matching and the other is automatic product matching. Manual product matching is done manually where experts visually match the products. This is time-consuming, 15 times more expensive than automatic product matching, and requires a ton of effort. Automatic product matching is much faster, more accurate, and uses deep learning algorithms to return results. Businesses that have less than 500 products can think of using manual product matching. However, in an e-commerce scenario where thousands of products need to be weaved through, the answer is automatic product matching.
Automatic product matching uses machine learning for added accuracy and speed. Prices change at lightning speed and you should be able to find out the price in seconds if you are basing your price on your competitor’s prices. You want to grab every opportunity you get and this can be achieved with machine learning. Machine learning helps you achieve high product matching precision as it learns and adapts. The more you use it the better it becomes in product matching because it will associate similar terms that are used to mean the same thing. Product matching is only successful when there is 99% accuracy. If that isn’t the case then it is considered a mismatch.
How does software enable you to match products so accurately? There is a process that takes place for product matching. Firstly, the software provider needs to build a database that contains products. This number is generally in the billions in the case of e-commerce. In the beginning, it is all about information collection wherever possible. Whether it is the images, the description, the attributes, and so on. Next, a universal taxonomy is built. This ensures that particular patterns are uncovered and understood from the information available from images and descriptions among others. Then the standardized taxonomy is made.
The third step is when the product matching actually starts. In the third step, the software will look for specific identifiers that can help differentiate products and find ones that match. Then the various parts of the page that have information are crawled to understand as much about the product as possible. Then representation is taken into account whereby the system learns of the similar words used to describe the same aspects such as 14 inches and 14”. Machine learning ensures that information is categorized for the highest precision so that correct and accurate results can be displayed. This is why they are more reliable than manual product matching which is impractical in the case of e-commerce.
Product matching is an essential part of e-commerce and you want to ensure the accuracy is as high as possible. Let us say you want to build a pricing strategy by comparing your products with those of your competition. If you got wrong results with product matching, it can result in the incorrect setting of prices which can end up being costly for you. However, if you have robust product matching software then you have a higher chance of succeeding in matching products and achieving your goals. Intelligence Node’s product matching capability promises 99% accuracy so you know you can price your products correctly. It also has the world’s biggest product database which ensures you can match with absolute precision.