In the following article we are going to see what are EAN codes and how they can be well categorized by the fuzzy matching technique.
The EAN code is a type of barcode that codes an item in number. Originally, EAN codes were used exclusively to code "European Article Numbers" (EANs). Since 2009, EAN codes are used to code GTINs - Global Trade Item Numbers.
The GTIN is encoded in the barcode. The code consists of the following components:
There are 13 numbers under barcodes presenting the following information.
In the first glance, the barcodes may seem unique, that is, you expect that for example similar t-shirts have a unique barcode in a store, no matter what color or what size they are. However, there are so many examples that break this expectation. For example, you have seen so many times promoted products in supermarkets. Most of the time, two or some articles are packed together with a barcode different from the barcode that each has separately. In other words, as an example, the barcode on each product separately shows a price of 10,00 euros and the barcode on the package of 3 of them together show 20,00 euros. Therefore, the same product is being sold by different barcodes.
In the other words, the main EAN codes' problem is that there is no unique and governed database giving one single and permanent EAN to one product. Therefore, most of the time, suppliers use different EAN code for the same product and retailers also attribute one EAN code to different products when the coded product is no longer available in the stock.
It is not unknown for businesses, the problems that come with non-unique EAN codes.
It may happen to them so many times that wrong articles are distributed and so they have to be returned to the company. It not only takes time and expenses for the businesses but also affects their reputation and puts their customer retention services under question.
The errors of EAN codes that may seem small and ignorable at the beginning, in a larger scale can lead to wrong sale analysis on the sold products as well as future sale predictions.
Therefore, identifying the articles with their description is an inevitable process for the businesses, if they are willing to step forward on the right path of success.
Some companies try to solve this problem by tracking the EAN history of products, but that does not work because as the number of EAN increases and they get changed frequently over time, the process of tracking the history seems impossible. Are you looking for the solution? then continue reading this article.
Imagine there is a list of products and their barcodes and you are in charge of finding the same products.
What the companies do is as follows.
First, they do the exact matching and they find the same items. Then, they do fuzzy matching to find approximately similar but not the same items.
Basically fuzzy matching logic deals with texts and this is why it compares names not the numbers or digits of the EAN codes. Therefore, it makes the process of finding similar products feasible. If you are eager to know what fuzzy logic is click here.
YZR tool uses the fuzzy matching logic to solve the mentioned problems. At YZR we deal with the description and names rather than barcodes and digits, because barcodes are changing most of the time but not the description. Even if the description is changing we still have the chance to standardize and normalize our data with YZR. That is why our tool is the perfect choice for those who encounter the problems of identifying two elements of text, strings, or entries that are approximately similar but are not exactly the same. If you need more information, do not hesitate to ask for a demo here.
If you want to know more about this topic and understand why data quality is a major growth driver for companies, please download our white paper available here!