Artificial Intelligence: how to automatically improve product data quality for your e-commerce website?

The importance of product data quality

Due to the fact that an increasing amount of products is sold in an online environment, complete and accurate product data is becoming more and more important. Even for offline sales, many customers still orient themselves online. High-quality product data ensures that customers are provided with all the information necessary to decide whether or not to buy a product. Thus, high-quality product data or a lack of product data can both drastically influence the sales on your e-commerce website.

High-quality product data in essence means that the product data is accurate, complete, relevant, valid, timeless, and consistent. Great product data is a key factor of customer satisfaction, and essential in converting customers.

The pitfalls of a lack of product data

Poor-quality product data can impair the efficiency of an organization as high-quality data is much easier to utilize compared to poor-quality data. Thus, the question is how to turn poor-quality data into high quality data?

Important steps to improve data quality is to first collect and prepare current product data from ERP (Enterprise Resource Planning), marketing systems, and external suppliers. This data can be collected within a Product Information Management (PIM) system. Then, the PIM system can help to create groups based on product attributes, making it easier to organize thousands of products. 

Nonetheless, with a lack of sufficient product data, the organization of products into group based on product attributes is impossible. Another way of organizing products is to categorize them into groups that customers potentially search for. For example, if a company is selling jackets, the categories could be rain jackets, winter jackets, denim jackets, etcetera. Again, with a lack of sufficient product data, this categorization of products is impossible.

How to enrich poor data?

Enriching poor data is a timely process, and oftentimes this process is still done by hand. A data steward is in charge of ensuring the quality of an organization’s data assets, as well as their metadata. For an organization selling fifty products, data stewards could help to enrich data by manually ticking checkboxes, applying categories, or creating groups based on product attributes. With such a small amount of products, it is even possible to create SEO texts based on competitor’s websites. However, imagine doing the same for an organization selling more than a thousand products. This process can take a lot of time, and can cost an organization a lot of money. 

How to automate this process?

Luckily, with the help of Artificial Intelligence it is possible to automatically enrich product data. More features are added to your product data by extraction from texts and images and smart scraping of PDF’s and e-commerce websites. This way, you can save valuable time and money, and offer the right product to the consumer in an instant due to better data quality.

Squadra Machine Learning Company offers such a smart tool for data enrichment; This is a set of algorithms to enrich product feature data. It uses Artificial Intelligence to extract data from sources like (ERP) product descriptions, product images, PDF files and websites. 


First of all, is capable of supporting data stewards by the extraction of product features from both texts and images. As shown in the example below, a manufacturer might deliver a long sentence with product data, and is capable of extracting the relevant product features. 


Secondly, is capable of scraping websites of competitors or suppliers as shown in the example below. This data is already visible online, thus mostly include more coherent texts compared to data files provided by the supplier. 

What are the benefits of using

With the use of, both wholesale and retail organizations can improve the availability and quality of product data on their e-commerce website. By automatically enriching the data, these organizations can save manhours from product specialists for manually searching, judging and entering product data. Moreover, increased fill-rates of product data and improved data quality results in increased profits.

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