Examples

Correctness

Achieving product data correctness is not an easy feat. New products are introduced every day, prices are changing, retailers change their policies... It's no wonder data gets lost or corrupted. It doesn't hurt only your users, your brand reputation and revenue are at risk too.

When someone performs a search in a search engine using a product identifier as a search term, one expects to see relevant results that will lead to pages displaying correct information. There are many examples where multiple online stores display different product information for the same product.

There are many other issues that may occur with your product data. Prices can miss currency, html code can be found in a field, there can be format errors and/or the same information may appear for more than one product.

Completeness

Search engines are constantly improving and setting new product data requirements. Comprehensive product data empowers you to reach more users by improving your rankings in search results.
Not only that, but when users are searching for a specific product on your website having all the important product information, e.g. model number, upc, brand name, allows them to easily find exactly what they are looking for.

Let's say a user tries to find a product at your online store by searching for a brand name and model number combo. You do have that product but not the complete information too, that is, you do not have its model number. So it may happen that the user gets displayed all products that are from said brand.
What it also may happen is that the search doesn't retrieve any product at all.

At the same time, there are other online stores carrying the same product, but they do have rich product data, thus users will be able to easily find the specific product.
Comprehensive product data is the most effective way of ensuring that your users find exactly what they need at your online store. For you this means higher conversion rates and higher revenue.

Data suggestions

We generate suggestions by matching product records from one store with other records for the same product from different stores. We can generate suggestions only for the fields that contain values in records that were matched.