Online retailers often focus on product descriptions for their online stores when creating content. Category descriptions are often forgotten, but it is exactly these that are of great importance. It is so simple: the required data can be derived from the product data that is available to e-commerce companies anyway. However, it is often a challenge to use up-to-date and thus not outdated product data.
In this article, we'll show why creating and using category descriptions makes sense, what data is necessary and how these can be aggregated with the help of DataCater.
Use e-commerce companies category descriptions in their online stores, then many positive effects result:
The category data is derived and summarized from the pure product data. The exemplary product data in image 01 are the store name, category name, brand, time of the last update and information about the delivery time.
In the example, this results in the category data store name, category name, number of products, time of the last update and information about the delivery time. The category data serves as a data source for an SEO optimized category description.
In order to integrate artificial intelligence (AI) applications as a business, high-quality data is necessary. Data can change constantly - keeping it up to date and high quality while maintaining content can be a big challenge and a lot of manual effort for e-commerce companies. Keeping data and content up-to-date without manual effort and completely automating content production is possible with a software solution like DataCater.
DataCater is a self-service platform for streaming data pipelines. The software transforms, cleanses, filters and enriches data in real time during transfer. It produces high-quality and always up to date data that is usable in an AI application.
Based on the product data that resides in the database (e.g. Google Cloud BigQuery), automated category page descriptions are created using AX Semantics software. DataCater enables companies to unleash the full potential of data and efficiently connect data systems. In doing so, DataCater is the connector between the product data and AX Semantics. The goal of the software is for companies to use high-quality, up-to-date data in their AI application. The technique that DataCater uses is called "change data capture". It ensures that only relevant data is transferred. This makes the process much more efficient. Because the database always contains the latest data, it is guaranteed that the descriptions are always up to date and reflect the current status.
If, for example, new products are added to a category, a new description with the changed number of products as well as a reference to the new goods (for example: "This week it is especially worthwhile to check out, because there are new products in the assortment") is generated and displayed.
Stefan Sprenger, Founder of DataCater and Alexandra Waldleitner, Customer Success Manager at AX Semantics talk on the concrete example of a furniture online comparison portal how high-quality category descriptions are created from current product data.