From Data to SEO: Automated Category & Filter Descriptions

Welcome to the world of SEO, where Google is the undisputed ruler and we are all just its puppets. Or is it?

It's time for a more efficient solution: the automatic and rapid creation of relevant and SEO-optimized text for category and filter pages. Combine your customers' search results with your product information and link user needs directly to what your online store has to offer.

SEO – a persistent bottleneck problem

The importance of SEO text and filter text is undeniable. Whether through a trained team of SEO experts, a partnership with an agency, or a jack-of-all-trades marketing guru, every company engages in SEO to a greater or lesser extent, with varying degrees of success.

Regardless of the approach, however, they all face the same problem: creating and maintaining category pages that meet current SEO standards while also fulfilling the specific needs of their target groups is a tedious and time-consuming task. The constant change in search algorithms, the demand for high-quality, relevant content, and the need to present products effectively pose an ongoing challenge for companies.

Giving up on SEO means burying your shop.

However, everyone should be aware by now that SEO is essential for online success. So it's time to turn our attention to the really important things – solutions to these challenges!

The content of category pages is an essential part of a comprehensive SEO strategy. We are also happy to help you with a comprehensive strategic orientation of your content.

Good to know: The importance of SEO

SEO text: automated category pages

The solution to the problem is right under most companies' noses, without them even knowing it: the sensible use of their data!

By combining your own product data with external data—the relevant long-tail keywords—it becomes possible to more accurately identify the needs and search habits of your target audience. This product data optimization allows you to present products in a more targeted manner and offer customized solutions that directly appeal to customers by creating SEO-relevant texts based on this information.

But first, back to the data: Many companies already have extensive data sets. However, even high-quality data in product information management (PIM) can lie dormant if it is not in the right format. This is where DataNaicer comes in, putting the data into the right format and thus enabling its optimal use.

With DataNaicer, long-tail keywords are converted into machine-readable formats and relevant category data is automatically aggregated from product data.

This data can be used as a basis for generating SEO-relevant category descriptions. The result is precise and complete content that is optimally tailored to relevant search queries and highly relevant to potential customers – allowing content to be continuously updated at the touch of a button.

How it works

1. Entering long-tail keywords

First, the relevant long-tail keywords are provided, which will serve as the basis for creating the category pages. Long-tail keywords are specific search terms or phrases that are used less frequently but are more targeted. They are crucial for accurately capturing the interests and needs of the target group. It is therefore important that these keywords are specific enough to cover relevant search queries, but at the same time have sufficient search volume to be SEO-relevant.

2. Product selection

In this step, the products to be presented in the new category are selected. This includes determining which products fit the theme of the respective category and are relevant to the target group. The selection of products forms the basis for the design of the category pages and enables DataContentNaicer to tailor the content specifically to the selected products. The unique features and benefits of the products are highlighted to increase the attractiveness of the category pages for potential customers.

3. Structuring and enriching the data

To ensure a smooth process, we first take the long-tail keywords provided and convert them into machine-readable data formats that can be efficiently processed by our system. The associated category data is then automatically aggregated based on the keywords. The relevant information required for creating the category pages is automatically merged. This ensures that the content of the category pages is accurate and complete, optimally tailored to the relevant search queries, and highly relevant to potential customers.

4. Automatic generation of text

Once the data is available in machine-readable form, the category descriptions are automatically generated using the Text Robot or ContentNaicer. Based on the information entered, SEO-optimized texts for the category pages are created. The relevant information about the selected products is integrated and linked to the long-tail keywords to ensure that the category pages are optimally tailored to the needs and search queries of the target group.

5. Delivery of the new category descriptions

Finally, the generated category texts are delivered to the desired destination, be it an online store, a product information management (PIM) system, or another destination. This allows the new content to be seamlessly integrated into the online presence and benefit from its SEO optimization.

Global applicability: automated and localized

DataContentNaicer is designed to be suitable for all country markets. This means that category pages can be created not only for one country, but also for international markets and different language regions. Regardless of whether companies are globally oriented or present in individual countries, DataContentNaicer can customize and optimize the category pages accordingly to meet the needs and search queries of the respective target groups.

Adaptation to local conditions

Not only are the linguistic differences between different countries taken into account, but also cultural characteristics and local search trends. The category pages created are therefore not only linguistically correct, but also culturally sensitive and tailored to local needs. This allows you to ensure that content is relevant and appealing in every market and language region.

An example: Search behavior in Spain differs from that in Mexico, even though Spanish is the national language in both countries. In Spain, people search for “zapatillas”, which means “sneakers” while in Latin America, they search for “tenis”.

Search behavior varies not only in vocabulary, but also from region to region. People in different regions search for different products depending on factors such as climate zone or geographical location, for example, whether they live in the mountains or on the beach. Certain product groups may not have a relevant search volume in one country, but may in another.

A simple translation without localization using well-known translation tools would have had a negative impact on the visibility of the online store.

Example: Automated category pages based on keywords

A company provided us with data for 1,400 to 1,900 category web pages from its German, Spanish, and English web shops, each with one keyword phrase per data record.

The requirements for the texts were clear: the original keyword phrase should appear as the heading, and the keywords or keyword phrases had to appear twice in the body text.

One challenge was that there were no existing category clusters in the original data, which was crucial for the architecture of the text automation project. In order to design the project, category attributes had to be created first, which were generated from the given keyword phrases.

Using DataNaicer, a data model was created that formed the basic structure of the text concept. Since the existing data with keyword phrases was unstructured, it was enriched by adding new structured attributes.

This allowed the keywords and keyword phrases to be organically integrated into the automated text without compromising the diversity and overall quality of the texts.

Secure automation in the world of Google and Co.

Following the major Google Core Update in March 2024, the security of AI texs is just as important as the significance of SEO. However, there is still uncertainty about how secure automated texts actually are and will be in the future.

There are currently numerous strategies for protecting AI-generated text from detection. But how can we ensure that our texts will continue to be protected in the future? The solution is as simple as it is complex: Our texts are classified by Google as human text—because, basically, that's what they are. Every text, every variation, and every synonym is conceived by our copywriters, while the algorithm enables high scalability.

Here you can read about the differences between AI, NLG, and other technologies again.

Since our texts are created by humans, they are not at risk of being penalized by search engines.

SEO advantages

By using automated category and filter text, companies can use their data efficiently and create high-quality category pages with minimal effort. These pages are not only SEO-optimized, but also precisely tailored to the needs and search queries of the target group.

The advantages of this AI approach are manifold: it saves time and resources, enables scalable content creation for international markets, and improves the visibility and reach of the website.

The advantages of automated category descriptions at a glance