Multilingual Product Knowledge Graph + Product Structured Data: the Secret SEO Success Formula

Table of contents:

  1. Why is Google Opening Free Listings?
  2. What We Underestimate the Most as Search Marketers
  3. What Different Website Structures Mean for Search Engines
  4. Why the Merchant Feed is Not Enough
  5. How the Merchant Feed and Structured Data Work Together
  6. Multilingual Product Knowledge Graph or…is Your SEO Strategy Graphy Then?

Why Is Google Opening Free Listings?

Google made a big move in 2020 after 8 years of adapting its pay-to-play model by opening shopping opportunities as free product listings for registered Google Merchant Center users. A few days ago (13 September 2022) they even extended their enhanced product experiences to all users by using the product structured data markup which previously was available only to registered users in the initial phase. 

This absolutely proves that they really started to understand the e-commerce space: Google introduced these features after accepting that Amazon dominates as a product search engine for years. The idea is simple: provide the best shopping experience for everyone, no matter where people come from, how developed their business is, what type of industry they are operating in, and whether they advertise on Google or not. 

We now have the product structured data in a combination with merchants’ listing reporting and this is a pivotal change for e-commerce SEO reporting overall. 

Google’s move represents a huge opportunity for all retailers, especially for many great small to medium size businesses given how much Google knows about commercial intents through Google Maps and Google Search in particular. By now, if you were a small retailer, you didn’t have the space to compete with bigger companies because they had bigger budgets than you. However, from a customer’s perspective today, it is not important where the product comes from as long as it provides what the client needs. Google understood this perfectly. 

That is why the priority now is not only to make a listing on Google but also to provide as much metadata about these listings in order to get better search visibility across all of Google’s products. 

What We Underestimate The Most As Search Marketers

Search engines like Google need these details because they have a hard time obtaining this data reliably and this is important when building their multilingual product knowledge graph. We as search marketers tend to think that this is a trivial task and underestimate the whole computer science and engineering investment that needs to be taken in order for the information retrieval process to work as it should. What seems to be trivial, is in fact a very complicated process of parsing and harvesting information, unifying it in a structured way for everyone. This is the primary reason why these free listings appeared in the first place – grasping data in a smarter and faster way than ever. 

What Different Website Structures Mean For Search Engines

Let’s take an example of a random product listing. It contains multiple data touchpoints that we can use and optimize for:

  • Ratings;
  • Price;
  • Savings;
  • Shipping details;
  • Related products;
  • Questions and Answers;
  • Manufacturer details;
  • Different facets: color, size, brand, distributor…

If we look closely at Amazon, we can observe that the information extraction process is easier for them because they have specifically defined structured fields from which they obtain all this information directly and at scale. Google’s way is more difficult because they obtain this information from many, many websites full of unstructured data, and not just that: they need to make a sense out of this data by combining different parts together or skimming through what might be unhelpful content. 

Now imagine that this information is partially available on other e-commerce stores which follow a different page structure, use different UI patterns and might even contain other metadata or visually-rich features like seeing the product in 3D. What will be the strategy to obtain this data from all these different stores at scale? This is where structured data fields and product structured data come into play.

Why The Merchant Feed Is Not Enough

In order to get a new product into Google Shopping, we can use the famous merchant feed, populate it with relevant data and provide it to Google Shopping. However, this is not enough in real life because Google likes providing 2 data-entry points to them: 

  • the merchant feed;
  • the structured data (schema markup) and a syncing strategy between themselves.

This is very useful for small businesses who offer their products with certain competitive advantages compared to bigger businesses, e.g. shorter delivery time or cheaper price and so on. They can get themselves featured on Google by exploiting the power of these 2 data-entry points. The more data you provide to Google, the bigger the business opportunity. What is even better is the fact that this data is universal, reusable, and specifically owned by you directly (first-party data). That is why your product data is the business weapon that you can exploit to win new markets online. 

This might not be the case with Amazon because they can force you to “adapt” to their e-commerce and product strategy slowly over time. It is not a secret that the umpire is the player itself: researchers have found a bias in private label product recommendations where Amazon favored its own products beyond organic recommendations. This means losing control over your products, shopping experience and your own competitiveness – competing with endless products on a single platform or Amazon’s products themselves. 

On the other side, when you have your own website, the data is yours and not Amazon’s. Therefore, it will be quite strategic for you to sell there because you have control over your data. You can do both Amazon and Google and even go beyond them. How great is that?

How The Merchant Feed And Structured Data Work Together

It is important to understand that there are some things that we can do with the merchant feed and not with structured data, and vice versa, things that you can exploit with structured data and not with the merchant feed itself.

This happens when we do not have mature schema markup for a particular purpose, while on the other hand, the merchant feed does not have fields that can be contextually explained by existing schema markup types.

Lately, there have been some challenges with the missing field “priceType” in the offers.priceSpecification attribute for the product structured data but Google is fixing this, so you don’t need to worry about this error in the Merchant listings report anymore.

In any case, the power of these 2 data entry points can provide more contextuality and thus an opportunity for you to outperform Amazon’s product search experience. It is not just about providing more information about a product, it’s about leveraging all the different platforms that Google has to reach targeted potential customers.

We have found that many organizations have a wealth of data available in their content management systems (CMS), but are not using it. Chances are, you are doing the same thing because you lack a holistic SEO strategy that ties all this information together. Therefore, all this data remains buried somewhere in text descriptions in your own product catalogs.

Remember, it is important to make your content explicitly available and pack it in a structured way that it is semantically understandable.

Multilingual Product Knowledge Graph Or…Is Your SEO Strategy Graphy Then?

Given the aspect of cross-system interoperability and the ability to harness the power of product-structured data, it makes sense to invest in a multilingual product knowledge graph that can organize your product metadata in a scientific way.

A product graph models entities that you consume or want to buy, while a typical knowledge graph models general entities, their types, relations and so on. 

Here’s an example of a product knowledge graph:

Product knowledge graphs are key business enablement: you can reuse this data for ads automation, SEO marketing intelligence but also for producing custom recommendations and even optimizing for Google Assistant for example. Natural language generation and natural language optimization are the second layers that can be built on top of that and with them. The possibilities for improvement are endless. 

The customer journey is a graph too. If you want to catch your customers on multiple levels and exclude duplication and assumptions from your strategy, the best way to meet your customers halfway on multiple touchpoints is to actually build a product knowledge graph in your organization.

WordLift has built a stack of technologies to improve the data in these product knowledge graphs. The practice has taught us that product knowledge diagrams are absolutely essential if you want to improve your data harmonization, data reconciliation, and knowledge cleansing, especially for multilingual and large e-commerce web platforms.

Here’s an example of the search performance of a client of ours before and after creating the product knowledge graph – the difference after the implementation is more than beneficial. Even though the initial SEO situation was not perfect and we had a lot of URL warnings and other technical SEO stuff that remained unsolved, the search impressions jumped after we added the PKG.

To sum up, we build knowledge graphs as a basis for unconventional, next-gen SEO. That is why customers come to us – to create a knowledge graph for them so that we can automate SEO at different levels and that it is pretty unique: we’re creating this PKG much like Google does and in a way that is native for them. 

To learn more about product structured data and how to build a multilingual product knowledge graph for your e-commerce website, watch the video with Jason Barnard and Doreid Haddad👇

Leave a Comment

Your email address will not be published. Required fields are marked *