By Valentina Izzo

2 years ago

SEO and Conversational AI: learn how we trained it using structured data to get more organic traffic and a better user experience.

Table of contents:

  1. What is conversational AI?
  2. Components of conversational AI
  3. Benefits of Conversational AI in SEO
  4. SEO and Conversational AI: a real-world use-case
  5. How to train conversational AI

What Is Conversational AI?

Conversational AI is the set of technologies, such as virtual assistants or chatbots, that can “talk” to humans (e.g., answer questions).

Conversational AI tools use machine learning, automatic responses, and natural language processing. Their goal is to recognize and replicate speech and communication and create an experience of human interaction.

AI technology can accelerate and simplify relationships with consumers by answering their questions and relaying their requests. It can be used on websites, online stores, and social media channels, and is often used in customer service.

Components Of Conversational AI

Conversational AI systems have 4 elements that contribute to their development and operation.

Machine Learning

Machine learning consists of algorithms, functions, and data sets that systematically improve over time. Artificial Intelligence recognizes patterns with increasing input and can respond to queries with greater accuracy.

Natural Language Processing

Conversational AI uses NLP to analyze speech through machine learning. Named entity extraction (NER) is widely used to detect intents (what the user has in mind when interacting with a chatbot).


The success of conversational AI depends on training data from similar conversations and contextual information about each user. Semantic rich data, in particular, can make a real difference when training an AI system as it provides contextual information.

Conversation design

Companies need to develop the content that AI will share during a conversation. Using the best data from the AI application, developers can select responses that fit the AI’s parameters. Human authors or natural language generation techniques can then fill in the gaps.

Benefits Of Conversational AI in SEO

Here are some key benefits of conversational AI.

Create a personalized user experience

The use of conversational AI makes it possible to provide users with a personalized experience that meets their needs. Whether it’s making an inquiry, completing a purchase, or providing customer service, you can ensure users get the answers they need in real time without having to engage your team. 

Users who find the answers they are looking for are less likely to leave your site. This way, the time spent on the website increases and the overall performance of it benefits from the positive impact of using Conversational AI.

We have developed a chatbot that is active in our documentation, and we show you the average time spent year-over-year on the most popular pages

Avg. Time spent year-over-year on the most popular pages.

This data is for the last 7 days, but it already shows the immense positive impact conversational AI has on our website.

Collect valuable data

Conversational AI, like an internal search engine, allows you to understand user personas. The resulting data can be used to drive your business forward and give you an edge over your competitors.

Get more conversions and new up-sell opportunities

Providing appropriate and timely information and updates to customers through conversational AI increases conversion rates. Virtual assistants help customers navigate and find the right product or service for their needs.

Conversational AI can also provide consistent and compelling up-sell opportunities that take into account consumer preferences, time, and other data to make the best possible offer.

SEO and Conversational AI: a real-world use-case

As we know, structured data allows the content of a website to be indexed more easily by Google and other search engines. The content is understandable for the machine. It has all the information it needs to understand what we are talking about and can use it to answer search queries. As a result, the results in the SERPs are more consistent, and users get more complete and relevant answers to their needs. In this way, our website gets more organic traffic and provides a better user experience that increases conversion rates. 

A chatbot works like a search engine. So what I do for Google will improve my chatbot (and my internal search). The same content we prepare for Google can also be used to train an artificial intelligence for conversations like a chatbot. And that’s exactly what we did to develop the chatbot for our documentation.

If you want to learn how to create a Knowledge Graph-based chatbot, I recommend our article.

We trained our chatbot using Jina AI DocsQA’s q-a service and we extended its knowledge by adding to the indexing flow also FAQs (question and answer pairs) from our knowledge graph. Structured data content (such as FAQPage markup) is available using WordLift GraphQL end-point.

This image has an empty alt attribute; its file name is chatbot-wordlift-docs.gif

Not only that, we have included the link to our blog where the user can find the answer and read the full content. In this way, traffic is directed from the documentation to the blog, increasing the number of visits and making the user experience more relevant.

Similarly, we are indexing (or trying to index) the FAQ content we have on the blog, and we plan to extend the same conversational AI to other parts of our site.

Developing this type of chatbot using structured data and Knowledge Graph, we expect to achieve: 

  • increase time spent on the page on docs
  • increase number of interactions with the chatbot (not yet available)
  • more visits from the doc to the blog.

We will soon share our reporting!

How To Train Conversational AI

There is more that can be done with a knowledge graph in the context of chatbots. Here is our latest research paper Question Answering Over Knowledge Graphs: A Case Study in Tourism on how to use the data in a knowledge graph to automatically generate questions and answers, which in turn are used to train the chatbot. To evaluate the proposed approach, the SalzburgerLand Knowledge Graph is used, which is real and describes tourism entities in the Salzburg region in Austria. The research results show that the proposed approach improves the end-to-end user experience in terms of interactive question answering and performance

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