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).
Data
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.
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.
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.
Visual content is one of the most direct ways to improve your organic presence. Image SEO and visual search optimization are the 2 aspects you need to keep in mind.
In this article, I will instead focus on how you can optimize visual search to appear in Google Lens to get more organic traffic and more visibility for your products.
What Is Google Lens?
Google Lens offers one of the earliest platforms for visual search – trained on millions of images. In addition to recognizing objects, it is also capable of translating and transmitting text found on images.
As mobile devices have become more prevalent in everyday life, voice assistants have taken on an increasingly central role in search. The same is true for image search, as we all have a camera with us at all times. Google Lens goes one step further and allows users to search for a product they are interested in within seconds, using images and text at the same time.
Imagine a person sees a friend wearing a dress and she likes it. Google MUM (Google Multitask Unified Model) is the giant transformer AI-based model trained with images and text that led to the introduction of multimodal search or Multisearch as Google itself has defined it.
This means that users can combine information in different formats (photos, voice search, text, etc.) to find the product they are looking for. A real revolution for the search experience of users on Google, who until recently could only use text queries.
In detail, Google Lens scans the image and compares it to its huge photo index and searches for that exact dress and several similar alternatives. And you can also search for the same dress but in a different color!
This shows that it is essential for e-commerce to have products with SEO-optimized images that meet the requirements and have the necessary features to make them optimal for this search mode.
How To Activate Google Lens?
For Pixel users it is already active in the camera.
Android users can access Google Lens via Google Assistant or use the standalone Google Lens app.
For iPhone users, Google Lens can be activated via Google Photos or the Google app.
How Does Google Lens Affect SEO?
John Mueller answered answered this question in one of his Google SEO office-hours sessions:
From an SEO point of view, there’s nothing you can do manually, but if your images are indexed, we can find your images and highlight them when someone uses that type of search. So there’s no direct impact on SEO, but if you do everything right, if your content can be found in search, if your content contains images, and if those images are relevant, then we can drive visitors to those images and your content in different ways.
How To Optimize Your Images For Google Lens
Google Lens takes SEO for images to a new level thanks to Artificial Intelligence. And it’s worth seeing what we can do to optimize SEO on this basis.
The alt tags attached to a photo must contain the most important keywords, brand keywords and other information that is important in any type of SEO search. Do not forget to take care of the page titles and meta descriptions as well, which are equally important for search engine optimization.
2. Include EXIF and IPTC Photo Metadata
The EXIF (Exchangeable Image File Format) is a specification that defines data about images, sounds, and tags used in digital still cameras. The date with your EXIF data should be optimized for Google Lens to make the most of the information. This will help your image and content rank better.
The IPTC Photo Metadata Standard is the most widely accepted and used standard for describing photographs. It structures and defines metadata properties that allow users to add accurate and reliable data about images.
In 2018, Google Images introduced some new features to its image search results. Next to a selected photo, the creator of the image, the credit line, and a copyright noticeare immediately displayed. This works by reading the corresponding IPTC photo metadata fields embedded in the image file.
On August 31, 2020, this feature was enhanced to also display a licensable badge above an image and a link to the licensing information.
The EXIF (Exchangeable Image File Format) is a specification that defines data about images, sounds, and tags used in digital still cameras. The date with your EXIF data should be optimized for Google Lens to make the most of the information. This will help your image and content rank better.
The IPTC Photo Metadata Standard is the most widely accepted and used standard for describing photographs. It structures and defines metadata properties that allow users to add accurate and reliable data about images.
In 2018, Google Images introduced some new features to its image search results. Next to a selected photo, the creator of the image, the credit line, and a copyright noticeare immediately displayed. This works by reading the corresponding IPTC photo metadata fields embedded in the image file.
On August 31, 2020, this feature was enhanced to also display a licensable badge above an image and a link to the licensing information.
Structured data: structured data is an association between the image and the page on which it appears with the markup. You must add structured data for each use of an image, even if it is the same image.
IPTC photo metadata: IPTC photo metadata is embedded in the image itself, and the image and metadata can be moved from page to page without being corrupted. You need to embed IPTC photo metadata only once per image.
3. Add Structured Data
Add structured data markup that helps Google and search engines understand your content and images. For example, a recipe page would include in the structured data the name of the recipe, the author, the preparation time, and the publication date. It would also include a brief description of the text and images.
Similarly, when the image represents a well-known entity (such as a person or a landmark), you can use the schema:about property to link the photo and the concept in Google’s Knowledge Graph. To learn more about improving the markup of your images, ImageSnippets is a great starting point.
ImageSnippets is an image-based, data-centric platform that links machine-readable descriptions to images using state-of-the-art Semantic Web and Linked Data technology and standards. You can see, using the tool, how images are translated into triples (subject > predicate > object) and immediately become machine-readable. Interestingly, the tool also uses AI to extract features, translating them into additional markup.
4. Have High Quality And High Resolution Images
Make sure you use SEO optimized images for your content. They should be of high quality, have a high resolution and be in the various formats required from Google best practices.
Try our free AI-powered Image Upscaler to enlarge and enhance images from the website to improve structured data markup using a state-of-the-art deep learning model. Moreover, when dealing with Google Lens, it is essential to provide multiple angles of the same object and prepare the different versions for the same image (1:1, 4:3, and 16:9).
An Example Of Content Optimized For Google Lens
If you want to optimize your content for Google Lens, you can train Google to recognize them by providing semantic meanings behind media assets using structured data. You can also increase the quantity and quality of imagery to help Google learn about our products. Last but not least, remember to make always visible and accessible to Google the licensing metadata of the image. Adding licensing information allows web searchers to use the drop-down menu in Google Images for filtering images based on their license.Here below is one of the experiments we did, along with the team of Ippen Digital, where we succeeded in bringing one of their sites to the top for a visual query depicting the wall mural in front of their office.
If you searched for the mural using Google Lens in October, you saw that it did not show up in the results, meaning it was not at the top of the rankings and none of the top results came from their properties. It was also interesting that Alamy was among the top results. Alamy is an excellent site that collects images and does a great job of promoting search engine optimization of their images.
How could we help this publisher to optimize this content fo Google Lens?
To achieve this, we worked on three aspects.
Enrich the structured data behind these images. This allowed us to gain Google’s confidence that, on the one hand, the images are rights-free and therefore can be used, and, on the other hand, that the images really represent this entity in this particular geographic area. So we added as much information as possible using structured data markup.
Provide the images in different formats and from different perspectives. Because, as you can understand, if I need to train a model that is able to recognize an image, the model will benefit from high quality images, but also from multiple perspectives of the same object.
Adding licensing metadata making the images free and accessible for everyone.
After a few months, we were able to outrank Alamy.
In his talk at Knowledge Graph Conference 2022, Andrea Volpini talked about visual semantic SEO. In particular, he showed how you could use the knowledge graph of a website to train an AI-image generator that uses CLIP and a diffusion model and how a similar approach might be in use behind Google’s recent multisearch functionality.
Most Frequently Asked Questions
What is Visual Search?
Visual search is an AI-assisted search via your camera instead of text. Imagine you are searching for a new pair of sneakers. You could go to Google and type in “sneakers.” You can refine the search if you know the brand. Or you could open Google Lens, snap or upload a picture of sneakers you like, and search with that. That’s a visual search.
Using machine learning and artificial intelligence, Google can pretty much “read” the content of your camera lens and search for it, showing you the results that are closest to what you’re interested in. Appearing in this type of search is a sure way to increase your market share and organic traffic.
Is Google Lens Free?
Yes, it is. You can download it on both iOS and Android devices in their respective app stores.
What is Pinterest Lens?
Pinterest Lens is one of the most important platforms where mainly e-commerce companies are present. It allows searchers to quickly get inspired, but also to directly buy a product that interests them.
Pinterest can accurately identify 2.5+ billion objects.
80% of Pinterest users start their shopping journeys with visual search
The platform has 454 million MAU (Monthly Active Users) globally
It’s well represented in most countries, especially in the West.
McKinsey Global Survey on artificial intelligence (AI) suggests that organizations are using AI as a tool to generate value. Increasingly, that value is coming in the form of revenue. As this data shows, AI for advertising allows you to increase your return on ad spend (revenue) and reduce the amount of money you spend on staff time and ineffective ad budgets.Suppose we take Google Ads as an example. Before using AI and then the automation that comes with it, you needed to invest a lot of time and effort in finding keywords, devices, targeting, messaging, and bidding to get more leads, sales, and subscriptions. Work that was being done manually by humans. Today, all this is outdated. With AI-based solutions, you can improve the efficiency of various aspects that affect paid campaigns, not only on Google Ads but on the various platforms available.
Budget optimization and targeting
Performance optimization is one of the prominent use cases of artificial intelligence in advertising. Machine learning algorithms can be used to analyze ad spreadsheets and get tips on optimizing them, either by automating previously manual actions or highlighting issues you didn’t know you had.
In more advanced cases, AI can automatically manage ad performance and spend optimization, allocating budgets across channels and audiences and letting humans focus on more strategic tasks.
Similarly, you can use AI to analyze audiences used in previous campaigns and optimize them based on past performance data. In this way, you can also identify new audiences interested in the business.
Ad creation and management
There are systems based on artificial intelligence that can create partially or completely ads for you based on your goals. This feature is already present in some social media advertising platforms, which use intelligent automation to suggest ads that you should run based on the links you are promoting.
But it also exists in some third-party tools, which use intelligent algorithms to write the ad copy for you. These systems leverage natural language processing (NLP) and natural language generation (NLG) to create texts that perform well or better than the human-written copy.
How Structured Data Can Make A Difference In PPC
Structured data is crucial for organic search but equally decisive for paid campaigns.
Structured data enrich your content with the information necessary to make Google and search engines understand what you are talking about. It makes your data more accurate and more appealing. This means that when you add structured data to your website, you enrich your dataset and, by using your first-party data, you push input into ad systems that are read correctly because they contain all the required attributes and allow you to get better performance from your paid campaigns.
We asked Frederick Vallaeys, CEO at Optmyzr, a few questions on this topic. Frederick was one of the first 500 employees at Google, where he spent ten years building AdWords and teaching advertisers how to get the most out of it as Google’s AdWords Evangelist.
Specifically, I asked Fred, how you can use structured data to optimize feeds and ad components to have better results.
“As Google Ads become more automated, the ways we optimize PPC are changing and structured data plays a big role in that. Rather than managing details inside the ads system, we have to shift towards managing the inputs into the ads system so that the automations can handle the details correctly.
Think of ads for example. Advertisers used to write separate ads for every ad group but now with Responsive Search Ads (RSA), they can re-use the most powerful ad components across many ad groups while changing just a small subset of ad assets for each. In effect, ad text has become a form of structured data and in our analysis it can drive significant incremental conversions.
One of our clients wanted to deliver automotive leads to their dealership clients at a lower cost. They had the structured data on the website. Through this and Optmyzr’s Campaign Automator, they were able to decrease cost per acquisition (-25%) and increase ROAS. In addition, they could save over 50 hours per month by keeping prices, colors, and trim levels up to date for their automotive customers.
There are many other ways in which structured data impacts search advertising, from Dynamic Search Ads (DSA), to shopping ads driven by merchant feeds, local ads driven by business feeds, dynamic ads driven by ad customizers, etc.
Knowing how to feed better structured data into the automations will be critical for continued PPC success”.
On-site search is the functionality by which a user can search for a piece of content or product directly on your website by entering a query in the search bar.
This functionality, also known as internal search, can significantly impact your website: it allows users to find what they are looking for and discover new content or products they are interested in but didn’t know about. It also gives you valuable data and insight into what content or products your audience likes and is most interested in, allowing you to tailor the website to the visitor’s specific needs.
Building an optimized on-site search can get you more conversions and foster brand loyalty. If the user doesn’t have a relevant search experience, they may choose to go to your competitors, risking losing customers.
On-site search is critical, not only if you have an e-commerce store. Any website with a collection of content can reap the benefits of relevant, fast, and easy-to-use site search: better click-through rates, more user engagement, and a better understanding of customer needs.
Why Is On-Site Search Important In SEO?
There is a relevant connection between SEO and on-site search. SEO allows you to increase organic traffic to your website. On-site search makes sure that visitors to your website find what they are looking for quickly and easily. SEO focuses on creating quality content that is relevant to your audience. But the more content you have on your site, the harder it is to get it found. That’s why there’s an internal search. It makes it easy for users to find your content, whether they’re looking for information or products, or services.
In addition, analyzing data on searches made by visitors to your site helps you understand which content or products are most popular and if there are any gaps in your content marketing, sales, or product development strategies. Knowing this data allows you to define more effective strategies and actions to optimize your website for both content and user experience.
How Can You Optimize On-Site Search with AI?
The Knowledge Graph is the key to your on-site search optimization. And below I will show you why.
The Knowledge Graph is the dynamic infrastructure behind your content. It allows your website to speak the native language of search engines, allowing Google and others to understand what you do and what you’re talking about. In this way, you build well-contextualized, related content that contains consistent information and addresses the needs of your audience. By providing users with a relevant experience, you get higher rankings on Google and more organic traffic to your website.
Not only that. We’ve seen that Google search is changing, moving from information retrieval to content recommendation, from query to dialogue. So the search engine is not only answering the user’s questions, but it’s also capable of discovering new content related to the user’s interests. Training the Knowledge Graph allows us to go in this direction, showing the user new content related to his search.
If Knowledge Graph makes Google search smart, it can make your on-site search smarter.
Creating a custom Knowledge Graph and then adding structured data allows your website to better communicate with Google and other search engines, making your content understandable to them. The same will happen for your website’s internal search engine. So, when a user enters a query, the results will be more consistent and respond to the user’s search needs. Also, through Knowledge Graph, you will be able to build landing page-like results pages that include FAQs and related content. In this way, the user will have relevant content, and their search experience will be more satisfying.
By answering users’ top search queries and including information relevant to your audience, these pages can be indexed on Google, also increasing organic traffic to your website.
For example, this is the result for the query vakantieparken met subtropisch zwembad (trad. “vacation parks with subtropical swimming pool”). As you see, we have a page with all the solutions offered by the brand and a FAQ block that will help give the user additional answers for what he is looking for.
If we search the same query on Google we will have the same page as a first result in the SERP.
Search engines use entities and their relationships to understand human language. A generic occurrence of the term ‘delivery’ on the website of a logistic company might indicate the more specific concept of ‘last mile delivery’- using structured data the term is lifted as it gets uniquely identified by a Machine-Readable Entity ID that references a similar concept in public graphs like Wikidata or DBpedia (ie last mile in this example). When a term is semantically enriched (or lifted in our jargon) a search engine is also able to leverage its synonyms and neighboring terms. A user making a search for ‘last mile delivery’ or even ‘last mile’ on that website will be able to find that page.
With WordLift, you can build your Knowledge Graph and add structured data to your website content. Together, we can optimize your site search if you already have it or make it in a way that your customers will love.
On-Site Search Best Practices
To have on-site search optimization, you can follow some best practices.
Make the search box user-friendly
Make sure the search bar is visible from any device and long enough to contain the user’s query. Usually, it will be at least 27 characters long. Remember to place search bars on your site’s primary pages, but not on all of them. Putting it on the checkout page or landing pages may be inappropriate and distract the user from other required actions, such as purchasing a product. Insert a clear call-to-action in the search bar and encourage research with phrases like “insert product, code or brand” or “what are you looking for?”, etc.
Analyze your search data
Analyzing data on internal searches on your website allows you to understand what users are interested in, what they’re looking for, and what content, products, or services are most popular. Also, you can see there may be search intents that aren’t covered with existing content, so work needs to be done to create it. (For example, if a user searches for “how to add FAQ markup” on wordlift.io, we know we are missing that content, and it needs to be created).
Improve imperfect inputs
Facilitates on-site search by improving imperfect inputs and making them predictive. Use autocomplete, autocorrect, filters, and facets to assist the search. Users will appreciate it.
Make the results page intuitive, helpful, and inspiring
Semantic search analyzes the context and searches intent behind the query to deliver relevant results to the user. Also, take advantage of “No Results Pages” to make suggestions to the user about other similar content or in line with their search, so they don’t miss out.
Always redirect the user to the right page (when you have already one)
Bypassing the search result page will make the customer journey smoother. If you have a laser target page on “outdoor speakers” and the user is searching for that, make sure to redirect him/her directly there without passing by the results page.
Not all result pages need to be the same, you can curate some of these pages to cover long-tail intents that really matter. You can do this by adding intro text and FAQ content on these pages. Now, these “enriched” result pages will also work well in search (provided that you add them to the sitemap).
A good-looking search box doesn’t mean anything if the results aren’t helpful to the searcher. And that’s where structured data and the Knowledge Graph come in, as we showed you above.
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