Conversational AI has been part of the enterprise landscape since 2016, but it has entered the scene in pandemic times. It has supported customer service and provided content suggestions to help businesses and people through difficult times.
In this scenario, it has become increasingly necessary for enterprises to invest in developing conversational AI to cope with changing user behavior. Conversational AI is therefore among the top 5 categories in AI software spending and its evolution from simple rule-based bots to intuitive chatbots that can accurately mimic human responses by providing assistance to customers and company employees.
So the evolution of user behavior has also led to a change in how search engines work. Google search is increasingly becoming a dialog. The advancement of conversational search inevitably affects SEO. In this article, I’ll show you how you can build a Conversational AI by using SEO to provide users with an optimal experience that increases the number of visits and the time users spend on your site.
The Future Of Search
Search could be reimagined as a two-way conversation with question answering systems synthesizing and answering users’ questions much as a human expert.
Metzler, Donald, et al. “Rethinking search: making domain experts out of dilettantes.” ACM SIGIR Forum. Vol. 55. No. 1. New York, NY, USA: ACM, 2021.
Today, when you do a Google search, you get results appearing in different Google rankings and features: SERP, top stories, People Also Ask, Google Knowledge Panel, etc.
Thanks to conversational AI, search will take the form of a dialog between the user and the search engine, which is not only able to answer a query, but also to anticipate the user’s wishes.
If you own a website or are an SEO, then you need to be able to fit into this workflow. In “traditional” search, SEO was required to:
Rank at the top of Google’s search results for target queries, e.g., “GPT-3”.
Earn a knowledge panel in Google’s Knowledge Graph for your business, people, products, etc.
Rank relevant video content on the first page for target queries.
Answer specific questions to be featured in the People also ask (PAA) section.
With conversational AI, the plan of action changes, and to make this happen, it is necessary:
Win the SERP competition for target keywords.
Provide a great user experience on your website.
Make it easy for users to find the right content (UI, navigation, search functionality, chatbot, ..).
Optimize for user retention whenever the information is available on your website.
For this to be possible and for you to get into conversational search, you can use your content. By storing your data in the Knowledge Graph, you can make your content accessible and reusable for developing a conversational channel such as a chatbot.
Building a conversational AI channel on your website is crucial in taking advantage of the opportunities and benefits of interacting with your customers. This will allow you to leap ahead and gain a significant competitive advantage over your competitors.
But let’s go through the concrete steps that will help you build a successful conversational AI starting from SEO.
Conversational AI and SEO: Concrete Steps
In this scenario, SEO plays a crucial role. How can you build a successful conversational AI ? To do so, you need to follow the steps below.
Build your Knowledge Graph. In this way, you can leverage the power of entities and the relationships between them to provide Google with all the information it needs to understand your site content and to provide users with results relevant to their search queries.
Earn a Knowledge Panel. Through it, Google provides quick and reliable information about people and brands. Getting a Knowledge Panel on Google depends on the data. The more accurate and consistent they are, the more accessible Google will put the information together and return a perfect Knowledge Panel.
Use the VideoObject.Adding Schema Markup’s VideoObject feature, you can use video as a magnet to attract traffic to your content by building irresistible, clickable snippets.
Having a bot and having the KG + FAQ you can deliver a better user experience to the users on the website itself (and eliminate the requirement to go back to Google to search for another info).
A Real Use Case: From Google Search To WordLift’s AI-Powered Chatbot
WordLift is replicating what Google is doing (at the scale of a single website).
Leverages the same data published on the website
Creates a chatbot on top of transformer-based models
In terms of information retrieval, 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.
In this example, you can see how to take ownership of the user journey with WordLift.
Case 1: Google directly provides the answer to the question/search query. It’s the top page result.
Case 2: Google returns the passage that talks about GPT-3 in the article about generation product descriptions.
Case 3: Google displays the questions/answers that are added as FAQ schema markups to the article.
Developing a chatbot like WordLift’s that can replicate Google’s responses has many benefits:
Improve the user experience by providing answers to their questions
Avoid losing a user we worked hard for by keeping them on the site once we have an answer.
From an analytical point of view, increase the number of users visiting the website and increase the time spent on the website (if this happens, it means that we serve users better).
Takeaways
Rethinking Search. Search can be reimagined as a conversation.
Two visions. Know-it-all AI vs. relevant and accurate information.
No additional effort. Same content that is created to support Google ranking and visibility can be used to train an AI to converse like a chatbot.
Multibot orchestration. Stack of bots, master bot and bot grouping.
Personalization. Era of hyper personalization for users and for businesses.
Multimodality. Enable text, image, videos, etc. in a single query.
To know more about how Conversational AI meets SEO, watch the video👇
Looker Studio is a great, free data visualization tool from Google that lets you collect data in informative dashboards and reports that are easy to read, easy to share, and fully customizable. The data comes from various sources such as proprietary databases, Google Analytics, Google Search Console and social media platforms, and you can blend them without programming.
How To Create Semantic SEO Reports with WordLift
The Semantic Web has changed the way we approach web content. As we know, Google is changing the way it crawls web pages, focusing not just on keywords (which are still important) but on concepts and then entities.
Adding structured data to your website means that you are enriching your data with information that allows Google and the search engines to better understand what the content of your website is about. This way you can get better rankings, more organic traffic and provide users with a more relevant user’s experience.
So, with structured data, you can make a difference for your business. However, it is often very difficult to show the SEO structured data value and thus a semantic SEO strategy.
If you have WordLift, we have the solution for you! You can use the Looker Studio Connector to understand and show others the results you have achieved by working with entities on your website. Let’s go and see how it can help you implement your semantic SEO strategy.
Getting Started With WordLift for Google Looker Studio
With WordLift Looker Studio Connector, you can create Semantic SEO reports by loading data from your Knowledge Graph right into Looker Studio and blend it with Search Console or any other web analytics platform.
Just click on it and enter the WordLift key. And we have a GraphQL query ready for you, so you don’t need to do anything to get started. In case you are a power user and you know the query that you want to run, just continue. For example, if you are running an e-commerce website, maybe you want to query for product attributes or prices. Then be sure to keep checking the box “use report template for new report”, so you can get a shiny new report premade for you. Then click Connect. Here you can see the fields that come from the report. Finally, click Create Report!
At this point, you are close to creating your report, but two more steps are needed:
To go to the managed data sources and add your Search Console data source: choose your website, choose URL Impressions anche choose Web Type, and then click on Create.
Check the blends to verify that the data is merged from the Knowledge Graph and GSC.
Save and enjoy the report🤩 You can filter the data for EntityType and choose the period of time you prefer.
Benefits
If you use WordLift Looker Studio Connector for your semantic SEO strategy, you can have these benefits:
Everything in one place. You can create a single source of truth about your SEO and business performance.
Learn more about your audience. You get meaningful data about your content that helps you learn more about your customers and optimize your SEO strategy.
Useful data. You’ll be able to identify new queries and search intent to optimize your content.
Improve SEO reporting and gain new insights. Take your SEO reporting to the next level and easily gain valuable insights into your keyword rankings, traffic, and more.
Now you can focus on semantic web analytics and the advantage that you can gain in modern SEO without worrying about how to prove the benefits.
Discover more about how to create a Web Analytics Dashboard using Google Looker Studio, traffic data from Google Analytics, and WordLift, reading this article.
Learn how to create a Semantic SEO Report in 3 simple steps👇
Other Frequent Questions
Are Looker Studio connectors free?
As Google says, “You can build, deploy and distribute connectors for free. You and your users can use connectors in Data Studio for free.” In the case of WordLift’s Looker Studio connector, you can use it if you have an active subscription, because you need a key to create the reports.
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.
Adding structured data to your website means enriching your data with information that makes your content easier for Google and search engines to understand. This way, your website and the search engines can talk to each other, allowing you to have a richer representation of your content in Google’s SERPs and increase organic traffic. You’ll then get more clicks and growth for your business.
With structured data in modern SEO, you can create an impact, and this impact is measurable whether you have a large or small business.
Focus on the importance of structured data beyond numbers (clicks, impressions, etc) and the advantage that you can gain in modern SEO.
Much of the adoption we see of modern standards like schema.org (particularly via json-ld) appears to be motivated by organizations and individuals who wish to take advantage of search engines’ support (and rewards) for providing data about their pages and content but outside of this, there’s a rich landscape of people who use structured data to enrich their pages for other reasons. Web Almanac
So structured data is not just the data we prepare for Google; it’s data that helps you understand the meaning of web pages.
The Semantic Web has changed the way we approach web content. As Tim Berners Lee himself says, the world is not made of words but is made of something more powerful: data. This means that to improve search engines’ understanding of web content, it is necessary to have a high-quality dataset enriched with information in structured data.
Structured data allows Google and search engines to understand what you’re talking about on your website and rank better by returning users with enriched results in SERPs. In this way, users can find relevant information that better meets their search intent.
We talk about entities and no longer about keywords. They represent “concepts” and allow machines (Google and search engines, voice assistants, etc.) to interpret what we know about a person, organization, place, or anything described in a document.
In this scenario, Semantic Web Analytics is the use of named entities and linked vocabularies such as schema.org to analyze the traffic of a website.
With this type of analysis, you’ll start from your website’s structured data, and you’ll be able to cross-reference it with the data from Google Analytics, Google Search Console or your CRM. In this way, you’ll be able to learn more about your users/customers and their behaviors, gaining a strategic advantage over impression and traffic data alone. As we’ll see below, with just a few clicks, you can extract structured data from web pages and blend it, in Google Data Studio, with traffic from Google Analytics.
How To Use Structured Data For Semantic Analytics
It’s clear that structuring information goes beyond search engine support and can also provide value in web metrics analysis.
At this point, we show you how you can extract structured data from web pages and blend it with Google Analytics traffic in Google Data Studio. You’ll also see how this will allow you to gain insights into web analytics.
We start from a demo website that we built for demonstration purposes. If you have a small business, with a small number of products, you can crawl your content by using a Streamlit application. Otherwise, if you are at a more advanced level and you have a large number of products, you can use Colab, working with the SEO crawler of Advertools, the free library created by Elias Dabbas, available here. With this system, you can crawl hundreds of thousands of URLs. But it has a pitfall: it is not able to detect structured data that has been injected with javascript.
Then the data will be brought by the crawler in Google Sheets and blended in Google Data Studio in order to have one single view.
You can create a Data Studio Dashboard where you can select and see some specific insights. Here, for example, you can see the breakdown of the session in Google Analytics with the category. So we can see that clothing is accounting for 50% for the session.
How Do Blended Sources In Google Data Studio Work? Blending Data Is Simple.
As you can see in the image, you have tables (in our case, Google Sheets and Google Analytics) and a list of available fields that you can use from this table within the join to create a view of combined fields.
Then you have the join configuration; that is how you want to blend this data. You can decide to take everything from the left table that overlaps with the right table, or you want to look at the strict overlap of the inner.
Then you have the name of the blended source that you will create and the fields that you will represent inside this blended source which is a view on one, two or more tables combined by a unique key. In this example, the unique key is the URL.
You are using the URL on both sides to combine them and these allow you to look at the analytic, for instance the session, by looking at the category.
If you want to see something more advanced, you can blend the second Spreadsheet with Google Analytics. In this case, you have more data, such as the color and the brand name, and you can create a chart using the product category, the session, and the price. This way, you can see traffic for each product category and the price. You can also see the breakdown of the colors and the brands.
You can play with different combinations in order to have the right data. Extracting structured data from your web pages and blending it with Google Analytics data gives you a more precise and more accurate picture of your users’ behavior with just a few clicks. This is particularly useful to improve your marketing strategy and grow your business in a data-driven way.
Keep In Mind: Structured Data Has Its Pitfalls.
Structured data, when injected using Javascript, cannot be easily crawled;
If you want to know how to create a Web Analytics Dashboard using Google Data Studio, traffic data from Google Analytics, and WordLift, read this article.
Frequently Asked Questions
What is Semantic Web Analysis?
Semantic Web Analytics is the analysis of a website’s traffic done using named entities and related vocabularies such as schema.org.
With this analysis, you can start from the website’s structured data and cross-reference it with data from Google Analytics, Google Search Console, or other CRM. In this way, you can learn more about user and customer behavior and gain a competitive advantage beyond just analyzing impressions and traffic.
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”.
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