GenAI and the Future of Branding: The Crucial Role of the Knowledge Graph

The one thing that brand managers, company owners, SEOs, and marketers have in common is the desire to have a very strong brand because it’s a win-win for everyone. Nowadays, from an SEO perspective, having a strong brand allows you to do more than just dominate the SERP — it also means you can be part of chatbot answers.

Generative AI (GenAI) is the technology shaping chatbots, like Bard, Bingchat, ChatGPT, and search engines, like Bing and Google. GenAI is a conversational artificial intelligence (AI) that can create content at the click of a button (text, audio, and video). Both Bing and Google use GenAI in their search engines to improve their search engine answers, and both have a related chatbot (Bard and Bingchat). As a result of search engines using GenAI, brands need to start adapting their content to this technology, or else risk decreased online visibility and, ultimately, lower conversions.

As the saying goes, all that glitters is not gold. GenAI technology comes with a pitfall – hallucinations. Hallucinations are a phenomenon in which generative AI models provide responses that look authentic but are, in fact, fabricated. Hallucinations are a big problem that affects anybody using this technology.

One solution to this problem comes from another technology called a ‘Knowledge Graph.’ A Knowledge Graph is a type of database that stores information in graph format and is used to represent knowledge in a way that is easy for machines to understand and process.

Before delving further into this issue, it’s imperative to understand from a user perspective whether investing time and energy as a brand in adapting to GenAI makes sense.

Should my brand adapt to Generative AI?

To understand how GenAI can influence brands, the first step is to understand in which circumstances people use search engines and when they use chatbots.

As mentioned, both options use GenAI, but search engines still leave a bit of space for traditional results, while chatbots are entirely GenAI. Fabrice Canel brought information on how people use chatbots and search engines to marketers’ attention during Pubcon.

The image below demonstrates that when people know exactly what they want, they will use a search engine, whereas when people sort of know what they want, they will use chatbots. Now, let’s go a step further and apply this knowledge to search intent. We can assume that when a user has a navigational query, they would use search engines (Google/Bing), and when they have a commercial investigation query, they would typically ask a chatbot.

Image source: Type of intent/Pubcon Fabrice Canel

The information above comes with some significant consequences:

1. When users write a brand or product name into a search engine, you want your business to dominate the SERP. You want the complete package: GenAI experience (that pushes the user to the buying step of a funnel), your website ranking, a knowledge panel, a Twitter Card, maybe Wikipedia, top stories, videos, and everything else that can be on the SERP.

Aleyda Solis on Twitter showed what the GenAI experience looks like for the term “nike sneakers”:

2. When users ask chatbots questions, they typically want their brand to be listed in the answers. For example, if you are Nike and a user goes to Bard and writes “best sneakers”, you will want your brand/product to be there.

3. When you ask a chatbot a question, related answers are given at the end of the original answer. Those questions are important to note, as they often help push users down your sales funnel or provide clarification to questions regarding your product or brand. As a consequence, you want to be able to control the related questions that the chatbot proposes.

Now that we know why brands should make an effort to adapt, it’s time to look at the issues that this technology brings before diving into solutions and what brands should do to ensure success.

What are the pitfalls of Generative AI?

The academic paper Unifying Large Language Models and Knowledge Graphs: A Roadmap extensively explains the problems of GenAI. However, before starting, let’s clarify the difference between Generative AI, Large Language Models (LLMs), Bard (Google chatbot), and Language Models for Dialogue Applications (LaMDA).

LLMs are a type of GenAI model that predicts the “next word,” Bard is a specific LLM chatbot developed by Google AI, and LaMDA is an LLM that is specifically designed for dialogue applications.

To make it clear, Bard was based initially on LaMDA (now on PaLM), but that doesn’t mean that all Bard’s answers were coming just from LamDA. If you want to learn more about GenAI, you can take Google's introductory course on Generative AI.

As explained in the previous paragraph, LLM predicts the next word. This is based on probability. Let’s look at the image below, which shows an example from the Google video What are Large Language Models (LLMs)?

Considering the sentence that was written, it predicts the highest chance of the next word. Another option could have been the garden was full of beautiful “butterflies.” However, the model estimated that “flowers” had the highest probability. So it selected “flowers.”

Image source: YouTube: What Are Large Language Models (LLMs)?

Let’s come back to the main point here, the pitfall.

The pitfalls can be summarized in three points according to the paper Unifying Large Language Models and Knowledge Graphs: A Roadmap:

1. “Despite their success in many applications, LLMs have been criticized for their lack of factual knowledge.” What this means is that the machine can’t recall facts. As a result, it will invent an answer. This is a hallucination.
   
   

2. “As black-box models, LLMs are also criticized for lacking interpretability. LLMs represent knowledge implicitly in their parameters. It is difficult to interpret or validate the knowledge obtained by LLMs.” This means that, as a human, we don’t know how the machine arrived at a conclusion/decision because it used probability.
   
   

3. “LLMs trained on general corpus might not be able to generalize well to specific domains or new knowledge due to the lack of domain-specific knowledge or new training data.” If a machine is trained in the luxury domain, for example, it will not be adapted to the medical domain.

The repercussions of these problems for brands is that chatbots could invent information about your brand that is not real. They could potentially say that a brand was rebranded, invent information about a product that a brand does not sell, and much more. As a result, it’s good practice to test chatbots with everything brand-related.

This is not just a problem for brands but also for Google and Bing, so they have to find a solution. The solution comes from the Knowledge Graph.

What is a Knowledge Graph?

One of the most famous Knowledge Graphs in SEO is the Google Knowledge Graph, and Google defines it: “Our database of billions of facts about people, places, and things. The Knowledge Graph allows us to answer factual questions such as ‘How tall is the Eiffel Tower?’ or ‘Where were the 2016 Summer Olympics held?’ Our goal with the Knowledge Graph is for our systems to discover and surface publicly known, factual information when it’s determined to be useful.”

The two key pieces of information to keep in mind in this definition are:

1. It’s a database

2. That stores factual information

This is precisely the opposite of GenAI. Consequently, the solution to solving any of the previously mentioned problems, and especially hallucinations, is to use the Knowledge Graph to verify the information coming from GenAI.

Obviously, this looks very easy in theory, but it’s not in practice. This is because the two technologies are very different. However, in the paper ‘LaMDA: Language Models for Dialog Applications,’ it looks like Google is already doing this. Naturally, if Google is doing this, we could also expect Bing to be doing the same.

The Knowledge Graph has gained even more value for brands because now the information is verified using the Knowledge Graph, meaning that you want your brand to be in the Knowledge Graph.

What a brand in the Knowledge Graph would look like

To be in the Knowledge Graph, a brand needs to be an entity. A machine is a machine; it can’t understand a brand as a human would. This is where the concept of entity comes in.

We could simplify the concept by saying an entity is a name that has a number assigned to it and which can be read by the machine. For instance, I like luxury watches; I could spend hours just looking at them.

So let’s take a famous luxury watch brand that most of you probably know — Rolex. Rolex’s machine-readable ID for the Google knowledge graph is /m/023_fz. That means that when we go to a search engine, and write the brand name “Rolex”, the machine transforms this into /m/023_fz.

Now that you understand what an entity is, let’s use a more technical definition given by Krisztian Balog in the book Entity-Oriented Search: “An entity is a uniquely identifiable object or thing, characterized by its name(s), type(s), attributes, and relationships to other entities.”

Let’s break down this definition using the Rolex example:

• Unique identifier = This is the entity; ID: /m/023_fz

• Name = Rolex

• Type = This makes reference to the semantic classification, in this case ‘Thing, Organization, Corporation.’

• Attributes = These are the characteristics of the entity, such as when the company was founded, its headquarters, and more. In the case of Rolex, the company was founded in 1905 and is headquartered in Geneva.

All this information (and much more) related to Rolex will be stored in the Knowledge Graph. However, the magic part of the Knowledge Graph is the connections between entities.

For example, the owner of Rolex, Hans Wilsdorf, is also an entity, and he was born in Kulmbach, which is also an entity. So, now we can see some connections in the Knowledge Graph. And these connections go on and on. However, for our example, we will take just three entities, i.e., Rolex, Hans Wilsdorf, Kulmbach.

From these connections, we can see how important it is for a brand to become an entity and to provide the machine with all relevant information, which will be expanded on in the section “How can a brand maximize its chances of being on a chatbot or being part of the GenAI experience?”

However, first let’s analyze LaMDA , the old Google Large Language Model used on BARD, to understand how GenAI and the Knowledge Graph work together.

LaMDA and the Knowledge Graph

I recently spoke to Professor Shirui Pan from Griffith University, who was the leading professor for the paper “Unifying Large Language Models and Knowledge Graphs: A Roadmap,” and confirmed that he also believes that Google is using the Knowledge Graph to verify information.

For instance, he pointed me to this sentence in the document LaMDA: Language Models for Dialog Applications:

“We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.”

I won’t go into detail about safety and grounding, but in short, safety implies that the model respects human values and grounding (which is the most important thing for brands), meaning that the model should consult external knowledge sources (an information retrieval system, a language translator, and a calculator).

Below is an example of how the process works. It’s possible to see from the image below that the Green box is the output from the information retrieval system tool. TS stands for toolset. Google created a toolset that expects a string (a sequence of characters) as inputs and outputs a number, a translation, or some kind of factual information. In the paper LaMDA: Language Models for Dialog Applications, there are some clarifying examples: the calculator takes “135+7721” and outputs a list containing [“7856”].

Similarly, the translator can take “Hello in French” and output [“Bonjour”]. Finally, the information retrieval system can take “How old is Rafael Nadal?” and output [“Rafael Nadal / Age / 35”]. The response “Rafael Nadal / Age / 35” is a typical response we can get from a Knowledge Graph. As a result, it’s possible to deduce that Google uses its Knowledge Graph to verify the information.

Image source: LaMDA: Large Language Models for Dialog Applications

This brings me to the conclusion that I had already anticipated: being in the Knowledge Graph is becoming increasingly important for brands. Not only to have a rich SERP experience with a Knowledge Panel but also for new and emerging technologies. This gives Google and Bing yet another reason to present your brand instead of a competitor.

How can a brand maximize its chances of being part of a chatbot’s answers or being part of the GenAI experience?

In my opinion, one of the best approaches is to use the Kalicube process created by Jason Barnard, which is based on three steps: Understanding, Credibility, and Deliverability. I recently co-authored a white paper with Jason on content creation for GenAI; below is a summary of the three steps.

1. Understand your solution. This makes reference to becoming an entity and explaining to the machine who you are and what you do. As a brand, you need to make sure that Google or Bing have an understanding of your brand, including its identity, offerings, and target audience. In practice, this means having a machine-readable ID and feeding the machine with the right information about your brand and ecosystem. Remember the Rolex example where we concluded that the Rolex readable ID is /m/023_fz. This step is fundamental.

2. In the Kalicube process, credibility is another word for the more complex concept of E-E-A-T. This means that if you create content, you need to demonstrate Experience, Expertise, Authoritativeness, and Trustworthiness in the subject of the content piece.

A simple way of being perceived as more credible by a machine is by including data or information that can be verified on your website. For instance, if a brand has existed for 50 years, it could write on its website “We’ve been in business for 50 years.” This information is precious but needs to be verified by Google or Bing. Here is where external sources come in handy. In the Kalicube process, this is called corroborating the sources. For example, if you have a Wikipedia page with the date of founding of the company, this information can be verified. This can be applied to all contexts.

If we take an e-commerce business with client reviews on its website, and the client reviews are excellent, but there is nothing confirming this externally, then it’s a bit suspicious. But, if the internal reviews are the same as the ones on Trustpilot, for example, the brand gains credibility!

So, the key to credibility is to provide information on your website first, and that information to be corroborated externally.

The interesting part is that all this generates a cycle because by working on convincing search engines of your credibility both onsite and offsite, you will also convince your audience from the top to the bottom of your acquisition funnel.

3. The content you create needs to be deliverable. Deliverability aims to provide an excellent customer experience for each touchpoint of the buyer decision journey. This is primarily about producing targeted content in the correct format and secondly about the technical side of the website.

An excellent starting point is using the Pedowitz Group's Customer Journey model and to produce content for each step. Let’s look at an example of a funnel on BingChat that, as a brand, you want to control.

A user could write: “Can I dive with luxury watches?” As we can see from the image below, a recommended follow-up question suggested by the chatbot is “Which are some good diving watches?”

If a user clicks on that question, they get a list of luxury diving watches. As you can imagine, if you sell diving watches, you want to be included on the list.

In a few clicks, the chatbot has brought a user from a general question to a potential list of watches that they could buy.

As a brand, you need to produce content for all the touchpoints of the buyer decision journey and figure out the most effective way to produce this content, whether it’s in the form of FAQs, how-tos, white papers, blogs, or anything else.

GenAI is a powerful technology that comes with its strengths and weaknesses. One of the main challenges brands face is hallucinations when it comes to using this technology. As demonstrated by the paper LaMDA: Language Models for Dialog Applications, a possible solution to this problem is using Knowledge Graphs to verify GenAI outputs. Being in the Google Knowledge Graph for a brand is much more than having the opportunity to have a much richer SERP. It also provides an opportunity to maximize their chances of being on Google’s new GenAI experience and chatbots — ensuring that the answers regarding their brand are accurate.

This is why, from a brand perspective, being an entity and being understood by Google and Bing is a must and no more a should!

How to Repurpose Your Old Content – Whiteboard Friday

Breathe new life into your existing content. Join Ross Simmonds in this Whiteboard Friday as he shares strategies to transform your old content into valuable assets that drive results and boost brand authority.

Click on the whiteboard image above to open a high resolution version in a new tab!

Video Transcription

Howdy, Moz fans. Howdy, "Whiteboard Friday" fans.

I am super excited to chat with you today about how to take something that we all have, you might smell it, you might smell it, it is content that is burning money in your pocket, content that you actually invested in that is collecting dust, content that no longer drives results, no longer does anything for your business, but you wish, you wish it could bring some type of life for your business, some type of joy to your business, some type of ROI for your business. And in today's "Whiteboard Friday," that's exactly what we're gonna be diving into.

Your old content is burning money

Were gonna be talking about how that content that is just burning money, just sitting on your site, not collecting any revenue, not driving any sales, driving no links, driving no results for your business, can be brought back to life. That's right, we're going to go to town on that old content, give it new life, bring it back, and ultimately give you the ability to unlock new levels of growth for content that you thought was dead.

Let's jump into it. So we're all there. We have all had content that we've invested in. We've all heard a guru speak onstage at a conference and say, "Content is king. Write more content. Create more content." And we've listened. We've developed tons of content as business owners, as marketers, as CMOs. We've invested in these assets, but the ROI of some of them has started to deprecate. It's started to go down.

At one point, it was up and to the right and everything looked beautiful, but now, eh, it's starting to tank. It is starting to tank. It's no longer driving results. The traffic is no longer there. It's no longer on page one of the SERP. You are starting to see that content that at one point was a cash cow is no longer driving the results that you and your business wants. How can you bring these pieces back to life? We're gonna talk about it.

In addition, you might've had a few pieces that were a one-hit wonder. When they hit the internet, when they went live, the internet went wild. The leads were flowing. The revenue was flowing. The links were flowing. Everything was flowing in the right direction. But time went on and those pieces that were at one point a hit, that resonated with people, that people embraced, that people shared, that people talked about, those pieces are no longer generating any buzz for you. They're no longer generating any results for you. We're gonna talk about how to bring those one-hit wonders back to life.

And finally, there might be a piece that you, your team, your organization created early on that really communicated some of the key messages that you want your audience to hear, some of the key stories, some of the key ideas, some of the key concepts that truly understand and communicate the value that you bring to the market, the pain points that your customers are having. And these key messages are messages that you want to get back out there. It's a key message that at one point resonated so closely with your audience that you feel like your team may have lost the way and have lost sight of the key message that you wanted to articulate.

How do you bring all of these pieces back to life? Things that are currently just getting zzzs, people are sleeping on them, they're not reading them, they're not engaging with them. It's essentially money just being burned because you invested time or energy in them. How do you bring 'em back to life? That's what we're gonna talk about.

We're gonna talk about how you can do that with text content, with video content, and with audio content. So these are the three primary types of content that you can repurpose, that you can repackage, on the internet.

Text content falls into the category of things like blog posts, of white papers, of e-books, resources that you created that, again, were very valuable at the time that they went live, but maybe got crickets, maybe were a one-hit wonder, or maybe they were ranking number one and now they're starting to tank. How do you bring those pieces back to life?

There's a few things that you can do with text content that will let you win. Similarly with video content. One of my favorite formats is video content because it's so versatile. That's exactly what you're looking at now. Kinda meta, but you're looking at a video asset. These video assets can be used still today. A webinar that you might've created, a actually just talking head video where you set up a camera and you talked into the screen. All of these types of assets can be repurposed. They can be remixed, and they can be reshared on your various channels. We're gonna talk about that.

Audio content is the exact same thing. There is less versatility with audio content, but audio content is powerful. But it's oftentimes misunderstood that these podcasts that you might've created two years ago, three years ago, can be repackaged even today.

How to bring your text content back to life

So let's start with text-based content. Text-based content is one of the fundamentals of the internet. When you were thinking about text-based content, let's say you have a blog post. Do you just press publish and call it a day? No, you need to take that old blog post that you created two years ago and then start to share it on social. And you do that with social media posts.

So you are going to start putting up posts on social directly on your various channels, whether it's LinkedIn, whether it's X, whether it's Facebook, you name it. Whatever channel it is that you're using, you're going to take that asset and you're gonna promote it. You're gonna amplify it. You're gonna share it on those channels. Then, we have things like carousels. Carousels are a very engaging way to tell a story about a piece of content that you created but tell it in a way that the user, the audience, the people who are on these channels, might actually get value from. What do I mean by that?

So let's say, for example, you have a blog post that are five simple things that every SEO needs to know to succeed. Every single one of those slides in a carousel could represent one of those five ideas. So as someone scrolls and they start to swipe their thumb, as they go from one carousel screen to the next, they're reading a summarized version of that blog post that you created. And when you start to share those on social and use hashtags that are relevant to your ICP, it gives you the ability to now take an old asset that was burning hole in your pocket, not generating results, but give it the ability to reach new people, to reach an audience that might not have been online at the same time when you first published that piece, but also have a personal connection to more interactive content and they want to engage via a carousel. You can use tools like Canva to be able to do this, and it is an amazing tool that I strongly recommend that you check out.

Email content. All those old blog posts, a lot of brands make the mistake of pressing publish on these pieces, and then, they just call it a day. They don't go anywhere. You have people who are currently on your site probably downloading things, signing up for things. Use your email sequences to reshare these old blog posts that you created that are so important to you. Because by doing that, you're able to connect with people who are meeting you and interacting with you for the very first time. And you can bring that old content, especially if it's a one-hit wonder, and bring it back to life for your audience to love, care about, and see again.

How to bring your video content back to life

Now, another type of asset is video content. If you have blog posts, if you have e-books, if you have content that you've developed from the text lens, why don't you take that same text and turn it into a script? Turn it into a script that you can read on camera. Turn it into a script that is worth reading, worth talking about, and if you are personally not like in the whole idea of wanting to be on camera and that kind of weirds you out, then instead, maybe you do a faceless video where it's just your audio with animations in the background. But video content is an amazing play for you to repurpose, repackage some of those old text pieces that you developed and then bring them back to life.

What do you think the second most popular search engine in the world is? It's YouTube. YouTube is going to give you the ability to now take those blog posts that you created, that you hoped would rank in Google, and now rank in the second most popular search engine in the world. And it just so happens that Google owns YouTube, so eh, you can win on both sites. That is the play. Now, that's text content. When you get that video asset created, if you create it and you develop it, now you're into a trifecta of situation. Because you can now take that video content that you developed and you can turn it into a whole bunch of new content as well.

What do I mean? So that video content asset that you just created, a new video, new script that you just recorded, it's amazing. Take the audio file, extract it out of the video, and start to upload that thing to Apple Podcasts, upload it to Spotify, upload it to all of the various podcasting platforms, and let it sing. Let it reach new audiences. Optimize that for search on the podcast platform and let more people hear the audio that was in your video on these different channels.

Now, you now have two different assets. You have a podcast episode. You have that, and you have the video. Take these assets and embed them into blog posts that you have produced. That same blog post that you wrote a few years ago, update the dates, add new info, and now embed either the YouTube video that you created or the podcast audio that you created directly into that piece. And now, you have a more interactive experience for the user and the reader who is consuming that content, right? Then, once you've done that, you should be looking at pages on your site that are already ranking high.

If you have pieces of content that are ranking well, that are generating organic traffic, take these video assets, take these podcasts, embed them in these pieces so those pieces get additional reach, get additional eyeballs. And it ultimately will influence your ranking in the Spotifys, in the Apples, and in YouTube itself.

Now, if you've got video content and you are starting from ground zero with video, let's say you did not embrace the idea of going from text to video, you just happen to have video content, because over the years, again, prior to it being set on fire with just like no results, you were investing in YouTube. Let's say you were investing in webinars. A lot of brands create webinars. They put them behind a gate. They get a bunch of signups and then, they never actually use them again. Take those video assets that you've created and run this exact same playbook, folks. Run the exact same playbook. Take those old webinars, turn them into podcasts, start embedding them into blog posts, or write long-form content based off of the material within that video. Take that, an export of the transcript from the video that you just created or that you created years ago, and start to transcribe it. Rewrite all of that into content in a way that is human, friendly, people would love, people would adore, and get them to consume it.

Then, the next thing you wanna do is start chopping up that video. You wanna look for key moments within the video that you developed years ago, or basically, right off of that text, you wanna take certain clips from that and start to repackage and repurpose them. Identify a few key moments where you're saying something special, saying something impactful and important, and then clip it and then start sharing that on social. And the whole cycle begins again.

How to bring your audio content back to life

Now, another type of content that comes out of this that we talked about was podcasts, audio content. Take clips of those audio, those parts of your video, and start to chop them up into audiograms. An audiogram is essentially a visual that shows the voice wave. So again, if you're not somebody who likes to be on camera, it's a great alternative. And start distributing those on social. Share them on all of your platforms. Share them out, hashtag, let people consume them, engage. Also, turn those podcasts into long-form text, and then, again, embed them into your blog posts.

Folks, this is the playbook you need to apply to these pieces of content that you invested so much time and energy into. And if you can, run through these sequences and bring them back to life, update them, improve them, and start to repurpose them.

Gain greater return on investment, increase your brand authority, and skyrocket

You will be able to see new ROI, new revenue, new results from content that you created decades ago. And when you start to see this, you're ultimately going to start to see an increase in the amount of people talking about your brand. You're gonna see an increase in the amount of people interested in buying your solution, your product, your service, whatever it may be. And from this, by having a content engine that doesn't let content just collect dust and get on fire, you're also gonna have the ability to increase your BA.

Your brand authority in the market is going to have the ability to start to rise, right? That is the ultimate goal. You want to increase your brand authority, because if you can do that, then you are ultimately going to have net new leads, net new opportunities, and you're gonna set yourself up for long-term success, which is the game that all of us should be playing. And when you do that, you're gonna essentially set yourself up as a rocket ship. You're gonna be heading to the Moon. You are no longer going to be trying to grab scraps, and you will no longer be in the rat race of always trying to figure out what content you should create next. Because the content that you should be optimizing, the content that you should be producing has already probably been created.

The next step is simple: go back, do an audit, identify your one-hit wonders, identify the depreciating ROI assets that you invested in before, identify the key messages that you wanna reiterate to your audience. And once you have those, start to run them through this engine and give your content new life. I'm Ross Simmonds. Thank you so much for checking out my "Whiteboard Friday." I hope you enjoyed it and I will see you on the internet. 

How to Make AI Your Writing Sidekick for Content Marketing

Artificial Intelligence (AI) has blown up like a supernova. Conversations about AI are happening everywhere. As you browse LinkedIn, read your favorite SEO blog, or tune into a marketing podcast, you will see discussions about AI.

Some marketers see AI as a threat. But that doesn’t have to be the case. You can learn how to work with AI. Building AI into your processes will future-proof your marketing skills if AI takes over the world. As a freelance copywriter and content marketer, I’ve seen ‘I, Robot,’ and I’m not taking any chances.

AI can be your marketing sidekick. You can use AI to support your writing process, be a fresh pair of eyes for proofreading, or act as a second brain. This makes it a powerful companion for content marketing.

AI won’t replace content marketers (yet)

AI opens up a universe of opportunities. As new AI tools emerge every day, so do new ways of using AI in your business and everyday life. You can use AI to do almost anything.

But that doesn’t mean AI will replace content marketers, SEOs, or writers — or, at least, won’t replace them yet.

AI has a lot of potential. But it also has limitations. Before using AI for content marketing, it’s helpful to get to grips with the constraints of AI.

It isn’t sentient

AI doesn’t have a brain like you or I do. It can’t think or feel.

AI’s knowledge is based solely on logic. In contrast, our knowledge is based on a mix of reason and emotion. Our experiences and feelings shape how we see things. But AI doesn’t have that ability.

AI might feign human emotion, but it’s merely an illusion. AI’s emotion is curated. AI can only perceive things based on the information it is fed.

You can’t rely on it to be accurate

Sure, AI is powerful. But it isn’t always accurate. Just like humans, AI sometimes gets things wrong.

When using ChatGPT, you will see a disclaimer stating, “ChatGPT may produce inaccurate information about people, places, or facts.” While AI tools do their best to provide factually accurate information, they still need a human touch.

If you’re using AI, fact-check the information it gives you. This is particularly important when writing YMYL (Your Money or Your Life) content that has real-world effects on the readers. In these cases, a human touch is crucial for ensuring E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) runs through your content. AI cannot replicate this, running the risk of your content being inaccurate and low quality.

The output is limited by the input

Getting the best possible results from AI depends on feeding it the best possible information.

It’s like asking someone to design you a logo without giving them the exact details about what you want the logo to look like. They might get it right. But it is more likely that they will get it wrong — and you will end up in a back-and-forth ping-pong match of reiterating the design.

You need to tell AI exactly what you want. Otherwise, it’s going to hazard a guess (and likely get it wrong). Be specific in your request — like really specific. Want AI to give you ten content ideas for a blog post on programmatic SEO? Tell it! Want to make sure those results are all about the SaaS industry? Tell it! The more specific your generative AI prompt is, the better the output will be.

The responses can feel canned

You can get AI to write your landing pages, blog articles, and ad copy. But you might not want to.

Remember how I said AI isn’t sentient? And that the output is limited by the input? Well, both of those factors also contribute to the responses feeling somewhat meh.

AI responses can feel canned. They lack the originality and personality you get from a human writer who knows your brand inside and out.

With that said, you can train some AI tools, such as ChatGPT, to produce content that mirrors your brand’s voice, personality, and tone. You can do this by sharing examples of your writing style, providing background information on your company, and feeding it your tone of voice guidelines. But I’ll share more on that later in the article.

Another attribute that limits the creativity of AI outputs is that it doesn’t automatically consider the user journey stage, their current motivations or desires, or the exact words that drive action for them. AI doesn’t know your deeper brand origins or future goals — unless you train it on them.

All that adds up to creating generic content anyone could write. Use AI enough times, and you will notice familiar patterns in the responses. It sounds like AI.

But AI can be a powerful content marketing sidekick

It pays to be aware of the shortfalls of AI before you add it to your content marketing process. As we’ve established, AI isn’t perfect. But it can still be useful.

You can use AI to optimize the research, writing, and editing phases of content marketing.

You might not be able to rely on AI alone. But you can pair it with a human touch to turn it into a powerful content marketing sidekick.

Here are some ways you can use AI to optimize and streamline various aspects of your content marketing process.

How to use AI in the research phase of content marketing

The research phase of a content marketing strategy can be time-consuming. While every company’s strategy is different, the core elements will follow a similar pattern.

At some point during on-site content planning, you will need to find content ideas, conduct keyword research, and create outlines for blog posts. There are a few ways you can use AI to simplify these content marketing research and planning steps.

Use AI to find content ideas

In its most basic form, the content you create for your brand should focus on two things:

• What you do

• Who you do it for

We can get more nuanced than that by layering in factors like goals, campaigns, and user intent. But most content will cover topics that are related to your business (what you do) and of interest to your audience (who you do it for).

You can use AI to develop content ideas for a wide variety of marketing channels — be it social media, newsletters, blogs, or videos. For the examples shared throughout this article, I’ve used ChatGPT.

A simple AI prompt you can use to find content ideas is:

Write a list of 10 [content type] ideas aimed at [audience] who are [problem they are trying to solve/goal they want to achieve]

In this prompt, the content type should relate to the channel you are creating content for, such as a blog. The audience will align with your target audience. The problem or goal should relate to what your brand does.

For a dog training school, the prompt might look like this:

Write a list of 10 blog post ideas aimed at new dog owners who are trying to train their puppy.

If you put this prompt into Chat-GPT, you will be presented with a list of potential blog post ideas such as “puppy separation anxiety: tips and tricks” or “10 essential commands every puppy should know.”

Work through this list to decide if any of those ideas are viable blog posts. Some might be perfect as they are, while others might need fine-tuning to make sure they align with your content marketing strategy.

Create a hub-and-spoke strategy from those content ideas

Drill down further into each idea to create the bones of a hub-and-spoke strategy. For each idea presented, note what the topic is.

For the blog post idea "10 essential commands every puppy should know,” you can confidently assume the focal topic is “puppy training commands.”

There is likely an array of blog posts you can write about puppy training commands, making this a potentially powerful hub topic for your hub-and-spoke strategy. Rather than doing lots of manual research to find suitable spoke blog post ideas, you can use AI to speed up the process.

Use the following AI generative prompt to ask Chat-GPT for spoke content ideas based on the hub topic:

I am writing a series of [content type] for a [business type] aimed at [target audience].

For the hub topic “[hub topic],” create a table of spoke content ideas.

Applying this to the puppy training commands keyword, the prompt would look like this:

I am writing a series of blog posts for a puppy training school aimed at new dog owners.

For the hub topic "puppy training commands," create a table of spoke content ideas.

In response to this prompt, Chat-GPT generates a table of more specific blog post ideas related to the hub topic “puppy training commands.” This will help you spin out a series of valuable content that strengthens your content visibility for this topic.

Use AI for keyword research

Love it or hate it — keyword research is essential to developing on-site content strategies. It tells you what topics people care about, how popular those terms are, and how difficult it might be to rank in SERPs.

Generic AI tools can’t do in-depth keyword research, so you should still use a keyword research tool like Moz’s Keyword Explorer. However AI can help with the early stages of keyword research.

If you’re starting keyword research from scratch, use AI to generate a list of keywords. Take this a step further by asking the AI tool to group those keywords by search intent. This prompt will look something like this:

Create a list of keywords for [business type/topic]. Present the keywords in a table grouped by search intent.

Sticking with our dog training school example, the prompt would be:

Create a list of keywords for a dog training school. Present the keywords in a table grouped by search intent.

You’ll then be presented with a neat table of keywords you might be able to use. The keywords likely won’t be perfect, but it’s a great starting point.

Go through the list and research each keyword in Moz’s keyword research tool to get more data, including metrics such as Difficulty and Monthly Volume. You can then use your keyword research to see which keywords you do (or don’t) want to use and find other viable keywords.

Using AI for keyword research is great for getting the cogs turning, taking you from a blank page to a list of keywords. Grouping by search intent lets you take that rough keyword research one step further, making sure you’re covering various user journey stages.

Use AI to write blog post outlines

Okay, by this point, you should have a bunch of target keywords and blog post ideas. You’re almost ready to start putting pen to paper. But before you write your blog post, you need to plan a first draft.

You can use AI to help you write the first draft of blog post outlines. AI-generated first drafts won’t be groundbreaking. You will need to cast your eyes over it and optimize it with your amazing brand, customer, and market insights and knowledge.

While they aren’t perfect, they give you a good blog post template you can then tweak and improve.

Input this AI prompt to generate blog post outlines:

Write a blog post outline for a blog post titled "[blog post title].”

The main keyword for this blog post is "[main keyword]"

You guessed it, we’re sticking with the puppy training example! Based on the puppy training research we’ve done so far, the AI prompt might look like this:

Write a blog post outline for a blog post titled "10 essential commands every puppy should know.”

The main keyword for this blog post is "puppy commands"

Chat-GPT will then give you a blog post outline split into sections, including the introduction, main content sections, and a conclusion.

As this post focuses on the “10 essential commands,” Chat-GPT has given a listicle-style blog post outline. It has also shared a list of additional resources at the end you might want to reference in the post.

You can then review the outline that Chat-GPT has provided and apply your topic knowledge, adapt based on your experiences, and carry out additional research to improve the outline before you start writing the article.

How to use AI in the writing phase of content marketing

The sidekick powers of AI don't stop when you start writing. When you’re writing content, the process will be predominantly human-led. Your tone of voice matters. As do your personal experiences, opinions, and insights. Those are the things that make your content unique and that AI can’t do very well. So don’t replace your uniqueness with generic AI content.

You can, however, use AI as a sub-writer to assist with the writing phase of content marketing projects.

Use AI to adapt your tone of voice

If you’re writing content and want to make sure it has a particular voice, you can use AI to tweak the tone of your writing. This can help make sure your content aligns with your brand voice and the page intent.

If you’re writing content for a sales landing page, you will want that content to sound more persuasive than an educational blog post. AI can help you adapt your content to make it better suit its intended purpose.

Use AI to adapt your content’s tone of voice by using generative AI prompts such as:

• Make the following text sound more [adjective]

• Improve the following text by using a [tone of voice type] tone of voice

For a sales landing page, these prompts might look like this:

• Make the following text sound more persuasive and encouraging

• Improve the following text by using a persuasive and confident tone of voice

Remember, the output from AI depends on the input. You can improve these prompts by providing more details. You could, for example, ask AI to use metaphors or similes in its version or to use simple vocabulary and short, snappy sentences.

You could even tell it your brand values to make sure those are taken into consideration for tone of voice content.

AI doesn’t always get the tone of voice right. Often, it will exaggerate the voice. Whatever AI provides, you will need to edit manually. But it can be great for finding inspiration and unraveling the thread for words you could use or changes you could make.

I never copy and paste the output given by AI. Instead, I’ll look for words, sentences, or content styles that stand out to me. I’ll then refine my content manually based on any learnings gleaned from the AI version.

Starting the paragraph with the word “Enroll,” for example, encourages action and lets the reader know the intention of the content. This is far more persuasive than the softer, descriptive opening line “In these classes…” used in the original content.

This feature is far from perfect, but it can be useful if you’re experiencing a classic case of writer’s block.

Use AI to generate headlines

The headline is one of the most crucial elements of a blog post. A compelling, well-written headline gets clicks. It drives traffic to your blog post and gets more eyes on your content.

Yet, headlines can be the hardest thing to write. Do a quick Google search, and you will find hundreds of formulas for writing compelling headlines or tools to improve your existing headlines.

When writing headlines, you should account for your target keywords and the intent of the content. If you’re writing an educational blog post that teaches readers how to do something, you will likely use phrases like “how to.”

The only issue is that having an endless list of “how to” articles on your blog starts to look boring and repetitive. You can mix this up by asking AI to generate potential headlines for your blog articles.

Use the following prompt to get AI to generate headlines for your blog posts:

Write a list of 10 potential headlines for a blog post about [blog post topic]. This blog post [brief description of blog post].

Using our puppy training school example, this prompt might look something like this:

Write a list of 10 potential headlines for a blog post about puppy commands. This blog post shares 10 essential commands any new puppy owner should know when training their puppy.

As this example is a listicle, it makes sense that Chat-GPT includes the number in the headline. However, you can see a variety of headlines you could potentially use that sound more compelling than just “10 essential commands every puppy should know.”

The headlines also use power words like “must-know” and “ultimate” to entice readers to click through to the article. Meanwhile, words and phrases like “learn,” “teaching,” and “training 101” let the reader know the article has an educational intent.

These outputs offer a great first draft of some potential headlines. You can then fine-tune these headlines using well-known headline best practices.

This prompt doesn’t have to just be used for blog articles. You can adapt it for YouTube video headlines or email subject lines.

You can also use AI to generate headline formulas that you can use when writing headlines in the future. Do this by using a prompt such as:

Write 10 headline formulas that can be used to write blog post headlines. Display these as a table and make sure they cover a variety of content types such as [types of blog posts you typically share]

As always, you can drill down further into this by asking AI to prepare several headlines for each article type. You can then tweak the formulas and keep these on hand next time you’re writing an article.

Use AI to summarize your writing

Every blog post should end with a conclusion that gives the TL;DR (too long; didn’t read) low down of the article.

Typically, your conclusion should summarize the article by sharing the key takeaways. It should succinctly wrap up the article, leaving a lasting impression on the reader.

You might also want to use the conclusion to encourage readers to take action. This might include prompting them to read related articles, encouraging them to get in touch, or directing them to additional resources.

If you’re using ChatGPT-4, you can input longer content. This makes it great for asking ChatGPT to summarize long-form blog articles. You can also use AI tools that have specifically designed conclusion generation features, such as WriteMe or LongShot.

Use the following AI prompt to get AI to summarize your content:

Summarize the following content and provide the key takeaways. This will be used to write the conclusion for the blog post about [brief description of the blog post]

For our puppy training school example, this prompt will look like this:

Summarize the following content and provide the key takeaways. This will be used to write the conclusion for the blog post about the 10 essential commands any new puppy owner should know when training their puppy.

In this example, I only shared a snippet of a blog post to give you an idea of how this might look.

You can even take this prompt a step further by asking Chat-GPT to provide the key takeaways as a bullet-pointed list.

This quickly transforms a long-form article into a scannable list of key takeaways. You can then use this output to get a quick overview of the blog article so you can easily write your conclusion.

Use AI to write meta-descriptions

While they might only be short, writing high-quality meta descriptions requires skill. The right meta description can be the deciding factor in whether someone clicks through to your website or not.

Luckily, you can use AI to distill your on-page content into a couple of short sentences you can use as a meta description.

You can get ChatGPT to write meta descriptions by using the following AI prompt:

Summarize the following content into a 2 sentence meta-description

The optimal length for a meta description is between 50 to 160 characters. Run the AI-generated output through a word count checker to ensure it’s a suitable length. If not, you can either ask AI to generate a shorter version, or you can edit it manually.

In the above example, ChatGPT generated a meta description that was 195 characters long. As this is slightly longer than desired, I inputted the prompt, “can you make it slightly shorter?”

In response, ChatGPT cut the original output down to 145 characters while maintaining the key takeaways of the article.

How to use AI in the editing phase of content marketing

Before any content sees the light of day, it needs to go through a couple of rounds of revision. These revisions will either be conducted by the original writer or by editors, proofreaders, or content managers.

But reviewing your own content is hard. You’re too close to it. You know it inside out. I like to give myself one day between writing my first draft and proofreading it. This makes sure I’m looking at my content with fresh eyes.

But you don’t have to edit or proofread your content alone. Your AI content writing sidekick can come in useful during the editing phase of content marketing.

Use AI to clarify your writing

If you’re writing about complex topics, your content can easily become overwhelming for the reader. It might be laden with jargon or use niche analogies that only a small subset of people understand. The trouble with this is that complex content runs the risk of alienating your reader.

On the flip side, being too creative with your writing can also have a negative impact. Perhaps you’ve tried to write content that’s witty or overflowing with metaphors to create a vivid emotional reaction. Except it doesn’t quite land how you expected. The meaning and purpose of the content are lost in creativity.

Both scenarios are examples of how being “clever” can negatively affect the impact of your content. As writers, we live by the rule of being “clear over clever.” Yes, being clever is great. It turns heads, captures attention, and shows people you know what you’re talking about. But it should never come at the expense of your writing being clear and easy to understand.

If you’ve written something that you think might be clever rather than clear, you can use AI to clarify your writing.

AI prompts such as “Does this text make sense?” or “Make this text easier to understand” let you check and improve the clarity of your writing.

You can use the AI output to see whether it has understood your content or if the original meaning has been lost. In its response, Chat-GPT will tell you what it interpreted the text as, and it might also provide suggestions for improving your copy. You can then use this information to rewrite your content so it is easier to understand.

Use AI to simplify your writing

I have the habit of being a bit of a waffler when I’m reading. My default writing style is to use all the words. So, when proofreading my content, I sweep my content for long sentences and large blocks of text. I will then highlight these and simplify them.

To speed up this process, you can use AI to simplify content so it’s shorter and more readable.

Some AI prompts you can use to simplify your content are:

• Make the following text shorter and snappier

• Explain [topic] to me like I’m 7 years old

• Make the following text easier to read

If we input a large block of text, you can see how the prompt “make the following text shorter and snappier” produces a simplified version of the original content. You can then comb through your content, making it short and more readable.

Final thoughts — AI can be a powerful content marketing sidekick

AI isn’t perfect, but it can be an invaluable asset for content marketers, acting as a powerful sidekick at every stage of the content creation process.

Guided by well-crafted generative AI prompts, you can optimize your content. From kickstarting content research with keyword research to creating hub-and-spoke content strategies, generating headlines, and clarifying your writing, AI can streamline your content marketing efforts across content research, writing, and editing.

Remember, you can’t rely on AI alone just yet. Your content still needs a human touch if you want it to outperform your competitors — especially when writing for YMYL topics and niches where E-E-A-T is crucial.

For the best results, pair AI’s capabilities with human creativity, experience, and expertise so you can stay ahead in the ever-changing world of content marketing.

Author names: Do They Matter? How to Attribute Content

Few aspects of my job drive me to extreme internal conflict, but the subject of author attribution is one that tears my brain in two.

My writer brain says, “Yes, absolutely! Writers’ names on all that they create! Credit where credit is due!”

My marketer brain is apparently more reserved because I find myself asking myself questions such as:

• Who deserves the credit for this article? The actual writer? The entire team of researchers who helped provide data? And what happens if a totally different writer updates the article later?

• Will the author’s name support this content’s performance, or would it be better received if a more well-known subject matter expert had their name on it?

• Does it need a name at all, or is that just distracting extraneous information?

So, who’s right? Me, or me? (I like the odds on this one.)

Let’s try to get inside Google’s mind to figure this out — first, by reviewing what Google has said on the matter, then by looking at some real search results from Google.

What does Google say about author attribution?

Google has grappled with author attribution for a long time, as evidenced by the birth and gradual death of the Google Authorship experiment that carried on for several years in the early 2010s.

In the end, the folks at Google decided they’d rather use algorithms to try to identify the author of any given piece instead of relying on the oft-forgotten, occasionally misused, rel=”author” tag.

The search giant seems confident that they could do this, as evidenced through comments made by Google leaders such as, “We are not using authorship at all anymore… we are smarter than that.”

But where their confidence really comes through is in their extensive collection of patents. As pointed out by Olaf Kopp, writing for Search Engine Land, there are ample methods by which Google can attempt to identify the author of a piece, including:

• Author vectors: Identifying the unique style of a writer and using that to attribute content.

• Author badges: Using identifying information such as an email address or name to verify authors.

• Agent rank: Assigning content to an agent (an author or a publisher), and using backlinks to, in part, determine the rank.

And there’s more. It’s not known which, or how many, of these are used actively in search algorithms — and if so, how they’re used or how heavily they’re weighted.

So, is that the end of it? Author attribution doesn’t matter because Google “just knows”?

No, that’s way too easy. See, we also have cues from Google pros like John Mueller and Danny Sullivan advising people to strive for highly authoritative content by way of having experts write or proofread content on their area of expertise.

Furthermore, Google’s own Search Quality Evaluator Guidelines include specific instructions on “Finding Who is Responsible for the Website and Who Created the Content on the Page,” and highlights author-related observations for both low-quality and high-quality content.

The Search Quality Evaluator Guidelines are the guidebook that search quality evaluators use to analyze organic Google results to provide feedback on the effectiveness of Google’s algorithm. If low-quality results end up in SERPs, they flag it.

So, why would they need guidance on identifying authors and responsible parties if it doesn’t matter?

The answer: It might just matter.

What do Google’s search results tell us about author attribution?

OK, now we know what Google says about the subject:

• It’s not necessary to name authors in content…

• Because Google already knows who wrote what on the internet…

• But it’s also advisable that content is created or checked by experts who have authority on the topic.

So, do actual Google search results reflect that?

Last year, I published the results from a study I conducted in an attempt to isolate which factors are really, truly important to demonstrate E-A-T (this was before the addition of the second E).

My writer’s brain approached that study with the idea that author attribution had to matter. And what I found was somewhat disappointing to that version of myself but validating to the marketer in me.

First: A quick primer on how this study went.

I chose seven categories and ten queries for each category. I searched all 70 queries, clicked on all 647 Page 1 results, and took notes. If a particular element was highly prevalent on Page 1 results, I considered it to be important. If it’s more common in the Top 3 results than the overall Top 10, then I’d view it as very important.

I looked for a bunch of author-related factors:

• Author name

• Author has previously been published online

• Author is affiliated with the organization

• Author is a guest contributor

• Detailed author bio is available

• Links to the author’s website, social accounts, or other information

• Link specifically to LinkedIn (I counted this one separately)

• Multiple authors or contributors listed

And here’s how each of these factors performed in my study:

Author name

• 46% of Page 1 results attributed their content to a person, a group of people, or to an organization.

• 43% of Top 3 results did the same.

Of all the 32 factors I looked for, this was number 15 in the study, following truly important things like HTTPS and having original research published on-site (you can see a detailed description of each of the factors I analyzed here.)

Does this tell us that authorless content is OK? I’d say it’s actually a reflection of the types of content being served. There are plenty of times when author attribution simply isn’t needed.

Now, let’s look at the rest of the author-related factors I considered. For the rest of the study, I considered the overall 647 results, as well as the results for what I called the “author set,” which is the 298 results that included an author name.

Previously published author

• 36% of Page 1 results had a named author who clearly had previously been published online.

• 35% of Top 3 results showed the same.

It doesn’t seem to matter one way or the other how much previous publishing experience the writer has. But, among those results that did name an author, how common was previous publication?

Among our author set, the percentages look a bit different:

• 79.2% of Page 1 listings with listed authors had previously published authors.

• 81.3% of Top 3 results with listed authors showed the same.

There’s a bit more experience among Top 3 results’ authors than among the general Page 1 results’ authors. This could reflect higher-domain publishers’ (which are likely to rank well already) careful discernment of the authors they work with. Or, this could show that experience matters for ranking — alternatively, it could indicate that experience is good for creating quality content. Practice makes perfect.

Author affiliation: In-house vs. guest contributor

Do guest posts perform better than in-house written content? It looks to me that there’s no real advantage in one approach or the other.

• 23% of Page 1 results had authors who were clearly affiliated with the organization (e.g. they were employees).

• 22% of Top 3 results had the same as above.

• 13% of Page 1 results had authors who were clearly guest contributors.

• 12% of Top 3 results had the same as above.

It’s more common to have an author who’s affiliated with the publishing organization. But that doesn’t mean it matters. The nearly identical results for Page 1 vs. Top 3 results for both of these factors show that it’s not particularly critical.

The author set reinforces my theory that author affiliation doesn’t really matter much:

• 49.33% of Page 1 results with named authors were in-house contributors.

• 50.33% of Top 3 results showed the same.

• 28.86% of Page 1 results with named authors were guest contributors.

• 28.57% of Top 3 results showed the same.

This could actually be a reflection of how difficult it can be to attract high-quality guest posters instead of how important either strategy is. To establish a guest posting program (which is what you’d want to do to support an ongoing guest post initiative), you’ll need a few things, including:

• Lots of traffic. Guest authors like to contribute to websites that get views.

• A good reputation. Otherwise, what will incentivize them to contribute?

• A manager. Guest post programs can get complex quickly, between vetting writers, approving topics, proofing content, and the publication and distribution of it all.

Author bio and links

Including an author biography or links to their personal website, portfolio or social media profiles can help readers learn more about whose content they’re reading. It also gives search crawlers more opportunities to get to know the content creator.

I considered these items separately:

• Detailed author bio (as opposed to a sparse, unhelpful one).

• Links to the author’s personal website, portfolio or social media, excluding LinkedIn.

• Links to the author’s LinkedIn profile.

Detailed author bios were the most common, with 22% of both Page 1 and Top 3 results containing one. Next up were links to authors’ personal websites, portfolios, or social media profiles, which showed up 18% of the time on Page 1 and 16% of the time in Top 3 results. Finally, 11% of Page 1 listings had LinkedIn profile links for the author, whereas only 10% of Top 3 did.

The only one of these factors that really changes when looking at the author set is the detailed bio. 48.3% of Page 1 results’ authors had one, whereas 51.7% of Top 3 did. So, it’s a small difference, but it’s enough to make me think that a bit of information about your author could be beneficial.

Multiple contributors listed

I called this multiple contributors rather than multiple authors because this category includes listed activities like:

• Editing

• Proofreading

• Fact-checking

• Contributing (e.g., providing research, interviews, or written content but not having written the entire thing)

• Updating

Could listing multiple contributors on your content help it rank? My results don’t really support that. This was a find that disappointed not just my writer’s brain but my marketer’s brain, too.

• 17% of Page 1 results listed multiple contributors.

• 13% of Top 3 results did, too.

Among our author set, 36.58% of the Page 1 results had multiple contributors listed, and so did 32.97% of the Top 3 results.

Here’s my disappointment: Many of the results I analyzed included multiple contributors because they were being fact-checked or reviewed by professionals in that field, like doctors checking medical content — exactly the type of thing Google advises.

Similar to the guest posting factor I considered above, this could be a reflection of the practicalities of having in-depth content that requires multiple hands before it’s published. It’s another activity that requires a lot of time, talent, and resources.

When — and why — should you attribute your content?

By now, we’ve learned that author attribution kind of matters for ranking but isn’t a make-or-break factor on its own.

Or is it?

I’ve come to the opinion that it depends on the type of content in question (my marketer’s brain takes the lead). Regardless of which method you choose, here are some of the benefits you could gain through your choice:

• Demonstrate your brand’s authority. Choosing the right author attribution can highlight your organization’s expertise.

• Give credit to the creator. When it’s appropriate to attribute the true author, doing so can help build a positive relationship with that author and gives the added benefit of boosting their online portfolio (which should, in the long term, add further credibility to the content they create for your brand).

• Provide information for readers and crawlers. Attribution helps the humans and robots who review your content to find more information about the topic as well as the expert who wrote it.

Here are some common marketing content types and appropriate options for author attribution:

Blog posts

Posts written for your organization’s blog are a prime opportunity to show off author credentials, or it could be an opportunity to highlight the expertise of your in-house experts (whether they penned the content or not).

Here are some questions to ask when deciding how to attribute blog content:

• Is the author a true expert on this subject? If yes, include their name.

• Will this author contribute regularly to your blog? If yes, all the better to include their name.

• Is this author well-known or respected in this field? If yes, definitely list their name.

If your author isn’t an expert in that topic, you could attribute the content to an actual expert to lend authority to the piece. In that case, it’s recommended to have that person read over the content to get their sign-off.

Another option for organization blogs is to attribute the content to the organization itself or to the group of people who are in charge of reviewing content. For example, lots of Mayo Clinic’s articles are authored “By Mayo Clinic Staff.”

Guest articles

Guest article attribution can be considered in much the same way as blog posts. If the author is a real expert (which is more likely when working with guest-post programs, as many choose their contributors carefully), including their name can add credibility to the piece.

Crediting a guest post can boost awareness or reinforce brand recognition among readers. For example, this Marketing Week article about TikTok is sponsored by the social media brand, but no author is listed. The piece’s main purpose is to spread awareness of TikTok’s capabilities as a marketing platform.

Landing pages

Landing pages are designed to get viewers interested in taking some type of action with your company. There are tons of types of landing pages:

• Commercial landing pages that discuss the virtues of your products or services.

• Conversion landing pages, which people see after clicking on an ad.

• Subscription landing pages, where people sign up for your newsletter.

• Company landing pages, such as your about page or careers page, which support people exploring your company with a variety of intents.

In any of these cases, it’s not necessary to attribute your content. Someone at your organization likely wrote them or, at the very least, reviewed the content for accuracy and brand consistency. It’s assumed that the responsible party for this type of content is your organization as a whole.

Pillar pages

Pillar pages are sort of like blog posts in that they are highly informational and support people who want to learn more about a particular subject. Where they differ from blog posts is in the depth of their content — usually, they’re breaking down a multifaceted topic, instead of focusing on just one facet like a blog post might do. In fact, pillar pages often link out to blog posts that dive deeper into relevant subtopics.

Because pillar pages address big, broad topics and link to articles for further reading, they are powerful topical authority pages.

Including an author’s name could add credibility to these pages, but it could also take away from it. Without an author’s attribution, the assumption is that, like landing pages, your organization at large is responsible for the content. And, if it’s covering a topic that’s central to your brand identity and linking to the many articles you’ve published on your blog covering the matter, then it may be best to let this content be “authored by your organization.” A great example of this is the Moz SEO Learning Center.

After all, your company’s credibility and expertise matter most when building brand trust — not the individual writer who happened to pen the piece.

Press releases

Press releases are announcements from your organization, meant to be distributed far and wide by a wire service. The tone should be congruent with your brand voice, your brand logo should be included, and details about your organization are a must.

In almost all cases, press releases are authored “by your brand.” That said, a real person should always be included as the media contact. This is the person that people — especially journalists — can contact to learn more about your brand and the announcement.

Original research and thought-leadership content

Original content and original research are going to be hugely important for SEO in the years ahead. Google’s own communiqués about the Helpful Content Update, E-E-A-T, natural language processing abilities in search, and more prove that Google really does care about original content.

Original research was one of the 32 points I checked for during the study, and it was more prevalent than even authors’ names — signifying that not all original research had an attributed author.

• 64% of Page 1 results’ websites had original research available.

• 70% of Top 3 results’ sites did, too.

Original research is any type of content that includes unique information that the company gathers, analyzes, and publishes itself. To name a few, these could be in the form of:

• Surveys

• Annual reports

• Original product reviews

• Website or organization data

• Grant or funding information

The decision to attribute original research depends on the type of original content in question. Things like annual reports and grant information could feasibly be authored “by your organization,” whereas original product reviews may read more trust-worthily if readers can get to know the actual people who tested the product.

At Brafton, we conduct original research to learn more about the state of the content marketing industry, and the blog post written afterward is attributed to the blog post’s author. We’ve chosen to do that to match the style on our blog (we love our authors because they’re all experts at what they’re doing, and it’s a great opportunity for them to showcase their talent).

To attribute or not to attribute: Who won the debate?

I’m pleased to announce that I won this argument against myself. More importantly, I’ve decided that both of my selves — writer me and marketer me — are correct.

My marketer self might be slightly more right, though.

Attributing content can be worthwhile for your brand and the author in lots of situations.

However, there are plenty of other situations where attributing an expert who didn’t actually create the content but did review or otherwise consult on it can lend credibility to your brand.

Finally, there are actually plenty of instances where there’s no need to attribute anyone at all.

In the end, it all depends on the purpose of the content and your goals with it.