How She Scaled an AI App to 3M Visitors/Mo Using AI SEO ft Chang Chen

Summary

Marketing Against The Grain: How She Scaled an AI App to 3M Visitors/Mo Using AI SEO ft Chang Chen

Hosted by HubSpot Podcast Network | Release Date: November 26, 2024

In this insightful episode of Marketing Against The Grain, hosts Kipp Bodnar and Kieran Flanagan engage in a deep conversation with Chung Chen, a seasoned growth advisor renowned for her work with companies like Heygin, Microsoft, and Otter AI. The discussion centers on the transformative impact of Artificial Intelligence (AI) on marketing strategies, with a particular emphasis on AI-driven Search Engine Optimization (SEO). Below is a comprehensive summary capturing the key points, discussions, insights, and conclusions from the episode.

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1. The Evolution of Marketing in the Age of AI

Chung Chen begins by outlining her perspective on the evolution of marketing, emphasizing AI's pivotal role in this transformation.

Chung Chen [02:08]: "AI can be your employee that you should be able to delegate stuff to it. It's not here to replace anyone."

She traces the journey from the traditional TV-era marketing, characterized by mass-produced, non-personalized content, to the data-driven segmentation strategies that enabled more tailored marketing efforts. The advent of AI, however, marks a significant leap towards one-to-one personalization, where AI agents autonomously learn and adapt to individual user behaviors, delivering highly personalized experiences without extensive human oversight.

Chung elaborates on how AI shifts the marketer's role from creating broad segments based on experience and experimentation to leveraging AI's self-learning capabilities for nuanced personalization.

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2. Core Use Cases of AI in Growth Marketing

The conversation delves into specific areas where AI is making significant strides in marketing:

• Localization: AI-driven translation tools reduce costs and accelerate content creation.

• AI Native Channels: Emerging traffic sources from AI search engines and AI answer engines, though still nascent compared to traditional SEO.

• AI SEO: Achieving substantial traffic increases, exemplified by driving 3 million monthly visitors within six months for a product.

• Influencer Campaigns: Utilizing AI to engage more micro-influencers efficiently, cutting costs by up to 80%.

• AI Conversion: Implementing AI agents for one-to-one personalization to convert more free users to paid subscribers.

• Content Repurposing: Leveraging AI to transform information-rich content into various formats, enhancing content scalability and versatility.

Chung Chen [06:08]: "Right now we are spending a lot of time on localization, SEO, and content repurposing and I'm very curious about where the AI native channels will grow."

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3. AI-Driven SEO Workflow

A significant portion of the episode focuses on the AI SEO workflow developed by Chung Chen, designed to streamline and scale content creation for SEO purposes.

a. Keyword Research and Scraping: AI tools scrape Google search results, summarizing top SERP (Search Engine Results Page) data to identify key keywords and user intents.

Chung Chen [13:00]: "We have AI to connect to the Google search and scrape data and to learn the key points and to memorize the key data points that we should reference."

b. Content Summarization and Briefing: AI assists in summarizing search data and generating article briefs, which are then reviewed and refined by human editors to ensure quality and relevance.

c. Content Creation and Polishing: While AI drafts the initial content, human writers and editors infuse unique perspectives, ensuring the content stands out and aligns with brand voice.

d. Scaling with Automation Tools: Utilizing platforms like LOps, the workflow integrates various AI models for different tasks, enabling programmatic creation of landing pages via CMS platforms like Webflow.

Chung Chen [14:19]: "They have a native component for Google search... you can choose that for summarization you want to use one model. And for writing you can apply to a different model and you can combine and mix different models to create higher quality content as well."

e. Customizing Landing Pages: For product-focused landing pages, AI targets specific features with dedicated keywords, automatically generating tailored content for each page.

Chung Chen [18:34]: "For this one, which is the AI video app, it has all the different features for one feature. We actually identified 10 plus different keywords that we can target."

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4. Maintaining Content Quality and Uniqueness

Chung emphasizes the importance of human oversight in the AI-driven content creation process to maintain quality and ensure uniqueness.

Chung Chen [15:23]: "Probably 50% or even sometimes more than 50%."

While AI handles the bulk of content generation, human editors play a crucial role in infusing personal experience, unique insights, and brand-specific information, thereby enhancing the content's value and ensuring it doesn't appear duplicated or generic.

Chung Chen [21:04]: "Most of the time it's the unique Content because we do see more and more people started to scale their content creation with AI. But at the end of the day, we are still the same Internet. We want to have information gain, we want to be able to stand out from the competition."

Key elements added by humans include:

• Unique Perspectives: Drawing from personal and professional experiences.

• Market Research Data: Embedding industry benchmarks and data points.

• User-Generated Content: Incorporating reviews and community discussions.

• Expert Insights: Leveraging content from interviews and specialized sources.

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5. Performance Metrics and Outcomes

The AI-enhanced SEO strategies have yielded impressive results, with AI-created content performing on par with traditional human-only content.

Chung Chen [23:22]: "Most of the content we read is performing on par with the traditional human owning content."

Key achievements include:

• Traffic Growth: Scaling to millions of monthly visitors within a short timeframe.

• Cost Efficiency: Significant reductions in content creation costs.

• Productivity Gains: Allowing human teams to focus on creative and strategic tasks by automating repetitive tasks.

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6. Common Mistakes to Avoid in AI SEO

Chung highlights several pitfalls that marketers should steer clear of to ensure effective AI SEO implementation:

1. Ignoring Keyword Intent: Failing to align content with the underlying intent of keywords can lead to ineffective content that doesn't meet user needs.

2. Targeting Overly Competitive Keywords: New websites should focus on niche, lower-difficulty keywords rather than attempting to rank for highly competitive terms initially.

3. Compromising Content Quality: Prioritizing quantity over quality can result in generic, low-value content that fails to engage users or rank well.

4. Lack of Unique Value: Creating AI-generated content without adding unique perspectives or additional value can lead to penalties from search engines and poor user engagement.

Chung Chen [28:10]: "The biggest mistake to avoid when it comes to AI SEO is like don't go and create a mass amount of AI generated content with LLMs that no human is working on improving like the quality, unique perspective, detail of information to make it really valuable and unique."

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7. Practical Recommendations for Implementing AI SEO

For marketers looking to harness AI for SEO, Chung offers actionable advice:

• Start Small: Begin with niche keywords that have lower competition to build a foundation before tackling more competitive terms.

• Focus on User Intent: Ensure that content aligns with the searcher's intent, whether informational or transactional.

• Maintain Human Oversight: Continuously refine AI-generated content with human input to uphold quality and uniqueness.

• Leverage Automation Tools: Utilize platforms like LOps to streamline workflows and scale content creation efficiently.

• Integrate Unique Content Sources: Incorporate data, user reviews, and expert insights to differentiate content from competitors.

Chung Chen [30:48]: "I always advise the company that don't create content when you don't have anything unique to add."

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8. Final Thoughts and Key Takeaways

The episode concludes with a synthesis of how AI is revolutionizing marketing, particularly through AI SEO. The integration of AI tools facilitates unprecedented scalability and efficiency in content creation, while human expertise ensures the delivery of high-quality, unique, and valuable content. This synergy between AI and human input represents the next generation of personalized marketing, moving beyond broad segmentation to individualized user experiences.

Chung Chen [27:04]: "Now that we're actually just skipping that step and to rely more on the AI agent to understand the user on the one on one level and provide that proactive as well as super personalizing experience."

For marketers aiming to scale their reach and enhance their SEO strategies, embracing a balanced approach that leverages AI's capabilities while maintaining human oversight is essential for sustained success.

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Conclusion

Chung Chen's insights provide a roadmap for leveraging AI in modern marketing strategies, particularly through AI-driven SEO. By understanding the evolution of marketing, implementing robust AI workflows, maintaining content quality, and avoiding common pitfalls, businesses can harness AI to drive significant growth and achieve a competitive edge in the digital landscape.

For those interested in exploring AI's potential in marketing further, this episode serves as an invaluable resource, blending practical advice with strategic foresight.

Transcript

[Host]

Hey everyone. Welcome to today's show. We've got a very special guest. We're joined by Chung Chen who is a growth advisor. She's worked with amazing companies like Heygin and Microsoft Otter AI and she is going to walk us through today how marketing is changing with AI. What are the core use cases that you should be doing right now to grow your business with AI? And we're going to do a deep dive on AI SEO. She's going to share her workflow that uses AI to help you research keywords, search results that helps do drafting and save a lot of the manual work that humans hate to do. And we're going to take you through the mistakes you want to make sure you avoid so that you don't waste a bunch of time creating content with AI that gets no results at all. That and so much more all in today's show. Let's get to today's episode.

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[Host]

When we talk about AI, you know, Kieran and I on marketing, it's the grain. We cover a whole host of topics. We talk about large language models, their innovation, different use cases within marketing. One of the reasons we're really excited to have you on the show today is because you've got a pretty clear framework for how you think about the primary AI marketing growth use cases and that you've done a ton of work. And maybe the best way to start for everybody watching the show today is for you to kind of kick us off and level set how you think about kind of the evolution of marketing and the role AI plays in.

[Chung Chen]

Yeah. Yeah. So I've been a big believer of AI and I think it's mostly because I started to help a company called Alter AI to grow before AI is popular and already started to see that how AI can help people to take care of the oriented repetitive task. One of the things that we already started to see that AI can really be your trustworthy employee that can take care of some of the things. So how we think about AI is that AI can be your employee that you should able to delegate stuff to it. It's not here to replace anyone. So with more and more application in AI that we started to see how the marketing start to transition. When I first started to practice marketing, that is still back in the TV era where we see high value production content being broadcast to everyone, where there's no personalization and it's really hard to provide each one of the content pieces. But then we started to have more data where we do see the personalization, we do see the data power the growth engines to build. We do start to see that the segmentation is built and we're able to provide a more catered and customized experience for each one of the segments. But nowadays with AI agents we are starting to observe AI will be able to learn. We don't much rely on the growth marketer to create segments based on our experience based on experimentation. But now the AI agent will actually be able to learn the user behavior and be able to provide one to one personalization to provide a white glove service to each one of the users. So it's not much a human decision based on data, it's more AI self learning and they'll be able to apply more personalized and more catered experience to each one of the users. So with all the observation we also started to experiment and test how we can better apply AI on different areas of marketing. So we have been experimenting with all the different aspects of growth marketing and see how AI can be helpful. We tried with AI with the localization. So far the translation is usable and we definitely have seen the AI can help us to reduce the cost and help us to create content a lot faster. We also experimenting and start to see more and more traffic coming from AI native channels like AI search engines and AI answer engines. So far it's not a major channel yet. It's not anywhere close to like SEO traffic. But we do start to see an increasing traffic from them. And AI SEO definitely saw to significant success for several of the companies influencer campaigns as well. It help us save at least 80% of our costs and be able to help us to reach more micro influencers with higher our efficiency. AI conversion is where the AI agent that was able to provide a one to one personalization by the glove service to help us to convert more free users to paid content repurposing. There's just so many different tools in we have been seeing success with all the different content repurposing, but generally if you started with the information rich content and repurpose it to something that's less information rich, then AI usually does a really good job to synthesize it. But if you start with something that's less information like an image or a plain text, but you want to repurpose it to video, then that will rely more AI to enrich and imagine it. Then usually the end result content will have a lower quality. So we're still experimenting, but we do see more exploration in terms of content repurposing and the social management, paid ads, market research, insights called outreach and the churn reduction. We do start to see more and more success with all the different areas of AI for growth and marketing.

[Kieran]

Where do you see Chun the earliest kind of success with AI for growth marketing right now? What are the things that you're most excited for or spending your time on?

[Chung Chen]

Right now we are spending a lot of time on localization, SEO and content repurposing and I'm very curious about where the AI native channels will grow.

[Host]

What's interesting is that's exactly how we're kind of thinking about it too. The localization has worked really, really well. At HubSpot, we have a content remix feature for our customers that's in that content repurposing bucket that has seen huge adoption and a lot of success and great retention and everything. So that's a clear early use case. And when you say add native channels, you're Talking about search, GPT, ChatGPT, you're talking about perplexity Claude, and how they can be referral channels. And I know Kieran, you're like, hey, I don't think they're ever going to be big referral channels. So maybe this is a time to have that discussion before we go deep into like the AI SEO side of things.

[Chung Chen]

Right now we don't see them as major channels yet. Yes, they said even how close that we try to optimize for some of the keywords. So especially for the shallower content like listicles, people may get some brand impression, but we haven't seen it become a major traffic source yet.

[Host]

Kieran, you and I have gone back and forth where you're like, hey, do we think people are actually going to interact with the links in these AI answer engines or are they just going to get their answer and move on and it's going to be kind of an indirect awareness motion? Want to share your take on that with everybody as we Kind of are in the monitoring stage of these folks.

[Kieran]

Yeah, I think for the most part you can't expect to get a bunch of traffic because it just provides the answer to you. So it's really hard to see lots of people clicking on those links. Like, I just don't see that actually happening that much. I think that for the most part, the AI agent is going to do a much better job than the blue links did. And so we're probably going to see many people kind of create content and measure that for influence and impressions and actually measure it for clicks. I think that's the big change. We used to create content for clicks and now we kind of create content for influence and I think that's the change I see happening.

[Chung Chen]

Another AI native channel we are monitoring very closely is the AI companions. So right now, so for example, that I'm using one of AI companions to help me to plan many of my trips, even though the AI companion most of the time won't provide direct links. So I started to trust more on the recommendations because the companion is already trained on my preference. So most of the time the recommendation is very personalized and I started to gain more trust on them.

[Kieran]

Yeah.

[Chung Chen]

So instead of going to different traveling blogs and some influencers, I actually started to taking more advice from my own AI companion. So that's also another influence channel that we are monitoring very closely. But right now it's still really hard to attribute.

[Kieran]

Yeah, yeah, exactly. Yeah, I think it's really hard to attribute. It's kind of indirect attribution.

[Host]

And because that's indirect attribution, I think one of the things that you're saying you're spending a lot of time on and seeing a lot of value is using AI for the traditional SEO motion for Google search to try to capture a higher percentage of traffic there kind of early in this transition. Given that the answer engines haven't scaled up to be massive consumer engines just yet, we still have to really focus on the Google product and what we're able to do from SEO there. And so maybe walk us through how you think about it, what you found to be successful, what folks can do on the AI SEO front.

[Chung Chen]

Yeah. So for AI SEO, we are definitely seeing success. I think this is one of the first application for AI because the generative AI technology is really good for copy creation and it can be treated as the tireless writer in your team that if you ask a writer to create 1000 piece of content or ask the AI to rewrite a thousand piece of content, that will Never complain and the writer will be available 24 7. So we started to see a lot more success. So for one of the products we were able to drive the traffic to 3 million monthly within just six months. And even with all the Google updates we haven't seen much traffic drop yet. And for 7 year old site we will do 2x amounts of visiting in just 6 months. And the content creation cost just has been slashed. But right now we're being very cautious to experiment and use AI owning content. Because if you think about AI for content, AI for SEO there can be three categories. There's a AI owning content that you give AI a prompt and they will write 100% of the copy or in the traditional way there's a human created content. And then we have editors to help us to ensure to create a high quality piece of content with several steps. And then now we are experimenting and try to mix the two. So we still have human in the loop and to be able to follow almost the same writing process as if that we still have the human team to help us to create the content, but we use AI to help us to streamline and to expedite some of the steps right now the process that we are using right now we still start with keywords and previously we have human to help us to do search and research and to find the top search results. But now we have AI to do the scraping and AI to help us to summarize them. After that we have the human review to distill the learnings and to identify good part of content that we should add to our content onto our own. And then we have AI to take the human input as well the learnings to create article brief. And then we have human review again to make sure that everything looks right. And the next part we have been experimenting the most which is how AI can learn and take some of the unique content from our product for our brand, for our user as well as our human writer to really be able to add information gain to the Internet and to create that unique content. And with all the learning information then we still have AI to draft the initial draft. But we always have the human which is our editor and writer to polish to make sure that it's on brand and make sure it's high quality. So far we find this mix of human expertise and AI employee or the AI writer to really be able to improve the efficiency without sacrificing the content quality.

[Kieran]

Cool. Yeah. Can you maybe explain the first step? What does it do? Like can you just take us through what that first step is, it summarizes the top SERP results.

[Chung Chen]

This is based on the observation of how our writers usually do for some of our keywords that they will come to Google and they will search for the keywords that we target and they will find the top results and the research. What are the different components the top results writes about? They will also look at the people also ask section and trying to find the relevant longer tail keywords and the longer tail questions that we can include and cover to make sure the content is comprehensive. And some of the key learning points they will also document like some of the industry benchmark market data points so that we can reference them in our article. And for the AI step that we have is we have AI to connect to the Google search and scrape data and to learn the key points and to memorize the key data points that we should reference so that we can have reference links added to our own article.

[Kieran]

Yep.

[Host]

What tools are you using to do each of these stages? I know that's one of the first questions we'd get in the comments is like, great, I understand how I can create a lot more search content that's better with AI, but like tactically, how do I go and do that?

[Chung Chen]

Yeah. This screenshot is using L Ops. So it's a workflow automation platform that can help you to connect different components. They have a native component for Google search. Then you can add your own prompt. You want to teach AI how to summarize the search results. And they also have different components like writing for alm and it has integration with almost all the models. So you can choose that for summarization you want to use one model. And for writing you can apply to a different model and you can combine and mix different models to create higher quality content as well.

[Host]

Got it. And just so that everybody's is this flow for blog articles, landing pages, can you give us more specificity on the types of pages? And tactically, how do you think about taking a workflow like this and doing it at scale? Because if you're going to grow millions of extra visits every month, you obviously have to get pretty high scale. And I'd love to just kind of walk through how you do that.

[Chung Chen]

Yeah, this was it. For an informational blog, we also have a different flow for listicle articles, for how to articles, and for landing pages we also have more programmatic ways. So this one is actually for programmatic landing pages because for programming landing pages that it has a structure like section one is talking about this topic. Section two is talking about a different topic. So we are able to use one workflow and a list of keywords because each page will have different keywords then turn into like a table. Then each row will represent the content for one page and we use webflow so we can just upload the CSV and be able to create Landing Pages at scale.

[Host]

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[Co-Host]

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[Host]

Got it. So you're basically for landing pages. You are taking your workflow, running your landing page keywords from that and just basically spinning those up within webflow or you know, most CMS products would do something similar here. And for those landing pages, are those mostly like content focused landing pages or different derivatives of product focused landing pages so that people kind of understand are you trying to take different product offerings and verticalize them and specialize Them or are you trying to do something different?

[Chung Chen]

Yeah, depending on the product. So for this one, which is the AI video app, it has all the different features for one feature. We actually identified 10 plus different keywords that we can target. So then we use that one feature to create one template landing page and then use the same workflow to create different versions of copy for the same feature so you can target it for different keywords.

[Host]

Got it. So basically what you're saying is like, hey, the old way is either have kind of one or two product focused landing pages that are more broad, or to have a person who manually goes and creates like a whole portfolio of specific landing pages. Instead you're like, hey, I'm using AI to figure out what keywords are best for this particular product. And then I am going to go after those keywords by having AI, through my kind of programmatic prompt and spreadsheet, generate the content and the pages automatically and then a human's going to go and review and publish. Is that correct?

[Chung Chen]

Yeah, yeah. And the key is to make sure that you actually have something unique for each one of the pages. You can pull in different unique blog articles that you wrote previously, or you can pull in different user reviews, but just to make sure that has the unique piece of content. So when the user review data won't see us duplicated content, so it takes.

[Kieran]

The keywords, it runs, AI creates all of the articles. What percent of a complete page do you think the AI does versus how much does the human have to do? So is it 50% of the work is done by the AI and then the human has to add the 50%? And then the second question would be really interesting, is like, what are the things the human is doing that is making these pages much better to rank? Is there some commonalities in this stuff that they add that works really well?

[Chung Chen]

Depends on the page. For example, if the same product feature but we want to target different keywords, then that part it's a lot easier because most of product description and most of the product positioning part can be reused. But we use AI to rewrite. But if it's a blog article like this one, then it takes a lot more human review and takes a lot more personal experience. And to have our editor's perspective, this one, I would say probably 50% or even sometimes more than 50%. But if it's for expansion, then it's more automated.

[Kieran]

Okay, and what do they typically add to the page to make it rank really?

[Chung Chen]

Well, most of the time it's the unique Content because we do see more and more people started to scale their content creation with AI. But at the end of the day, we are still the same Internet. We want to have information gain, we want to be able to stand out from the competition. So the unique content, the unique perspective is still the key. Right now we start to identify more ways that we can have unique content sources that can be author and editor's input. The product messaging is still very important because all the SEO is for driving traffic and to have conversion, to have brand awareness. So we want to make sure that the AI will still be able to write our product and introduce our product in the proper location and in the right way. Market research data is also something we started to embed and started to insert to our articles because our unique perspective of how the market that we are in, it's a way to help to educate potential users about how the market is progressing and how our product positioned within the market. User generated content like a user review defined user discussion in our own community. Those can be really, really good content to help us to bring out that unique perspective and the subject matter expert insights we have on YouTube interviews broadcast like this to bring in the subject matter experts insights as well. Competitors analysis. It's usually the alternative pages or some other competitors versus type of landing pages or article that we already created. It's also another really good source to help us to bring out that unique perspective view brand guide as well as audience insights. So those are all the unique content sources that we are experimenting to add to our product. So it can rank higher other than all the traditional on page SEO stuff, the on page yourself stuff AI is already really good at. But I think the unique content piece is still something that AI cannot do by itself.

[Kieran]

Okay, that makes a ton of sense. And what has the performance been like for the pages that have been created by AI and humans? Has it been on a par with what humans have done? Like, have you found the same sort of ability to rank those pages to get traffic from those pages?

[Chung Chen]

Yeah, we do. See most of the content we read is performing on par with, with the traditional human owning content. Because right now you can see that AI is not replacing anything yet. We still need a lot of human input, but it definitely helps to save time.

[Kieran]

Yeah. So it's basically an ability for the human to do more. It's a productivity gain versus being able to rank things better. It's just being able to create more content.

[Chung Chen]

Yeah. And the most important part for the team right now, feel like AI is taking over the Part that human does not like to do, no one likes to click on the top 10 links one by one. And the scan through, scan through each one of one by one manually is that you can just actually do that with one click. So you're taking over the repetitive and the energy draining part so the human can actually focusing on the creative part.

[Host]

Yeah.

[Kieran]

Cool. This is a very cool workflow.

[Host]

And just to clarify for everybody watching, what we're doing here is you're creating the content with AI and obviously all these pages are trying to convert these visitors, but using AI to basically get the traffic and then all the calls to action, everything, those are still just at like the template level. So as the pages are spun up like the landing pages and everything, the forms or call to action, everything are just built into the template. You're not doing any kind of conversion personalization with AI on these pages, is that right?

[Chung Chen]

Yes, yes. We are seeing some AI agents on the page, like chatbots that can start to do some of those. But this workflow is not that.

[Host]

Got it. So if you're a company out there and you have kind of a limited footprint on Google, especially for like kind of blog content, you have a pretty wide product offering but aren't getting a lot of product traffic, don't have a wide like portfolio of product pages, you would use this workflow to basically increase the traffic. And then what you're saying is the number one recommendation for converting that traffic would be to have some type of AI kind of chat agent that folks can interact with that can qualify and either route to a human. If it's a high consideration purchase or kind of facilitate an E commerce transaction. If it was like a lower consider purchase.

[Chung Chen]

I will say I don't have too many conversations, number one. But we do start to see the conversion gain if you apply a personal assistant or a personal agent to help the shopper to explore your site and to help a user if you are a SaaS product, to understand more about the free product offering and to handhold some of the early questions. The AI agent works better than the traditional tutorial, than a traditional rule based surveys, than traditional rule based onboarding flow.

[Host]

I do think that's one of the most important points that you've made today. I want to emphasize it for everybody kind of at the top. You walked us through that. We went from this kind of like broad mass market marketing to this second generation of data and segmentation focused marketing. And what's really happening with AI is that all that work we used to do to Create segmentation is becoming way, way, way less important because AI is able to do that on a one to one basis instead of a broader segmentation basis. And whether you're talking about, in the case of today's show, AI SEO and how you're trying to basically have a lot of variations of landing pages or a higher density of blog articles to speak to more detailed versions of your core kind of buyer, that is really one of the things that AI is great at facilitating and gives us like the next generation of segmentation. Am I getting all that right?

[Chung Chen]

Yeah. So I have been spending most of my growth career in creating segmentation to bring out that personalization. Then I also started to realize that manual segmentation was actually created based on experience, based on some data modeling. And we also have been iterating many of the times when we first created a segment. It may not be accurate. So now that we're actually just skipping that step and to rely more on the AI agent to understand the user on the one on one level and provide that proactive as well as super personalizing experience and be able to engage customers in the exact way that they want to be engaged.

[Co-Host]

Perfect. Okay.

[Host]

So I think that makes a ton of sense. I think to kind of close out today's show. What would be super helpful is if somebody's watching this and they're like, look, I've been doing a bunch of stuff with AI, but I haven't been able to get millions of more visits or drastically change the curve of growth for my business. What would you recommend for them? You gave them the framework, but what should they go and do to practically get started to go from kind of 0 to 1 on this type of work? When it comes to AI SEO, for.

[Chung Chen]

AI SEO we do see more and more people creating tons of pages. They can be pages of blog, they can be pages of landing pages without getting much traffic gain. Most common mistake we notice is the ignorance of the keywords and the user intent behind the keywords. Why I say that because many people that they will start with looking at the keyword that they can the keywords from their competitors and the keywords that they potentially can go after without looking at the potential difficulty or look at the existing website situation. So if it is a new website and they are trying to rank for super high traffic and super difficult keywords, then in the very short time there's not much chance that you can so new websites, you start with smaller keywords with lower keyword difficulty. And also another common mistake that we observe is ignoring the intent. So some keywords is informational, then it's more suitable for blog and for some keywords it's actually more transactional. People actually looking for do something, then a landing page or SEO tool will be more suitable to meet that intent then. The third most common mistake that we have been observing is the content quality. Because AI is really powerful. You can just give AI a prompt and it will be generated a lot of text for you. The longer text doesn't mean that it's a higher quality content and the longer text doesn't mean that it's a unique content. The longer text doesn't mean that it's a content that it can rank. And AI is not the game that the more the better. Most of the time it's actually the higher quality the better. So instead of creating tons and tons of low quality content that we highly recommend to really examine your workflow, to really take a look at the quality of the content to make sure that it will actually rank before you continue to create more.

[Host]

Got it. So to kind of close out, the biggest mistake to avoid when it comes to AI SEO is like don't go and create a mass amount of AI generated content with LLMs that no human is working on improving like the quality, unique perspective, detail of information to make it really valuable and unique. Right. Like if it's just going to be generic LLM content, people aren't going to go to it and you're just going to be spending a bunch of time for no results slash, maybe results that might even get you penalized in a search engine like Google. Is that what everybody should take away?

[Chung Chen]

Yeah. And I always advise the company that don't create content when you don't have anything unique to add.

[Host]

I love that. That is great advice. This framework around kind of the core use cases of AI and then how to actually get started and drive real impact with AI SEO has been awesome. If you have any questions, any follow ups on this, please put them down in the YouTube comments below and we'll see you all real soon. On the next episode of Marketing, it's the Green SA.