Generative AI Archives Generative AI

Nov 4 2024

OpenAI Reddit AMA And SEO For ChatGPT Search via @sejournal, @martinibuster

CEO Sam Altman and OpenAI executives held a Reddit AMA to answer questions, including those about ChatGPT Search, providing an inside look at how it works. Their answers offer insights into what SEO may look like in the immediate future.

The people from OpenAI answering the questions:

Sam Altman, CEO
Kevin Weil, Chief Product Officer
Mark Chen, SVP of Research
Srinivas Narayanan, VP Engineering

Why ChatGPT Search Is Important

ChatGPT Search is not a search engine, it’s an AI chatbot with search, which means it doesn’t compete with Google as a search engine, it simply replaces it with something else that people already use for work and play. Now it has additional utility as an assistant in daily life and search.

Another advantage to ChatGPT Search is that it doesn’t show advertising nor does it follow users around the Internet. Users already trust ChatGPT with personal and business information so it’s already has goodwill with users.

What makes ChatGPT Search a threat to Google is that Users are already familiar with ChatGPT and have good feelings about it. Because it’s already in use there is no switching away from Google to break the habit of searching with Google.

Sam Altman On Why ChatGPT Search Is Better

In the Ask Me Anything (AMA) session on Reddit, a Redditor asked OpenAI CEO what the value of ChatGPT Search is over other search engines.

The person asked:

“My question is about the value ChatGPT Search offers compared to popular search engines. What are the unique advantages or key differentiators of ChatGPT Search that would make it worthwhile for a typical search engine user to choose it?

Sam Altman answered:

“For many queries, I find it to be a way faster/easier way to get the information I’m looking for. I think we’ll see this especially for queries that require more complex research. I also look forward to a future where a search query can dynamically render a custom web page in response!”

That bit about a “custom web page” is something to look out for because it hints at personalization based on what a user is searching for.

Complex Queries Are ChatGPT’s Advantage

Altman’s response about ChatGPT Search’s handling of complex queries calls attention to an advantage over Google. ChatGPT users are accustomed to using natural language, whereas Google users habitually use keyword searches. Keyword searches disadvantages Google because it’s harder to understand those queries, which is why Google displays People Also Ask features in Search.

Natural language queries is the way users interact with ChatGPT and that is an advantage for ChatGPT Search.

Grounding For Better Answers

The next question was about OpenAI’s progress on preventing ChatGPT from making things up (aka hallucinations) and also about how it’s going to incorporate fresh data to the index.

Both problems are generally approached with a technology and technique called Retrieval-Augmented Generation (RAG) which selects data from an up to date database like a search index or a knowledge graph and then provides that to the LLM-based chatbot to summarize and use as a base for an answer.

This is the question:

“Are hallucinations going to be a permanent feature? Why is it that even o1-preview, when approaching the end of a “thought” hallucinates more and more?

How will you handle old data (even 2-years old) that is now no longer “true”? Continuously train models or some sort of garbage collection? It’s a big issue in the truthfulness aspect.”

The answer was given by Mark Chen, SVP of Research

“We’re putting a lot of focus on decreasing hallucinations, but it’s a fundamentally hard problem – our models learn from human-written text, and humans sometimes confidently declare things they aren’t sure about.”

Mark Chen continued his answer by saying that they are getting better by the use of “grounding” which is something that Retrieval-Augmented Generation (RAG) helps large language models with. Chen also reveals that they believe that using Reinforcement Learning (RL) may help models stop hallucinating.

Reinforcement Learning (RL) is a way to teach a machine with experience, rewarding it when it’s correct and withholding the reward when it’s not correct, thus reinforcing good answers. The machine “learns” by making choices that maximizes rewards. In the context of hallucinations, a reward could be a score or signal that indicates that the answer is factual (and it could also be provided by human feedback scores).

Mark Chen continued his response:

“Our models are improving at citing, which grounds their answers in trusted sources, and we also believe that RL will help with hallucinations as well – when we can programmatically check whether models hallucinate, we can reward it for not doing so.”

Does ChatGPT Search Use Bing?

The next question is about what search data does ChatGPT Search use.

The question asked:

“Is ChatGPT Search still using Bing as the search engine behind scenes?”

The answer was provided by Rinivas Narayanan, VP Engineering at OpenAI:

“We use a set of services and Bing is an important one.”

That’s an interesting answer because it’s commonly assumed that Bing is the only search engine. The answer indicated that ChatGPT Search uses multiple “services” and that Bing is the most important. What are the other services that ChatGPT might use? That’s an open question.

What Does OpenAI Say About SEO For ChatGPT Search?

Someone asked the important question about how to optimize content for ChatGPT Search in order to improve rankings. The question was answered by Kevin Weill who said that they were still figuring it out, which could mean that they don’t know or that they’re still figuring out what to say about optimization.

Kevin Weill, Chief Product Officer responded:

“This is a great question—the product just launched today so there’s a lot to figure out still about where search will be similar and where it will be different in an AI world. Would love any feedback you have!”

Takeaways – SEO For ChatGPT Search

Chief Product Officer Kevin Weill is right, these are still the early days of their search and much can still change. The OpenAI Reddit AMA offers first hints at what SEO is growing into.

Other insights:

Bing is the main service ChatGPT Search uses but there are other services it uses as well. That makes Bing an important search engine to rank in.
ChatGPT users are accustomed to natural language interactions and may during the course of their work day use ChatGPT Search.
OpenAI may use Reinforcement Learning at some point to get a better handle on hallucinations.
Personalization may be arriving at some point in the future in the form of a dynamically rendered web page.

Beyond those takeaways is the consideration that OpenAI is not directly competing against Google with a standalone search engine, it has created a completely different experience for searching the web.

Featured Image by Shutterstock/Vitor Miranda

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Oct 30 2024

Streamlining PPC Workflows With AI: How Efficiency Meets Effectiveness via @sejournal, @brookeosmundson

In the fast-paced world of PPC advertising, marketers are constantly seeking ways to streamline their workflows and improve performance.

Managing PPC campaigns efficiently requires a delicate balancing act of multiple tasks:

Analyzing data.
Optimizing bid strategies.
Testing creatives.
Reporting performance.
And so much more.

While AI and machine learning have been around in PPC for years, a new wave of AI tools for streamlining productivity and workflows has made its way into the PPC scene.

Whether it’s automating repetitive tasks, enhancing audience targeting, or analyzing vast datasets, AI tools are reshaping how PPC professionals work.

Who doesn’t want to save time doing repetitive, busy work tasks?

In this article, we’ll explore several unconventional ways AI tools can help PPC marketers save time, increase efficiency, and make smarter decisions.

Using AI To Automate Data Interpretation And Trend Insights

PPC campaigns can generate enormous amounts of data that need to be consistently analyzed and interpreted.

AI tools outside of the standard Google and Microsoft Ads platforms can help streamline this process by helping with tasks like:

Quickly summarizing key trends.
Look for patterns in performance data.
Identify any data anomalies for further analysis.

These insights can enable marketers to move from data to action faster.

Using AI Tools For Trend Identification And Insights

If you’d rather not manually sift through reports identifying changes in performance metrics changes, you can actually feed campaign data into ChatGPT (or similar AI tools) to receive summaries that highlight performance trends.

For example, they can help identify seasonal changes in performance or pinpoint potential issues, such as a sudden dip in conversion rate.

Say you run 20 different campaigns in Google Ads and start to see a significant drop in conversion rates from the platform. It can be daunting to immediately pinpoint the cause of the issue.

By processing raw performance data from your campaigns, these AI tools can quickly analyze the data and provide insight into not only where the problem(s) can lie, but also glean insights as to why performance has shifted, like:

Ad fatigue.
Increased competition.
A shift in consumer behavior.

Using AI tools in this capacity helps marketers cut down on analysis time while helping to identify core issues faster, allowing for quicker optimization.

This automation saves hours of manual work, enabling you to focus on more strategic decision-making instead of spending time analyzing large datasets.

Enhancing Competitor Analysis And Strategy Development

Keeping up with competitors is crucial in the PPC landscape, but the task at hand can be time-consuming and complex.

AI tools simplify this process by providing insights into competitors’ strategies, allowing you to stay one step ahead.

There are plenty of tools to help drive competitor insights, whether in the Google Ads platform, third-party tools, or AI tools.

If you’re looking to take the analysis a step further, you can input reports from other competitive analysis tools into ChatGPT (or a similar tool) to receive a quick summary that highlights a competitor’s recent actions.

For example, this could include information like:

Shifts in bidding strategies.
Introduction of new ad copies.
Keywords being targeted.

Based on this data, the AI tools can suggest ways to adjust your own campaigns or suggest counter-strategies to stay competitive.

By automating competitor analysis tasks, you can gain valuable insights faster, which allows for quicker, more informed decision-making and strategic actions.

Simplifying Multi-Account And Cross-Platform Reporting

Managing campaigns across multiple platforms – whether it’s Google Ads, Microsoft Ads, Meta, or others – means compiling huge data sets from different sources.

Trying to put together a compelling, holistic story about your marketing campaigns can take up a lot of time as you navigate from platform to platform.

This is where the power of AI tools can come in to help aggregate reports and create cohesive summaries.

Streamlining Cross-Platform Reporting

Multi-channel reporting is often a daunting task, especially when managing accounts across Google, Microsoft, and social platforms.

By inputting performance data from these platforms into ChatGPT, marketers can receive a single, unified report that summarizes key performance indicators (KPIs) across channels.

For example, say you manage several campaigns across Google Ads, Microsoft Ads, and Meta Ads.

Instead of switching between dashboards and manually pulling data, you can input the performance metrics from each platform into your AI tool of choice.

The tool can summarize the top-performing platforms, highlight underperforming campaigns, and suggest where to reallocate budgets to maximize ROI.

AI’s ability to consolidate multi-channel data helps reduce reporting time, enabling marketers to spend more time optimizing campaigns and less time on administrative tasks.

Keyword Research And Expansion With AI

Keyword research is at the core of every PPC strategy, and expanding keyword lists can be labor-intensive.

AI tools can make the process more efficient by identifying relevant keywords, negative keywords, and keyword variations that are often missed in traditional tools.

While tools like the Google Keyword Planner are great at providing keyword recommendations, AI tools can take it a step further.

They can generate items like long-tail keyword variations and help identify opportunities for new targeting strategies.

Additionally, they can analyze an existing keyword list and suggest related keywords that reflect user intent or emerging trends.

For example, say you manage PPC campaigns for an ecommerce retailer. You input a list of current top-performing keywords with your latest KPI performance data into your AI tool of choice.

From there, the tool can generate suggestions for new long-tail keywords that may have lower volume, but higher intent to purchase.

Additionally, you can ask the tool to suggest negative keywords to eliminate irrelevant traffic, which improves both relevance and cost efficiency.

To really kick this into high gear, you can then ask the tool to format these new keywords and negative keywords into a format that allows you to upload them into Google Ads Editor, saving you hours of manual work adding each one individually.

Using AI tools beyond the ad platforms can help marketers discover new opportunities faster, ensuring more comprehensive targeting with minimal manual effort.

AI-Assisted Testing And Creative Optimization

There’s no debate that A/B testing is critical to campaign optimization, but interpreting results and making decisions about the next steps is where most people fall flat.

Using AI tools to streamline this process can aid you in analyzing test data and suggest optimizations based on performance.

Say you want to test two different versions of a headline in a PPC campaign. You can upload your test performance data into an AI tool for analysis.

Not only will it summarize which headline performed better, but it goes a step further to help answer why one headline outperformed the other.

By providing insights into which elements contributed to success, it can save you time in the long run and help keep those driving factors top of mind for the next test.

AI For PPC Budget Allocation And Forecasting

Effective budget management is essential for optimizing PPC performance.

The ad platforms are great at automating tasks like changing daily budgets based on scripts, but what about strategic budget allocation decisions?

Using AI tools to assist budget allocation across campaigns or platforms by forecasting potential outcomes based on past performance data can streamline the process of deciding where to invest – and when.

For example, a retail client has an upcoming holiday sale and they want to know if they can expect a higher return than last year’s sale.

Inputting last year’s campaign performance into AI tools like ChatGPT can help analyze performance, while also taking into consideration current market trends.

The output could be to suggest how much of the budget should be allocated to high-performing keywords or certain product categories.

It can also provide a forecast of expected returns based on historical data, current CPC trends, and consumer behavior trends to help you make informed budget decisions ahead of time.

AI-driven budget forecasting helps ensure that resources are allocated to the right areas, reducing wasted spend and improving overall campaign performance.

Automating Market Trend Exploration And Forecasting

Market trends can shift quickly, and staying ahead of these changes is key to successful PPC campaigns.

AI tools can analyze search trends, consumer behavior, and historical campaign data to predict future shifts in demand and help marketers prepare.

For instance, AI tools can identify trends in consumer searches in real time, helping you adjust your campaign strategies proactively.

For example, you manage Google Ads campaigns for a fitness brand, and you’re noticing a seasonal uptick in searches for [home workout equipment].

By using AI tools to analyze Google Trends data, you can forecast how that demand will continue to rise or fall in the coming months, and even if certain geographical areas are driving the high demand.

This allows you to adjust bids based on location, increase overall budgets if necessary to help capture demand, and create relevant ad copy that speaks directly to the emerging trend.

Conclusion

AI is revolutionizing PPC workflows, allowing marketers to work smarter, not harder.

Whether you’re leveraging Google Ads’ AI capabilities, like Gemini’s conversational ad creation or integrating third-party tools for deeper insights, AI is becoming indispensable in managing and optimizing PPC campaigns.

From automating bid management and audience targeting to optimizing ad creatives and providing actionable insights, AI offers opportunities to boost efficiency without sacrificing effectiveness.

As AI tools continue to evolve, those who embrace these technologies will find themselves better equipped to deliver superior results, whether managing in-house campaigns or serving clients.

By integrating both Google’s AI features and powerful third-party tools, you can unlock new levels of performance, save time on manual tasks, and focus on strategy and innovation.

More resources:

Featured Image: 3rdtimeluckystudio/Shutterstock

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Oct 30 2024

Meta Takes Step To Replace Google Index In AI Search via @sejournal, @martinibuster

Meta is reportedly developing a search engine index for its AI chatbot to reduce reliance on Google for AI-generated summaries of current events. Meta AI appears to be evolving to the next stage of becoming a fully independent AI search engine.

Meta-ExternalAgent

Meta has been crawling the Internet since at least this past summer from a user agent called, Meta-ExternalAgent. There have been multiple reports in various forums about excessive amounts of crawling with one person on Hacker News reporting having received 50,000 hits by the bot. A post in the WebmasterWorld bot crawling forum notes that although the documentation for Meta-ExternalAgent says it respects robots.txt it wouldn’t have made a difference because the bot never visited the file.

It may be that the bot wasn’t fully ready earlier this year and that it’s poor behavior has settled down.

The purpose of the bot is to summarize search results and according to the results it’s to reduce reliance on Google and Bing for search results.

Is This A Challenge To Google?

It may be possible that this is indeed a the prelude to a challenge to Google (and other search engines) in AI search. The information at this time supports that this is about creating a search index to complement their Meta AI. As reported in The Verge, Meta is crawling sites for search summaries to be used within the Meta AI Chatbot:

“The search engine would reportedly provide AI-generated search summaries of current events within the Meta AI chatbot.”

The Meta AI chatbot looks like a search engine and it’s clear that it’s still using Google’s search index.

For example, a search t Meta AI about the recent game four of the World Series showed a summary with an accurate answer that had a link to Google.

Screenshot Of Meta AI With Link To Google Search

Here’s a close up showing the link to Google search results and a link to the sources:

Screenshot Of Close-Up Of Meta AI Results

Clicking on the View Sources button spawns a popup with links to Google Search.

Screenshot Of Meta AI View Sources Pop-Up

Read the original reports:

A report was posted in The Verge, based on another reported published on The Information.

See also:

Featured Image by Shutterstock/Skorzewiak

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Oct 28 2024

Google Expands AI Overviews In Search To Over 100 Countries via @sejournal, @MattGSouthern

Google expands AI-powered search summaries globally, now reaching over 100 countries with support for six different languages.

Google’s AI Overviews is expanding from US-only to over 100 countries, reaching 1 billion monthly users.
The feature now supports six languages and includes new ways to display website links.
Google has started showing ads in AI Overviews for US mobile users.

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Oct 23 2024

[B2C Marketers] 5 Tips To Drive More Revenue With Google Ads AI via @sejournal, @invoca

This post was sponsored by Invoca. The opinions expressed in this article are the sponsor’s own.

In today’s marketing world, AI is more than a buzzword — it’s a necessity.

Nearly 90% of marketers plan to increase their investment in AI this year, primarily focusing on boosting their Return on Ad Spend (ROAS).

If you’re not using AI to maximize your ad budget, chances are your competitors are, which could leave you behind.

But don’t worry — there are plenty of AI tools to help you get more from your campaigns, boost productivity, and drive revenue growth without spending more on ads. One of the most impactful marketing tools is Google Ads Smart Bidding.

In this post, we’ll break down five essential Smart Bidding strategies that can help you drive more revenue.

Want the tips without reading? Check out the video series >>>

We’ll also explore how pairing Google’s AI with a revenue execution platform can elevate your ad performance.

What Is Google Ads Smart Bidding?

Google Ads Smart Bidding is an AI-driven tool that automatically adjusts bids in real-time to help you hit your campaign goals.

Its strength lies in its ability to analyze patterns and trends far quicker than any human could.

By optimizing your budget and freeing up your team for other high-value projects, Smart Bidding helps you focus on what matters most: growing revenue.

5 Tips to Drive Revenue With Google Ads Smart Bidding

Want the tips without reading? Check out the video series >>>

1. Align Your Bidding Strategy With Revenue Goals

Google Ads Smart Bidding offers multiple options tailored to different campaign objectives. Choosing the right strategy depends on your specific goals and budget. Here are a few:

Maximize Conversions: This Smart Bidding strategy sets bids to maximize the number of actions taken by users, such as sign-ups, purchases, or form submissions. It is ideal if you want to drive more actions like form fills, sign-ups, or purchases.
Target CPA (Cost Per Acquisition): With the target cost per acquisition (CPA) strategy, you specify the amount you’re willing to spend to acquire a customer. Google Ads then automatically sets bids to achieve that desired CPA. This strategy is best for maintaining cost efficiency by acquiring customers at a specific price.
Target ROAS: The target ROAS strategy allows you to set a specific ROAS goal, and Google Ads adjusts bids based on expected conversion values. If maximizing revenue while maintaining a specific ROAS is your priority, this is your go-to strategy.
Enhanced Conversions: You can use Enhanced Conversions to optimize for specific actions or events that hold significant value for your business. This strategy leverages machine learning to predict and adjust bids based on the likelihood of driving valuable conversions, improving the overall return on ad spend, and enhancing the efficiency of your marketing campaigns. According to Google, marketers who use this strategy see a 5% average conversion rate improvement on Search.

The key is continuously monitoring performance and adjusting to hit your revenue targets.

2. Use Advanced AI Tools To Reach More Customers

Google offers new AI tools to take your Smart Bidding strategy to the next level, helping you expand your reach. You can pair these tools with your desired bidding strategy.

Here’s what they are and how they work:

Broad Match: Use this tool to capture a wider audience by covering related searches and synonyms. Craft a comprehensive keyword list, incorporating broad-match keywords to increase visibility and attract potential customers who may use different search terms. According to Google, marketers who use Broad Match in Target CPA campaigns see 35% more conversions, on average.
Performance Max: This AI-powered tool optimizes your campaigns across all Google networks (YouTube, Google Maps, etc.) and ad formats to maximize results. With Performance Max, the AI technology automatically adjusts bids to achieve the best possible results, making it ideal for driving conversions and optimizing ad spend across Google’s expansive network. According to Google, marketers who use Performance Max achieve 18% more conversions at a similar cost per action. By pairing Broad Match with your chosen Smart Bidding model, you can maximize your query coverage on Google search.

By combining Broad Match with Performance Max, you’ll significantly increase your reach and boost conversions.

3. Use Revenue Execution Platforms To Supercharge Smart Bidding

AI is only as good as the data it’s fed, and many marketers miss a crucial piece of the puzzle: phone call conversions.

This can be a significant problem, as our research shows that 20-50% of conversions come in over the phone in many high-stakes purchase industries like healthcare, home services, automotive, and telecommunications.

If you’re not tracking all of those phone call conversions, your Google Smart Bidding instance is likely underperforming. That’s because automated bidding tools track the number of conversions each ad variation drives and then optimize bids based on what’s performing best. If you’re not tracking the phone call conversions your ads drive, you’re not giving the tool a complete picture of your performance.

Illustration, Invoca, October 2024

Illustration, Invoca, October 2024

A revenue execution platform like Invoca allows you to track these call conversions and feed them directly into Google Ads. This enables Google’s Smart Bidding AI to optimize more effectively, ensuring your ad dollars are spent on what truly drives revenue.

Check out this video series, to learn more about revenue execution platforms.

Illustration, Invoca, October 2024

4. Optimize Retargeting With Rich Data Insights

Retargeting is an incredibly cost-effective way to drive more conversions, especially when you’re targeting people who have already interacted with your brand. To enhance your retargeting efforts, first-party data is key — and phone conversations are a treasure trove of insights that can be unlocked with revenue execution platforms like Invoca.

Phone conversations contain more insights than an online form fill ever could — when your customers call you, they tell you about their needs, preferences, and how to make them happy. Invoca’s AI analyzes these conversations at scale and mines them for insights. The beauty of it is that you can easily train the AI to capture whichever data points are most relevant to your business — for example, you can track products callers expressed interest in, if they were price-sensitive, and if they made a purchase.

Check out the graphic below to see more of the data points you can collect with Invoca:

Illustration, Invoca, October 2024

With these deep conversation insights, you can build more complete customer profiles and retarget leads with more relevant ads. Below are a few common examples of retargeting and suppression strategies marketers use with Invoca’s first-party data:

Retarget callers who didn’t make a purchase with ads for the products they mentioned over the phone.
Retarget callers who bought over the phone with ads for relevant companion purchases.
Retarget callers who expressed price sensitivity with ads touting a special discount code.
Suppress callers who bought over the phone from seeing future ads for that product or service.

5. Detect & Solve Call Experience Issues

Many marketers lose potential revenue because they aren’t aware of call experience issues—missed calls, long hold times, or unoptimized call scripts that don’t convert leads. You could be flushing good leads down the drain without even knowing it. Using a revenue execution platform, you get detailed reports on call handling and identify areas where improvements are needed.

Invoca shows you the total number of calls your Google Ads campaigns send to each location or contact center, the number of calls answered, the name of the agent who handled the call, the number of leads, and the number of calls successfully converted to revenue.

If you notice specific locations or contact centers have high unanswered call rates, you can collaborate with them to improve call routing procedures and staffing. If you learn that some agents have low phone call conversion rates, you can review their call recordings and transcripts to learn the cause and notify their managers to help them improve.

You’ll increase conversion rates and revenue from your Google Ads campaigns when you work with your contact centers and locations to correct these issues.

Below is a sample Invoca report showing call handling by location:

Illustration, Invoca, October 2024

Addressing these issues, from ensuring calls are answered promptly to refining sales scripts, can lead to better conversion rates and higher revenue from your ad campaigns.

By following these five tips and integrating a revenue execution platform, B2C marketers can fully take advantage of Google’s AI capabilities, driving conversions and revenue from every marketing dollar spent.

Ready to learn more about how Invoca’s AI-powered revenue execution platform can help you level up your marketing? Check out this video series to see how it’s done.

WATCH THE VIDEO SERIES

Image Credits

Featured Image: Image by Invoca. Used with permission.

In-Post Image: Images by Invoca. Used with permission.

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Oct 21 2024

Google’s AI Fails At 43% Of Finance Queries, Study Finds via @sejournal, @MattGSouthern

A study by The College Investor finds significant inaccuracies in Google’s AI-generated summaries for finance queries.

Out of 100 personal finance searches, 43% had misleading or incorrect information.

Key Findings

The study evaluated AI overviews across various financial topics, including banking, credit, investing, taxes, and student loans.

The results showed:

57% of AI overviews were accurate
43% contained misleading or inaccurate information
12% were completely incorrect
31% were either misleading or missing crucial details

Areas of Concern

Researchers noted that the AI struggled most with nuanced financial topics, such as taxes, investing, and student loans.

Some of the most concerning issues included:

Outdated information on student loan repayment plans
Incorrect details about IRA contribution limits
Misleading statements regarding 529 college savings plans
Inaccurate tax information that could potentially lead to penalties if followed

The AI handled basic financial concepts well but overlooked important exceptions and recent policy changes.

There are notable patterns in the queries Google’s AI got right versus those it got wrong.

Here are common themes.

Queries Google AI Got Right

Basic definitions and explanations: For example, “What is a wire transfer?” and “How does a credit card work?”
Simple, straightforward questions: Such as “Do I have to pay back student loans?”
Recent trending topics: Like “What was the Chase Glitch?”
General insurance questions: For instance, “When should I get life insurance?”

Queries Google AI Got Wrong

Complex tax topics: For example, “Can you use a 529 plan for a Roth IRA?” and “Does owning your house in an LLC help with taxes?”
Nuanced financial products: Such as “Is an IUL better than a 401k?”
Time-sensitive information: Like outdated student loan repayment plans or savings account rates.
State-specific financial rules: For instance, misrepresenting California’s 529 plan rules.
Questions requiring context-dependent answers: Such as “Can I file as independent for FAFSA?”
Queries about financial limits or thresholds: For example, incorrect IRA contribution limits.
Complex student loan topics: Particularly around forgiveness programs and repayment plans.
Investment comparisons: Like “Are annuities better than CDs?”

What This Means

Google’s AI performs well at giving straightforward answers to factual queries.

On the other hand, it struggles with nuanced understanding, up-to-date information, and consideration of multiple factors.

This suggests that the AI can handle basic financial literacy topics, but it’s unreliable for complex financial decisions or advice.

Potential Impact

Robert Farrington, founder of The College Investor, expressed concern about the findings, stating:

“If Google continues to present bad or misinformation about money topics to searchers, not only could it hurt their personal finances, but it could weaken already poor financial literacy in the United States.”

The study noted that following AI guidance could result in tax penalties or financial harm to consumers.

The College Investor believes Google should disable these AI-generated overviews for finance-related queries, especially those concerning taxes and investments.

Looking Ahead

Searchers must exercise caution when relying on AI-generated summaries for financial decisions.

When questioned about instances of misinformation, Google has previously stated, “the vast majority of AI Overviews provide high-quality information.”

The complete study, including detailed examples and methodology, is available on The College Investor’s website.

Featured Image: Koshiro K/Shutterstock

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Oct 10 2024

How To Get Quality SEO Content Out Of Generative AI [Checklist] via @sejournal, @DAC_group

This post was sponsored by DAC Group. The opinions expressed in this article are the sponsor’s own.

With its ability to rapidly produce content at scale, generative AI has quickly become a pivotal content creation tool for any brand trying to maximize its visibility, engagement, and performance online.

However, while AI significantly reduces the time from ideation to creation, the real challenge has become clear: How do you make sure the content it generates is relevant, resonates with your brand’s voice, and drives measurable SEO gains?

This is where the careful combination of AI’s capabilities and human expertise becomes critical. By leveraging AI for its strengths in processing and content generation while applying human insights to refine and guide these outputs, you can strike a balance that achieves both efficiency and quality.

In this article, you’ll explore actionable strategies that combine AI’s rapid output with human creativity, enabling you to produce SEO-optimized content that truly connects with your target audiences. Then you can use the checklist below to create a process for AI in your SEO workflows.

Blending AI With Human Expertise In SEO

At its core, AI’s strength is its ability to process vast amounts of data quickly. When it comes to keyword and topic research, AI can analyze thousands of keywords in seconds, identifying patterns and uncovering trending themes. This capability empowers SEO experts to spot opportunities that might otherwise be missed and prioritize topics that are more likely to resonate with their audience.

For instance, AI can help in:

Analyzing large data sets to find keyword patterns.
Identifying popular topics and emerging trends through large-scale natural language processing (NLP).
Prioritizing topics based on search volume and relevance.

Yet human expertise remains indispensable in interpreting the data AI produces. AI might identify a keyword with high search volume, for instance, but only a human expert can determine if that keyword aligns with a brand’s message and audience’s intent.

With human analysts bringing critical thinking, contextual understanding, and the ability to interpret subtle nuances that AI might miss, this is a collaboration built to make data-driven decisions that strategically align with business goals.

For more sophisticated semantic analyses, you can leverage AI’s ability to perform advanced topic clustering. By utilizing models like sentence transformers, AI can understand and group similar ideas, helping SEO specialists identify overarching themes and subtopics—leading to the creation of comprehensive content recommendations that cover key topics from multiple angles, thus boosting SEO coverage and performance.

Leveraging AI For Strategic Content Ideation And Planning

AI’s ability to quickly generate content ideas makes it a powerful tool for content strategy. By feeding AI data on audience behavior, brand guidelines, and the aforementioned SEO trends and insights, you can produce a wealth of content ideas in a fraction of the time it would take manually. However, it’s important to view AI’s output as a starting point rather than a final product.

By layering AI into your content strategy processes, you can:

Rapidly generate a wide array of content ideas.
Use audience and SEO data to inform and enrich content ideation.
Brainstorm high volumes of original content angles.

Content strategists play a crucial role in fine-tuning these AI-generated ideas, directing content to make sure it aligns with a brand’s overall market strategy and audience expectations. This process may involve assessing AI suggestions—produced rapidly in an organized format—for their potential to meet business objectives, refining content briefs, and proposing content initiatives that integrate SEO opportunities identified earlier.

You can ensure a symbiotic, collaborative use of AI with the following approach to content ideation:

Strategy is also the bridge between SEO insights and creative execution to ensure that the resulting content recommendations are both data-informed and strategically sound. This step is essential to create content that resonates with your intended audiences while simultaneously fulfilling your business’s strategic goals.

Ensuring Quality And Consistency In AI-Generated Creative Content

Generative AI excels in speed, making it an invaluable tool for brainstorming ideas and generating serviceable first drafts. Even so, its outputs can be repetitive, unoriginal, inaccurate, and may lack the nuanced voice of a brand.

To mitigate these weaknesses, remember that generated content is only a starting point. Even when an AI model has been extensively trained and all the major kinks worked out, it’s not perfect. Human oversight and intervention are essential to refine the output for human audiences.

This is where copywriters and editors step in to finesse the content, applying edits to ensure it aligns with a brand’s tone and style. In addition to paraphrasing repetitive structures and adding the “human touch” throughout, refinements in this final step may include:

Reviewing for natural phrasing to ensure keywords are integrated smoothly.
Adjusting tone and vocabulary to capture the brand’s voice more accurately.
Correcting any factual errors, unsubstantiated claims, or AI hallucinations.
Enhancing engagement by making the content more audience-focused

This emerging process is beginning to transform copywriting as a discipline. Writers working with AI are likely to spend less time creating first drafts and more time editing, fine-tuning, and curating AI-generated content for human audiences. The result, in theory, is higher quality content produced far more rapidly than traditional methods.

To maximize the benefits of AI in content creation, it’s essential to establish a feedback loop that joins the dots between SEO, strategy, and creative. Content creators should regularly review AI outputs and provide feedback, helping the system improve over time by refining the AI’s training data, experimenting with its parameters, or even rethinking how AI is integrated into the content creation process. This culture of continuous refinement can enhance the quality of your AI-assisted content while minimizing its shortcomings.

The Future Of AI In Content Creation And SEO

Generative AI has already begun to revolutionize content creation, particularly for brands that have integrated it into well-structured content strategies supported by human expertise. By following the best practices outlined in this guide, you can leverage AI to produce SEO-optimized content that not only enhances your online presence but can help you carve out your position as a thought leader.

As you explore these strategies, consider how DAC can support your enterprise-level content needs with scalable AI-driven solutions. By blending the strengths of AI with the critical insights of human experts in SEO, content strategy, and creative copywriting, your business can create content that resonates with your audience, ranks well in search engines, and drives measurable results.

Explore DAC’s Forward-thinking solutions

Image Credits

Featured Image: Image by LookerStudio/Shutterstock. Used with permission.

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Oct 4 2024

Google’s AI Overviews Avoid Political Content, New Data Shows via @sejournal, @MattGSouthern

Study reveals Google’s cautious approach to AI-generated content in sensitive search results, varying across health, finance, legal, and political topics.

Google shows AI Overviews for 50% of YMYL topics, with legal queries triggering them most often.
Health and finance AI Overviews frequently include disclaimers urging users to consult professionals.
Google avoids generating AI Overviews for sensitive topics like mental health, elections, and specific medications.

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Sep 24 2024

AI On Innovation [Part 2]: More Insights From +546,000 AI Overviews via @sejournal, @Kevin_Indig

Following up on my first analysis of +546,000 AI Overviews, I dug deeper into three questions:

How are common crawl data and AI Overviews related?
How does user intent change AI Overviews?
How do the top 20 positions break down for domains that rank in organic search and get cited in AIOs?

How Are Common Crawl Data And AI Overviews Related?

Common crawl inclusion doesn’t affect AIO visibility as much as sheer organic traffic.

Common Crawl, a non-profit that crawls the web and provides the data for free, is the largest data source of generative AI training.

Some sites, like Blogspot, contribute a lot more pages than others, raising the question of whether that gives them an edge in LLM answers.

Result: I wondered whether sites that provide more pages than others would also see more visibility in AI Overviews. That turned out not to be true.

I compared the top 500 domains by page contribution in Common Crawl to the top 30,000 domains in my dataset and found a weak correlation of 0.179.

The reason is that Google probably doesn’t rely on Common Crawl to train and inform AI Overviews but its own index.

Relationship between AIO citations and organic traffic

Image Credit: Kevin Indig

I then analyzed the relationship between the 3,000 top domains by organic traffic from Semrush and the top 30,000 domains in my dataset and found a strong relationship of 0.714.

In other words, domains that get a lot of organic traffic have a high likelihood of being very visible in AI Overviews.

AIO seems to increasingly reward what works in organic search, but some criteria are still very separate.

It’s important to call out that a few sites distort the relationship.

When filtering out Wikipedia and YouTube, the relationship goes down to a correlation of 0.485 – still strong but lower than with the two behemoths.

The correlation doesn’t change when taking out bigger sites, solidifying the point that doing things that work in organic search has a big impact on AI Overviews.

As I wrote in my previous post:

Ranking higher in the search results certainly increases the chances of being visible in AIOs, but it’s by far not the only factor.

As a result, companies can exclude Common Crawl’s bot in robots.txt if they don’t want to appear in public datasets (and gen AI like Chat GPT) and still be very visible in Google’s AI Overviews.

How Does User Intent Change AI Overviews?

User intent shapes the form and content of AIOs.
In my previous analysis, I came to the conclusion that the exact query match barely matters:

The data shows that only 6% of AIOs contain the search query.

That number is slightly higher in SGE, at 7%, and lower in live AIOs, at 5.1%. As a result, meeting user intent in the content is much more important than we might have assumed. This should not come as a surprise since user intent has been a key ranking requirement in SEO for many years, but seeing the data is shocking.

Calculating exact (dominant) user intent for all 546,000 queries would be extremely compute-intense, so I looked at the common abstractions informational, local, and transactional.

Abstractions are less helpful when optimizing content, but they’re fine when looking at aggregate data.

I clustered:

Informational queries around question words like “what,” “why,” “when,” etc.
Transactional queries around terms like “buy,” “download,” “order,” etc.
Local queries around “nearby,” “close,” or “near me.”

AIO answer contains query by user intent

Image Credit: Kevin Indig

Result: User intent differences reflect in form and function. The average length (word count) is almost equal across all intents except for local, which makes sense because users want a list of locations instead of text.

Similarly, shopping AIOs are often lists of products with a bit of context unless they’re shopping-related questions.

Local queries have the highest amount of exact match overlap between query and answer; informational queries have the lowest.

Understanding and satisfying user intent for questions is harder but also more important to be visible in AIOs than, for example, Featured Snippets.

How Do The Top 20 Organic Positions Break Down?

In my last analysis, I found that almost 60% of URLs that appear in AIOs and organic search results rank outside the top 20 positions.

For this Memo, I broke the top 20 further down to understand if AIOs are more likely to cite URLs in higher positions or not.

Breakdown of top 20 search results for URLs that are also AIO citations

Image Credit: Kevin Indig

Result: It turns out 40% of URLs in AIOs rank in positions 11-20, and only half (21.9%) rank in the top 3.

The majority, 60% of URLs cited in AIOs, still rank on the first page of organic results, reinforcing the point that a higher organic rank tends to lead to a higher chance of being cited in AIOs.

However, the data also shows that it’s very much impossible to be present in AIOs with a lower organic rank.

Where the top 20 domains that are visible in AIOs and search results rank (Image Credit: Kevin Indig)

Scenarios

I will work with my clients to match the AIO’s user intent, provide unique insights, and tailor the format. I see options for the progress of AI Overview that I will track and validate with data in the next months and years.

Option 1: AIOs rely more on top-ranking organic results and satisfy more informational intent before users need to click through to websites. The majority of clicks landing on sites would be from users considering or intending to buy.

Option 2: AIOs continue to provide answers from diversified results and leave a small chance that users still click through to top-ranking results, albeit in much smaller amounts.

Which scenario are you betting on?

Featured Image: Paulo Bobita/Search Engine Journal

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Sep 12 2024

OpenAI Claims New “o1” Model Can Reason Like A Human via @sejournal, @MattGSouthern

OpenAI has unveiled its latest language model, “o1,” touting advancements in complex reasoning capabilities.

In an announcement, the company claimed its new o1 model can match human performance on math, programming, and scientific knowledge tests.

However, the true impact remains speculative.

Extraordinary Claims

According to OpenAI, o1 can score in the 89th percentile on competitive programming challenges hosted by Codeforces.

The company insists its model can perform at a level that would place it among the top 500 students nationally on the elite American Invitational Mathematics Examination (AIME).

Further, OpenAI states that o1 exceeds the average performance of human subject matter experts holding PhD credentials on a combined physics, chemistry, and biology benchmark exam.

These are extraordinary claims, and it’s important to remain skeptical until we see open scrutiny and real-world testing.

Reinforcement Learning

The purported breakthrough is o1’s reinforcement learning process, designed to teach the model to break down complex problems using an approach called the “chain of thought.”

By simulating human-like step-by-step logic, correcting mistakes, and adjusting strategies before outputting a final answer, OpenAI contends that o1 has developed superior reasoning skills compared to standard language models.

Implications

It’s unclear how o1’s claimed reasoning could enhance understanding of queries—or generation of responses—across math, coding, science, and other technical topics.

From an SEO perspective, anything that improves content interpretation and the ability to answer queries directly could be impactful. However, it’s wise to be cautious until we see objective third-party testing.

OpenAI must move beyond benchmark browbeating and provide objective, reproducible evidence to support its claims. Adding o1’s capabilities to ChatGPT in planned real-world pilots should help showcase realistic use cases.

Featured Image: JarTee/Shutterstock

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