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Everything You Need To Know

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Everything You Need To Know

Google has just released Bard, its answer to ChatGPT, and users are getting to know it to see how it compares to OpenAI’s artificial intelligence-powered chatbot.

The name ‘Bard’ is purely marketing-driven, as there are no algorithms named Bard, but we do know that the chatbot is powered by LaMDA.

Here is everything we know about Bard so far and some interesting research that may offer an idea of the kind of algorithms that may power Bard.

What Is Google Bard?

Bard is an experimental Google chatbot that is powered by the LaMDA large language model.

It’s a generative AI that accepts prompts and performs text-based tasks like providing answers and summaries and creating various forms of content.

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Bard also assists in exploring topics by summarizing information found on the internet and providing links for exploring websites with more information.

Why Did Google Release Bard?

Google released Bard after the wildly successful launch of OpenAI’s ChatGPT, which created the perception that Google was falling behind technologically.

ChatGPT was perceived as a revolutionary technology with the potential to disrupt the search industry and shift the balance of power away from Google search and the lucrative search advertising business.

On December 21, 2022, three weeks after the launch of ChatGPT, the New York Times reported that Google had declared a “code red” to quickly define its response to the threat posed to its business model.

Forty-seven days after the code red strategy adjustment, Google announced the launch of Bard on February 6, 2023.

What Was The Issue With Google Bard?

The announcement of Bard was a stunning failure because the demo that was meant to showcase Google’s chatbot AI contained a factual error.

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The inaccuracy of Google’s AI turned what was meant to be a triumphant return to form into a humbling pie in the face.

Google’s shares subsequently lost a hundred billion dollars in market value in a single day, reflecting a loss of confidence in Google’s ability to navigate the looming era of AI.

How Does Google Bard Work?

Bard is powered by a “lightweight” version of LaMDA.

LaMDA is a large language model that is trained on datasets consisting of public dialogue and web data.

There are two important factors related to the training described in the associated research paper, which you can download as a PDF here: LaMDA: Language Models for Dialog Applications (read the abstract here).

  • A. Safety: The model achieves a level of safety by tuning it with data that was annotated by crowd workers.
  • B. Groundedness: LaMDA grounds itself factually with external knowledge sources (through information retrieval, which is search).

The LaMDA research paper states:

“…factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator.

We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.”

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Google used three metrics to evaluate the LaMDA outputs:

  1. Sensibleness: A measurement of whether an answer makes sense or not.
  2. Specificity: Measures if the answer is the opposite of generic/vague or contextually specific.
  3. Interestingness: This metric measures if LaMDA’s answers are insightful or inspire curiosity.

All three metrics were judged by crowdsourced raters, and that data was fed back into the machine to keep improving it.

The LaMDA research paper concludes by stating that crowdsourced reviews and the system’s ability to fact-check with a search engine were useful techniques.

Google’s researchers wrote:

“We find that crowd-annotated data is an effective tool for driving significant additional gains.

We also find that calling external APIs (such as an information retrieval system) offers a path towards significantly improving groundedness, which we define as the extent to which a generated response contains claims that can be referenced and checked against a known source.”

How Is Google Planning To Use Bard In Search?

The future of Bard is currently envisioned as a feature in search.

Google’s announcement in February was insufficiently specific on how Bard would be implemented.

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The key details were buried in a single paragraph close to the end of the blog announcement of Bard, where it was described as an AI feature in search.

That lack of clarity fueled the perception that Bard would be integrated into search, which was never the case.

Google’s February 2023 announcement of Bard states that Google will at some point integrate AI features into search:

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.

These new AI features will begin rolling out on Google Search soon.”

It’s clear that Bard is not search. Rather, it is intended to be a feature in search and not a replacement for search.

What Is A Search Feature?

A feature is something like Google’s Knowledge Panel, which provides knowledge information about notable people, places, and things.

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Google’s “How Search Works” webpage about features explains:

“Google’s search features ensure that you get the right information at the right time in the format that’s most useful to your query.

Sometimes it’s a webpage, and sometimes it’s real-world information like a map or inventory at a local store.”

In an internal meeting at Google (reported by CNBC), employees questioned the use of Bard in search.

One employee pointed out that large language models like ChatGPT and Bard are not fact-based sources of information.

The Google employee asked:

“Why do we think the big first application should be search, which at its heart is about finding true information?”

Jack Krawczyk, the product lead for Google Bard, answered:

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“I just want to be very clear: Bard is not search.”

At the same internal event, Google’s Vice President of Engineering for Search, Elizabeth Reid, reiterated that Bard is not search.

She said:

“Bard is really separate from search…”

What we can confidently conclude is that Bard is not a new iteration of Google search. It is a feature.

Bard Is An Interactive Method For Exploring Topics

Google’s announcement of Bard was fairly explicit that Bard is not search. This means that, while search surfaces links to answers, Bard helps users investigate knowledge.

The announcement explains:

“When people think of Google, they often think of turning to us for quick factual answers, like ‘how many keys does a piano have?’

But increasingly, people are turning to Google for deeper insights and understanding – like, ‘is the piano or guitar easier to learn, and how much practice does each need?’

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Learning about a topic like this can take a lot of effort to figure out what you really need to know, and people often want to explore a diverse range of opinions or perspectives.”

It may be helpful to think of Bard as an interactive method for accessing knowledge about topics.

Bard Samples Web Information

The problem with large language models is that they mimic answers, which can lead to factual errors.

The researchers who created LaMDA state that approaches like increasing the size of the model can help it gain more factual information.

But they noted that this approach fails in areas where facts are constantly changing during the course of time, which researchers refer to as the “temporal generalization problem.”

Freshness in the sense of timely information cannot be trained with a static language model.

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The solution that LaMDA pursued was to query information retrieval systems. An information retrieval system is a search engine, so LaMDA checks search results.

This feature from LaMDA appears to be a feature of Bard.

The Google Bard announcement explains:

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence, and creativity of our large language models.

It draws on information from the web to provide fresh, high-quality responses.”

Screenshot of a Google Bard Chat, March 2023

LaMDA and (possibly by extension) Bard achieve this with what is called the toolset (TS).

The toolset is explained in the LaMDA researcher paper:

“We create a toolset (TS) that includes an information retrieval system, a calculator, and a translator.

TS takes a single string as input and outputs a list of one or more strings. Each tool in TS expects a string and returns a list of strings.

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For example, 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 information retrieval system is also capable of returning snippets of content from the open web, with their corresponding URLs.

The TS tries an input string on all of its tools, and produces a final output list of strings by concatenating the output lists from every tool in the following order: calculator, translator, and information retrieval system.

A tool will return an empty list of results if it can’t parse the input (e.g., the calculator cannot parse ‘How old is Rafael Nadal?’), and therefore does not contribute to the final output list.”

Here’s a Bard response with a snippet from the open web:

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Google Bard: Everything You Need To KnowScreenshot of a Google Bard Chat, March 2023

Conversational Question-Answering Systems

There are no research papers that mention the name “Bard.”

However, there is quite a bit of recent research related to AI, including by scientists associated with LaMDA, that may have an impact on Bard.

The following doesn’t claim that Google is using these algorithms. We can’t say for certain that any of these technologies are used in Bard.

The value in knowing about these research papers is in knowing what is possible.

The following are algorithms relevant to AI-based question-answering systems.

One of the authors of LaMDA worked on a project that’s about creating training data for a conversational information retrieval system.

You can download the 2022 research paper as a PDF here: Dialog Inpainting: Turning Documents into Dialogs (and read the abstract here).

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The problem with training a system like Bard is that question-and-answer datasets (like datasets comprised of questions and answers found on Reddit) are limited to how people on Reddit behave.

It doesn’t encompass how people outside of that environment behave and the kinds of questions they would ask, and what the correct answers to those questions would be.

The researchers explored creating a system read webpages, then used a “dialog inpainter” to predict what questions would be answered by any given passage within what the machine was reading.

A passage in a trustworthy Wikipedia webpage that says, “The sky is blue,” could be turned into the question, “What color is the sky?”

The researchers created their own dataset of questions and answers using Wikipedia and other webpages. They called the datasets WikiDialog and WebDialog.

  • WikiDialog is a set of questions and answers derived from Wikipedia data.
  • WebDialog is a dataset derived from webpage dialog on the internet.

These new datasets are 1,000 times larger than existing datasets. The importance of that is it gives conversational language models an opportunity to learn more.

The researchers reported that this new dataset helped to improve conversational question-answering systems by over 40%.

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The research paper describes the success of this approach:

“Importantly, we find that our inpainted datasets are powerful sources of training data for ConvQA systems…

When used to pre-train standard retriever and reranker architectures, they advance state-of-the-art across three different ConvQA retrieval benchmarks (QRECC, OR-QUAC, TREC-CAST), delivering up to 40% relative gains on standard evaluation metrics…

Remarkably, we find that just pre-training on WikiDialog enables strong zero-shot retrieval performance—up to 95% of a finetuned retriever’s performance—without using any in-domain ConvQA data. “

Is it possible that Google Bard was trained using the WikiDialog and WebDialog datasets?

It’s difficult to imagine a scenario where Google would pass on training a conversational AI on a dataset that is over 1,000 times larger.

But we don’t know for certain because Google doesn’t often comment on its underlying technologies in detail, except on rare occasions like for Bard or LaMDA.

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Large Language Models That Link To Sources

Google recently published an interesting research paper about a way to make large language models cite the sources for their information. The initial version of the paper was published in December 2022, and the second version was updated in February 2023.

This technology is referred to as experimental as of December 2022.

You can download the PDF of the paper here: Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models (read the Google abstract here).

The research paper states the intent of the technology:

“Large language models (LLMs) have shown impressive results while requiring little or no direct supervision.

Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios.

We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting.

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We formulate and study Attributed QA as a key first step in the development of attributed LLMs.

We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures.

We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.

Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?).”

This kind of large language model can train a system that can answer with supporting documentation that, theoretically, assures that the response is based on something.

The research paper explains:

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“To explore these questions, we propose Attributed Question Answering (QA). In our formulation, the input to the model/system is a question, and the output is an (answer, attribution) pair where answer is an answer string, and attribution is a pointer into a fixed corpus, e.g., of paragraphs.

The returned attribution should give supporting evidence for the answer.”

This technology is specifically for question-answering tasks.

The goal is to create better answers – something that Google would understandably want for Bard.

  • Attribution allows users and developers to assess the “trustworthiness and nuance” of the answers.
  • Attribution allows developers to quickly review the quality of the answers since the sources are provided.

One interesting note is a new technology called AutoAIS that strongly correlates with human raters.

In other words, this technology can automate the work of human raters and scale the process of rating the answers given by a large language model (like Bard).

The researchers share:

“We consider human rating to be the gold standard for system evaluation, but find that AutoAIS correlates well with human judgment at the system level, offering promise as a development metric where human rating is infeasible, or even as a noisy training signal. “

This technology is experimental; it’s probably not in use. But it does show one of the directions that Google is exploring for producing trustworthy answers.

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Research Paper On Editing Responses For Factuality

Lastly, there’s a remarkable technology developed at Cornell University (also dating from the end of 2022) that explores a different way to source attribution for what a large language model outputs and can even edit an answer to correct itself.

Cornell University (like Stanford University) licenses technology related to search and other areas, earning millions of dollars per year.

It’s good to keep up with university research because it shows what is possible and what is cutting-edge.

You can download a PDF of the paper here: RARR: Researching and Revising What Language Models Say, Using Language Models (and read the abstract here).

The abstract explains the technology:

“Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.

However, they sometimes generate unsupported or misleading content.

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A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence.

To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible.

…we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.

Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.”

How Do I Get Access To Google Bard?

Google is currently accepting new users to test Bard, which is currently labeled as experimental. Google is rolling out access for Bard here.

Google Bard is ExperimentalScreenshot from bard.google.com, March 2023

Google is on the record saying that Bard is not search, which should reassure those who feel anxiety about the dawn of AI.

We are at a turning point that is unlike any we’ve seen in, perhaps, a decade.

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Understanding Bard is helpful to anyone who publishes on the web or practices SEO because it’s helpful to know the limits of what is possible and the future of what can be achieved.

More Resources:


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Time To Replace the Content Marketing Funnel (3 Alternatives)

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Time To Replace the Content Marketing Funnel (3 Alternatives)

You won’t read anything good about the content marketing funnel in this article. Only bad things. Like, it’s too linear and simplistic to address the complexities of customer journeys.

If you need a framework to build your content strategy on, it should probably be a no-funnel framework instead. And there are very good reasons for it.

A funnel in marketing is a multi-stage process that guides potential customers from first learning about a product to making a purchase.

Depending on the version, it has 3 – 6 stages, and it looks something like this:

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Traditionally, all content types have their designated place in each stage:

  • Top: product landing pages, ebooks, guides, most social media posts, etc.
  • Middle: webinars, case studies, lead nurturing programs, etc.
  • Bottom: success stories, white papers, sales enablement materials, etc.

Makes sense, right? Not entirely.

It oversimplifies literally everything important for a content marketer. And because of that, the model gets some things completely wrong and ignores others.

This isn’t just theoretical. I’ve applied the funnel approach at various companies. Initially, it was reassuring, providing a sense of structure and control. However, the deeper I got, the more confusing it became. It started to seem like the sense of order was purely imaginary, as there was no reliable method to verify if people were truly following the funnel.

1. Misunderstands consumer behavior

The funnel model assumes a perfectly linear path from awareness to purchase and tries to rush people through it. Or, actually, it makes you think you should rush people through it with your content.

However, consumer behavior is more complex and non-linear. People often jump between stages, revisit them, or take unique paths to purchase.

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So, the journey is not a funnel; it’s more like a maze.

Illustrative B2B Buying JourneyIllustrative B2B Buying Journey
Source

B2C customer journeys are even more peculiar. Remember that time when you saw an ad and bought that product immediately? Or conversely, how the journey from see to buy lasted for years. I know I can:

Short and long buyer journey examples.Short and long buyer journey examples.

But content marketers shouldn’t try to solve that maze, or cut a straight line through it just for their convenience. They should rather adapt to it.

2. Tries to fit round pegs in square holes

Not all content types can be, nor should be, fit into rigid stages of the funnel, as the model wants it.

Here’s an example based on one of our articles. Which stage(s) of the marketing funnel does our blog post about “How to find low competition keywords” serve?

Example of content fitting multiple stages of the funnel with explanation.Example of content fitting multiple stages of the funnel with explanation.

As you can see, the model can’t handle one of the basic forms of content marketing – a blog post. But take any type of educational content, and you’ll find the same problem. Many content types can serve multiple stages of the funnel or work across them. They can both attract and reengage a visitor or even bring them all the way from discovery to purchase.

Because of that, the content marketing funnel simply isn’t helpful for creating content that’s enjoyable for the user and effective for the business.

3. Neglects customer retention

Customer retention is how good you are at keeping your customers. It’s important because you don’t want customers to buy just once from you; you want to keep coming back so that you don’t need to attract a total stranger each time to make a sale — that’s both hard and expensive.

Here’s another way to look at it. According to the study by Bain and Company, increasing customer retention rates by 5% increases profits by 25% to 95%. And it makes total sense if you think about it — if someone asked you to generate an extra $1000 in sales in 24 hours, would you go to existing customers or try to find new ones?

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But if you’re practicing the old ways of the funnel, catering to your existing customers is very limited because the funnel ends at the purchase stage. There’s nothing a content marketer can do nor should do after a prospect becomes a customer.

It’s having a party where you’re so focused on inviting new guests that you forget to entertain the ones already inside.

4. Ignores customer expansion

If you only chase new customers and forget about the ones you already have, you miss the chance to make more sales to them or get them to recommend your business to others. Happy customers can really boost your business by buying more and telling their friends about you.

How can content help with that? One good way is to create product-led content. This type of content is designed to show how your product can solve the customer’s problem.

The mechanism is simple: showing product features in action turns a regular user into a power user. They start to use more features and get better value from them, which builds loyalty and gives you a good ground for upselling.

And if that content is really good, people will share it with others, amplifying your brand’s reach.

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The best thing: good content will be recommended not only by your customers. People don’t really need to be your customers or know a lot about your brand to give your content a shout-out on social media.

The best solution to the shortcomings of the funnel is to have no funnel at all. Here’s why:

  • Adapting to consumer behavior, not forcing it. Focus on how consumers naturally interact with content rather than trying to dictate their journey. Make your content easily accessible without imposing how it should be consumed.
  • A more efficient use of content marketing. Content can work both pre-sales and post-sales. It doesn’t have to be useful in one moment in time. It can be designed to stay useful and relevant over time.
  • A more helpful way to create content. No time wasted on deciding whether that guide you’re about to write belongs to the top or middle of the funnel. You can simply focus on delivering value and delighting your audience.

Here are three different no-funnel models that share those advantages.

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This approach is about using your content to directly boost demand for your product, whether before or after a sale.

Instead of sorting content by stages of a sales funnel, you rate it based on how closely it relates to your product.

The Business Potential Framework.The Business Potential Framework.

So for example, for a content marketing tool, topics with high business potential would include content marketing metrics, “B2B content marketing”, “content ideation”, “content optimization”, and “content distribution” (and not an interview with content marketers or “history of content marketing”, etc.).

This scoring system makes planning your content strategy really easy. You can quickly decide how much of each type of content to make. Also, you can use it with other important metrics (we use it with organic traffic potential) to further prioritize content.

Ahrefs has been using this model for years, especially for SEO content, which is most of what we publish. It’s great for understanding which search terms are most valuable.

Take these two keywords below as an example. The first one has a lot more traffic potential but is too broad to easily include our product — it would get a “1.” Conversely, the keyword with less traffic but more focused on SEO would get a “3” because it’s more relevant to our customers and our product.

Traffic potential data via Ahrefs' Keywords Explorer.Traffic potential data via Ahrefs' Keywords Explorer.

The Business Potential Framework might be a good fit for you if you’re working in an established industry, where there’s already considerable demand for content directly linked to products like yours. This will make it easier to find topics with a score of 2 and 3. You can gauge that demand by looking at search volume in our free keywords generator.

Free keyword research with Ahrefs' Free Keyword Generator.Free keyword research with Ahrefs' Free Keyword Generator.

The Content Playground, devised by Ashley Faus, reimagines the buyer’s journey as an open, interactive space, akin to a playground, moving away from the traditional funnel’s linear path.

Content playground visualization. Content playground visualization.

It aims to cater to varied audience interests and learning styles by offering a mix of deep dives, strategic frameworks, and practical tips. To achieve this, it covers topics in three levels:

  • Conceptual: covering big ideas and their significance.
  • Strategic: outlining frameworks and processes.
  • Tactical: providing specific, actionable steps.

Staying with the content marketing tool example, topics you would create content about could look like this: “what is content marketing” (conceptual), “developing a content marketing strategy” (strategic), “how to promote content” (tactical).

To illustrate, this content hub on Agile from Atlassian is designed to be a content playground. There is a mix of all three types of content, and the user can start at any point, go as deep as they like, and jump to another topic at any time.

Example of content playground in practice.Example of content playground in practice.

Naturally, the content needs to be interlinked and ungated so consumers may access it however they want and navigate through it freely. The bonus of that is getting organic traffic from related keywords. According to Ahrefs, this one hub attracts over 591k organic visits every month, and it looks like it’s about to get more.

Organic performance graph via Ahrefs.Organic performance graph via Ahrefs.

But a playground doesn’t need to be confined to one site. As long as you tackle a topic with these three types and allow people to access them freely, you can have it scattered across a limitless number of sites and platforms: microsites, blog posts, social media, email, ebooks, etc.

I had a brief chat with Ashley, the mind behind this framework, to understand where this framework fits best. I learned that the framework was developed and tested with B2B marketers in mind, and that’s where it’s most relevant. B2C marketers simply don’t have as big of a problem with customers “coming and going” and re-engaging them on different channels.

There is a way to cover all customer intents, topics, journey stages, and key marketing channels naturally by simply focusing on what matters to your audience and where they are willing to consume content. I call it the Cluster-Channel Network (CCN).

Two core elements of the framework are:

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  • Clusters: thematic groupings of content around a central topic, supported by a network of related subtopics. They represent things people care about.
  • Channels: platforms and mediums through which your message reaches your audience. They represent meeting places that bring you and your audience together to talk about things they care about. Think advertising, email, social media, Google, etc.

CCN ensures a multi-channel presence with content that both attracts your audience and makes your brand an authority in a carefully picked selection of topics.

What’s more, this is an efficient framework because it allows you to “squeeze out” the most of any topic. That’s an important benefit because there are only so many topics a brand can comfortably cover, without creating turning into a content farm spinning irrelevant content just for the sake of traffic.

The framework consists of five steps.

  1. Identify relevant clusters: choose clusters aligned with your brand’s expertise and audience interests.
  2. Define subtopics: within each cluster, pinpoint subtopics for comprehensive coverage.
  3. Produce core content: select a primary channel and format for in-depth content, making this your centerpiece to attract traffic from other platforms.
  4. Distribute across channels: repurpose the core content into smaller, channel-specific formats.
  5. Interlink clusters and subtopics: connect related clusters and subtopics. Chances are, people interested in more than one cluster (e.g. SEO and content marketing).

If we were to visualize this framework consisting of four clusters, it would look something like this:

Visualization of the Cluster-Channel framework. Visualization of the Cluster-Channel framework.
Content playground could be visualized as a fully connected network with 3 node sizes.

So if we used content marketing as a cluster, one of the subtopics could be AI content. For that subtopic, you could create a blog post about ethics in content marketing in the AI era and distribute it as a thread on X, offer that topic to podcast hosts, etc.

This framework will work best if you have the resources to be present on multiple channels and you’re committed to long-term goals (building trust and authority takes time).

Tip

You can find clusters and subtopics very fast using Ahrefs’ Keywords Explorer. Just plug a broad term related to your product (your cluster), and let AI do the brainstorming.

Using AI to aid keyword research process in Ahrefs.Using AI to aid keyword research process in Ahrefs.

From a bit over 10 keywords the AI found for me for the word “SEO”, Keywords Explorer found over 32k keywords which then organized into 3466 ready-to-target topics in a matter of seconds. All with traffic potential and keyword difficulty metrics to help with prioritization.

Clusters by Parent Topic report in Ahrefs' Keywords Explorer. Clusters by Parent Topic report in Ahrefs' Keywords Explorer.

Final thoughts

On a final note, the topics you choose to cover are as important as these frameworks. Check out our guide to content ideation to never run out of ideas.

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How to Avoid Ruining SEO During a Website Redesign

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How to Avoid Ruining SEO During a Website Redesign

It’s too easy to break your SEO during a website redesign. Here’s a foretaste of what can go wrong:

  • Loss of rankings and traffic.
  • Loses of link equity.
  • Broken pages.
  • Sluggish page loading.
  • Bad mobile experience.
  • Broken internal links.
  • Duplicate content.

For example, this site deleted about 15% of organic pages (yellow line) during the redesign, which resulted in an almost 50% organic traffic loss (orange line). Interestingly, even the growth of referring domains (blue line) afterward didn’t help it recover the traffic.

Fortunately, it’s not that hard to avoid these and other common issues – just six simple rules to follow.

Easily overlooked but could save the day. A backup ensures you can restore the original site if anything goes wrong.

Ask the site’s developer to be prepared for this fallback strategy. All they will need to do then is redirect the domain to the folder with the old site, and the changes will take effect almost instantly. Make sure they don’t overwrite any current databases, too.

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It won’t hurt to make a backup yourself, too. See if your hosting provider has a backup tool or use a plugin like Updraft if you’re using WordPress or a similar CMS.

Testing your site for Core Web Vitals (CWV) and mobile friendliness before it goes live is the best way to ensure that your new site will comply with Google’s page experience guidelines.

The thing is, a website redesign can seriously affect site speed, stability, responsiveness, and mobile experience. Some design flaws will be quite easy to spot, such as excessive use of animations or layout not scaling properly on mobile devices, but not others, like unoptimized code.

Ask your site developer to run mobile friendliness and CWV tests on template pages as soon as they are ready (no need to test every single page) and ask for the report. For example, they should be able to run Google Lighthouse on a password-protected website.

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An SEO audit uncovers SEO issues on your site. And if you do it pre-and post-launch, you will easily spot any potential new problems caused by the redesign, especially those that really matter, such as:

  • Unwanted noindex pages.
  • Sites accessible both as http and https.
  • Broken pages.

So before the new site goes, click on New crawl in Site Audit and then again right after it goes live.

Starting a new crawl in Site Audit.Starting a new crawl in Site Audit.

Then after the crawl, go to the All issues report and look at the Change column – new errors found between crawls will be colored red (fixed errors will be green) .

Change column in All issues report. Change column in All issues report.

You might want to give some issues higher priority than others. See our take on the most impactful technical SEO issues.

Tip

You can access the history of site audits by clicking on the project’s name in Site Audit.

How to access crawl history in Site Audit (1).How to access crawl history in Site Audit (1).
How to access crawl history in Site Audit (2).How to access crawl history in Site Audit (2).

By URL structure, I mean the way web addresses are organized and formatted. For example, these would be considered URL structure changes:

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  • ahrefs.com/blog to ahrefs.com/blog/
  • ahrefs.com/blog to ahrefs.com/resources/blog
  • ahrefs.com/blog to blog.ahrefs.com
  • ahrefs.com/site-audit to ahrefs.com/site-audit-tool

Altering that structure in an uncontrolled process can lead to:

  • Broken redirects: redirects leading to non-existing or inaccessible pages.
  • Broken backlinks: external links pointing to deleted or moved pages on your site.
  • Broken internal links: internal site links that don’t work, hindering site navigation and content discoverability.
  • Orphan pages: pages not linked from your site, making them hard for users and search engines to find.

Naturally, you should keep the old URL structure unless you’re absolutely sure you know what you’re doing. In this case, you will need to put some redirects in place. On top of that, make sure to submit a sitemap via Google Search Console to help Google reflect changes on your site faster.

Tip

Google also advises submitting a new sitemap if you’re adding many pages in one go. You may want to do that if that’s the case in your redesign project.

Redesigns often include some kind of content pruning or simply arbitrary deleting of older content. But whatever you do, it’s crucial that you keep the pages that are already ranking high.

Traffic is one reason, but since these pages are already ranking, chances are they’ve got some backlinks you risk losing.

To make sure you’re not cutting out the good stuff, use two reports in Ahrefs’ Site Explorer: Top pages and Best by links.

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Top pages report is a list of all the pages on your site ranking in the top 100, appended with SEO data and sorted by traffic by default. So, just one click on your left-hand side, and you’ll see a list of your best “traffic generators”.

Top pages report in Ahrefs' Site Explorer.Top pages report in Ahrefs' Site Explorer.

The Best by links report follows the same logic, but the focus is on links (both external and internal) and it shows all crawled pages on your site (not only the ones ranking in top 100).

Best by links report in Ahrefs' Site Explorer.Best by links report in Ahrefs' Site Explorer.

You can also plug in any page in Ahrefs’ Site Explorer and see whether it can be cut without any damage to the site’s organic performance.

Looking up single page organic performance in Site Explorer. Looking up single page organic performance in Site Explorer.

Recommendation

If part of the redesign is an inventory cleanup, you can still get traffic to products you don’t offer anymore if you create an “archive” page and link to a place where visitors can find more similar products. E-commerce sites and hardware brands do that regularly.

Example of an archive page. Example of an archive page.

This way, you can still rank for related terms, and the user experience is better than simply redirecting old products to new products.

Lastly, if you find yourself in a situation where the new design imposes significant changes to your top-ranking pages, take extra caution when altering these elements:

Final thoughts

While an overall site redesign might sound like a good moment to introduce some SEO, you need to think about the traffic and backlink equity the site has already earned. If you change too much in one go, you won’t know what worked and why, and maybe more importantly, what didn’t work and how to fix it.

Truth is, SEO is always about experimentation. You can have a well-educated guess, but you can never really know what will happen.

Want to share your SEO story here? Let me know on X or LinkedIn.

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There’s No Such Thing as “Accurate” Search Volume

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There’s No Such Thing as “Accurate” Search Volume

I often post my favorite new Ahrefs features on X. And last time I announced our newest addition to Keywords Explorer, someone replied with this:

Which was not the first time I saw us being criticized for the accuracy of our search volume metric.

But here’s the kicker…

There’s NO SUCH THING as an accurate search volume:

  • The volumes in Google Keyword Planner aren’t accurate.
  • The “Impressions” in GSC aren’t accurate either.
  • And the metric itself is just an average of the past data.

I already published a pretty detailed article about the search volume metric back in 2021. But I don’t think too many people have read it.

“Everything that needs to be said has already been said. But since no one was listening, everything must be said again.”

André Gide

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So let me address this topic from a whole new angle.

First of all, what do SEOs even mean when they ask for search volumes to be “accurate?”

Well, the less experienced folks just want the metrics in third-party tools to match what they see in Google Keyword Planner (GKP).

But the more experienced ones already know all Google Keyword Planner’s Dirty Secrets:

  • The numbers are rounded annual averages.
  • Those averages are then assigned to “volume buckets.”
  • Keywords with similar meaning are often grouped together and their search volume summed up.

In other words, the search volume numbers that you see in GKP are very imprecise. And once SEOs learn that, they no longer use GKP as their baseline of accuracy.

They use GSC.

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Ok. So the numbers in GKP are rounded and bucketed and clustered together and all that. But Google Search Console (GSC) shows you the actual impressions for a given keyword, right?

Well, did you know that a simple rank-tracking tool can easily pollute your GSC impressions?

Think of how many different “robots” might be scraping the search results for a given keyword, and therefore giving you a fairly inaccurate impression of its real (human-driven) search volume.

And besides, in order to see the actual monthly search volume your page has to be ranking at the top 10 for thirty days straight. And it should rank nationwide, just in case the search results might differ based on the location.

On top of that, I’m sure GSC is no different from any other analytics tool in the sense that it might have certain discrepancies in “counting” those impressions. I mean, go compare the “Clicks” you see reported by GSC with your server log files. I bet the numbers won’t match.

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How much time do you think would pass between you selecting a certain keyword to rank for and actually having your page rank at the top of Google for it?

According to our old research, it could be anywhere from two months to a year for a newly published page to get to the top. Don’t you think the monthly search volume of a given keyword will change by then?

That’s actually the exact reason why we’ve added search volume forecasting to our Keywords Explorer tool. It uses past data to project what would likely happen to search volume in the next 12 months:

Is it accurate? No.

But does it help to streamline your keyword research and make better decisions? Absolutely.

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Let’s do a thought experiment and imagine that there was an SEO tool which would give you a highly precise search volume for any keyword. What would you use it for? Would you be able to accurately predict your search traffic from that keyword?

No!

You can’t know for sure at which position your page will end up ranking. Today it’s #3, tomorrow it’s #5, the day after is #1. Rankings are volatile and you rarely retain a given position for a long enough period of time.

And even if you did: you can’t get precise data on the click-through rate (CTR) of each position in Google. Each SERP is unique, and Google keeps rolling out more and more SERP features that steal clicks away. So even if you knew precisely the search volume of a keyword and the exact position where your page would sit… you still would not be able to calculate the accurate amount of search traffic that you’ll get.

And finally…

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Pages don’t rank for a single keyword! Seven years ago we published a study showing that a typical page that ranks at the top of Google for some keyword would actually rank for about a thousand more related keywords.

So what’s the point of trying to gauge your clicks from a single keyword, when you’ll end up ranking for a thousand of them all at the same time?

And the takeaway from all this is…

Here at Ahrefs we spend a tremendous amount of time, effort and resources to make sure our keyword database is in good shape, both in terms of its coverage of existing search queries, and the SEO metrics we give you for each of these keywords.

None of our SEO metrics are “accurate” though. Not search volume, nor keyword difficulty, nor traffic potential, you name it.

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But none of them can be.

They’re designed to be “directionally accurate.” They give you an overall idea of the search demand of a given keyword and if it’s a lot higher (or lower) compared to some other keywords which you are considering.

You can’t use those metrics for doing any precise calculations.

But hundreds of thousands of SEO professionals around the world are using these exact metrics to guide their SEO strategies and they get precisely the results that they expect to get.



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