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Bing Explains SEO For AI Search

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Bing Explains SEO For AI Search

AI search is inevitable so it’s vital for SEO to understand everything about it. An interview with Bing’s Fabrice Canel revealed interesting insights about this topic with some takeaways that offer some insights on the future of search.

Fabrice Canel is the Principal Product Manager at Bing and because of his position there he is in the position to know more about AI search from the search engine side, something we don’t get to see.

AI Search Clicks Are Valuable

Something of special interest for SEO professionals is his discussion about what host Jason Barnard calls the perfect click and what Fabrice referred to as qualified clicks.

I’ve noticed that contextual links are in some versions of Google SGE and is also a main feature of the search engine results pages (SERPs) of some of the newer AI search engines like Perplexity AI.

Bing AI search also shows citations to websites where users can dig deeper into the topic that is relevant to them at that moment.

Fabrice talks about how these links to websites that are shown to users from AI search are more valuable than standard links from a regular search engine.

He uses the phrase “qualified clicks” to refer to traffic to websites that originate from from AI search.

Fabrice (at the 6 minute mark of the video):

“Bing is all about satisfying the end user and sometimes it’s all about exploring the web.

But sometimes it’s all about understanding the web and providing this kind of experience where at the end we can learn the clicks to the website having extremely qualified clicks.

And this is something we’ve seen where clearly when people are clicking…

And this translates to a benefit for the end user, for the website more, certainly more, than [from a] search engine, typical search engine.”

What he’s saying is that there is more context in the interaction between users and Bing, which results in better answers and in turn better traffic, qualified clicks.

Why AI Search Clicks Are Better Than Normal Search Clicks

Fabrice explains in more detail why clicks from AI search are better than from a regular search engine.

He explains that user interaction provides Bing with more search query context, which in turn allows Bing to offer links to the exact site that offers the answers that the user is looking for.

Users provide so much query information that the click to the website is essentially a perfect click, the qualified click.

Fabrice answered:

“Yeah. So fundamentally, …we have abilities to do in Bing Chat what we don’t really have out of the box of in search.

It’s a little bit more time to really go deep in understanding the query, understanding what the query can return as results.

So this is all about at the end being able to have an orchestration between the queries itself and the ranking and the profile of a user to really go deeper in understanding what the user is looking for and retrieving from the set of content.”

These interactions are so rich in data about what users want that it allows Bing to make their search even better.

And the better Bing understands user queries the better the traffic that it sends.

What’s important about that insight is that it can very much apply to traffic from other AI search engines.

Let’s take that idea a little further.

If AI search engines understand what is asked of them, then they are better able to provide the correct answers. That makes offering ten blue links no longer necessary.

It very much resembles the interaction between humans, where one will ask the other something and receives a response.

Nobody needs to respond with ten answers, right? The same applies for AI search.

What’s extraordinary is that AI search not only helps users, but it allows Bing to become better in satisfying user queries.

Fabrice continued:

“…this new technology helps us to improve even faster and certainly better to satisfy even more users.

We see that the satisfaction of users at Bing has really improved even more in the last six months than before.

So this is continuous improvement of the technology.”

AI Search Is Not About Keyword Matching

Fabrice next speaks about keywords in AI Search.

He says that the technology is not in any way about matching keywords (terms) in the query to keywords on a webpage.

He noted:

“… the technology has evolved.

This is not about …term matching, this is really understanding the context of a query, the context of a user to really retrieve the best content on the internet.”

The AI search experience again resembles a conversation between humans, where when you provide an answer to a question, using the keywords in the question is not something you consciously do, right? Your focus is on providing an answer.

AI search understands the full context of the question and answers it, just like you would.

Ranking In AI Search – Role of Verbs And Keywords

Fabrice next says that keywords matter, not because the AI search engine is matching keywords to queries but rather, the keywords help Bing understand what the page is about.

This is an important insight. It reinforces one of the most important trends of the past several years about how SEOs need to be precise in communicating what a page is about.

Keywords matter to the extent that they tell the search engine what the page is about.

Fabrice explains [16:34 minute mark]:

“So the verbs of a user matter even more these days.

People want to say, I want to book a ticket to this thing and “book” maybe not really in the content of a page, but we know that it’s a booking activity.

So maybe this is all about retrieving the event itself where people then can book the concert.

So think technology really improve, don’t think about keyword …and so on, think about satisfying the user for a set of queries that they think they will do.

…obviously, if a customer specify a verb, this is important, but if a customer …do not specify a verb, then this is all about understanding the context of this query in this specific chat, of the ability to understand what the session was all about.

Because maybe you want to search, give me a restaurant…

Maybe we will give a list of restaurants near you and then you can say, hey, I want a vegetarian restaurant. Okay?

And then you have a list of vegetarian restaurants, or give me a vegetarian one and give me one that can accommodate 20 people.

So again, you don’t repeat the restaurant [can’t understand], you just continue the chat experience and we have a full context of the full session and helping to reply [to] your question.

And for search engine optimization, …It means at the end that you may care about keyword and you should care about keyword because we need to know that it’s a restaurant for vegetarian, we need to know that it can accommodate a large group of people.

But this is less about really matching this query. This is again, not really matching, this is matching what people are searching, looking for.”

What’s The Connection Between AI And Search Algorithms?

Jason Barnard next asks if the Bing chat algorithm and the search algorithm are the same.

Fabrice answers [19:35 minute mark]:

“This is an excellent question.

So first of all, in Bing Chat and search we benefit obviously of a big index.

It’s not, let’s say, a large language model store that you interact here.

Here, we benefit from not only this new tech…, but also by having deep interaction with the index itself.

So mean that …we are doing multiple queries and retrieving from this query the best content on the Internet. It’s not a static set, it’s a dynamic set.

You benefit from having the latest content and index and we have technology for that to make sure that content can be indexed, latest content can be indexed in seconds.

But it’s really this kind of interaction with the best content on the Internet that we can retrieve and we do multiple queries to retrieve.

So overall I will share that the technology is the same, but in chat there is even more complex queries that are done to really retrieve the content, analyze the content.

Chat give us access to more time to do a little bit more things, understanding, also interacting deeper with the user via the chat experience and session, where we can also not only suggest text, suggest verbs that the customer can do to continue the discussion with a search engine to retrieve the best content on the Internet.”

Will Ten Blue Links Disappear?

The ten blue links have been going away as a standard in traditional search engines for many years, more than a decade.

Where does the ten blue links paradigm fit into AI search?

Surprisingly, ten blue links still have a place in AI search.

Fabrice answered [30:55 minute mark]:

“Yeah, again, personally I do not believe that.

Again, don’t know if mindset of people evolve and they really prefer chat, why not?

But again, I still feel that there is a set of queries where the ten blue links are really satisfying the user today.

And so this means again you query for specific query, navigational query.

You just certainly don’t want at least me, I don’t want to have an experience where there is asking me more questions.

No, no, I want to click this link, this is the link.

I know where I want to go. I don’t remember the domain names, but I want to go there.

And so this is kind of a directory address book where okay, I know this is perfect. Thank you. I’m done.

I am visiting the site now.

And this is then you don’t really need a huge experience and you need really this navigational… And so blue links satisfy your need.”

The takeaway then is that there are certain contexts where users need the ten blue links and that it doesn’t make sense to drag the full chat experience into those kinds of queries.

Two Things To Do For LLM Search

Fabrice later discusses what SEOs should do for AI search.

He basically says to make it easy to get indexed because building an index on the LLM side can take years.

The first thing is to adopt IndexNow for incredibly fast indexing. On the AI side, the LLM can take months to years to be up to date.

Fabrice said:

“…using IndexNow, you will get your content indexed in seconds.

…In LLM, it takes weeks, months, more likely years to build the new LLM tech.

So this is important for the SEO community because you have to do it right now, as soon as possible, to be a part of the next LLM version.”

The second thing that Fabrice suggested that the SEO community do is to make the content easily accessible by search engines.

Fabrice continued:

“Second is …have your content based on a basic template.

Don’t do the craziness things with plenty of Ajax calls to retrieve the content that the developer developing that says it will be great, but for a search engine it will be a disaster.

Machine learning is all about learning from a set of documents and then aligning to some judgment.

The more basic you are, the more standardized you are, the better it is for the search engine.

And as part of this you really want to help the content to be understood by search engines, means not only add HTML tags, the appropriate HTML title to differentiate the headings from the paragraph. And so on.

But add structured data to help the index and help the LLM to really understand what this is all about. What your content is all about.

So all this information is leveraged, real time as soon as we call the page for the index.

LLM has a different lifecycle. …LLM are not built at the same lifecycle as building an index.

Building an index is real-time. In an LLM it takes weeks, months, more likely years, to build the new LLM tech.

So this is important for the SEO community because you have to do it right now, as soon as possible to be a part of the next LLM version.

If you think of the old search engines, this is kind of the lifecycle that you need to target.

Fundamentally this about doing the right thing now, today.

And …doing the right things will benefit not only for search engine indexing, but also for LLMs.”

Bing Avoids Big Updates

Something interesting that Fabrice mentioned is that they try to avoid disruptive changes in rankings, which is different from the way Google’s core algorithm updates function. Instead, he described a process that is always changing.

Fabrice said:

“At Bing we in general avoid this kind of big change. Because this is constantly ongoing, meaning there are always improvements.

The lifecycle of a Bing engineer is to dream of a relevance improvement, to go to work in the morning to be able to code and test this experiment and in the afternoon this engineer will start to get feedback.

And the next day it’s good, then they can start …testing and rolling out the change.

So this is multiple hundreds of experiments that are done in the course of a day to really test things.

The ones that are good go live.

So this is continuous improvement, it’s not waves of improvement as we may see often in other search engines.”

Understanding SEO For AI Search

Learning what LLM search is about is critical because AI search is upon us right now. It may be in beta status like Google SGE or it may still be evolving, like Bing, as users figure out for themselves how to best use AI search.

As search professionals we need to get on board with certain ideas and practices:

  • Don’t think in terms of keyword matching but rather use keywords to help the content become easy to understand what it’s about.
  • Consider verbs that users may use to ask questions in order to better align your content to be relevant to their queries.
  • Links from AI search are qualified, they’re on target.
  • Use structured data.
  • Use IndexNow in order to help your content get indexed fast.
  • Avoid complex websites as best that you can.
  • Blue links are not entirely going away.

Watch the video:

How does Generative AI in Search Work and What is Coming in 2024

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Link Building Outreach for Noobs

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Link Building Outreach for Noobs

Link outreach is the process of contacting other websites to ask for a backlink to your website.

For example, here’s an outreach email we sent as part of a broken link building campaign:

In this guide, you’ll learn how to get started with link outreach and how to get better results. 

How to do link outreach

Link outreach is a four-step process:

1. Find prospects

No matter how amazing your email is, you won’t get responses if it’s not relevant to the person you’re contacting. This makes finding the right person to contact equally as important as crafting a great email.

Who to reach out to depends on your link building strategy. Here’s a table summarizing who you should find for the following link building tactics:

As a quick example, here’s how you would find sites likely to accept your guest posts:

  1. Go to Content Explorer
  2. Enter a related topic and change the dropdown to “In title”
  3. Filter for English results
  4. Filter for results with 500+ words
  5. Go to the “Websites” tab
Finding guest blogging opportunities via Content ExplorerFinding guest blogging opportunities via Content Explorer

This shows you the websites getting the most search traffic to content about your target topic.

From here, you’d want to look at the Authors column to prioritize sites with multiple authors, as this suggests that they may accept guest posts.

The Authors column indicate how many authors have written for the siteThe Authors column indicate how many authors have written for the site

If you want to learn how to find prospects for different link building tactics, I recommend reading the resource below.

2. Find their contact details

Once you’ve curated a list of people to reach out to, you’ll need to find their contact information.

Typically, this is their email address. The easiest way to find this is to use an email lookup tool like Hunter.io. All you need to do is enter the first name, last name, and domain of your target prospect. Hunter will find their email for you:

Finding Tim's email with Hunter.ioFinding Tim's email with Hunter.io

To prevent tearing your hair from searching for hundreds of emails one-by-one, most email lookup tools allow you to upload a CSV list of names and domains. Hunter also has a Google Sheets add-on to make this even easier.

Using the Hunter for Sheets add-on to find emails in bulk directly in Google SheetsUsing the Hunter for Sheets add-on to find emails in bulk directly in Google Sheets

3. Send a personalized pitch

Knowing who to reach out to is half the battle won. The next ‘battle’ to win is actually getting the person to care.

Think about it. For someone to link to you, the following things need to happen:

  • They must read your email
  • They must be convinced to check out your content
  • They must open the target page and complete all administrative tasks (log in to their CMS, find the link, etc.)
  • They must link to you or swap out links

That’s a lot of steps. Most people don’t care enough to do this. That’s why there’s more to link outreach than just writing the perfect email (I’ll cover this in the next section).

For now, let’s look at how to craft an amazing email. To do that, you need to answer three questions:

  1. Why should they open your email? — The subject line needs to capture attention in a busy inbox.
  2. Why should they read your email? — The body needs to be short and hook the reader in.
  3. Why should they link to you? — Your pitch needs to be compelling: What’s in it for them and why is your content link-worthy?

For example, here’s how we wrote our outreach email based on the three questions:

An analysis of our outreach email based on three questionsAn analysis of our outreach email based on three questions

Here’s another outreach email we wrote, this time for a campaign building links to our content marketing statistics post:

An analysis of our outreach email based on three questionsAn analysis of our outreach email based on three questions

4. Follow up, once

People are busy and their inboxes are crowded. They might have missed your email or read it and forgot.

Solve this by sending a short polite follow-up.

Example follow-up emailExample follow-up email

One is good enough. There’s no need to spam the other person with countless follow-up emails hoping for a different outcome. If they’re not interested, they’re not interested.

Link outreach tips

In theory, link outreach is simply finding the right person and asking them for a link. But there is more to it than that. I’ll explore some additional tips to help improve your outreach.

Don’t over-personalize

Some SEOs swear by the sniper approach to link outreach. That is: Each email is 100% customized to the person you are targeting.

But our experience taught us that over-personalization isn’t better. We ran link-building campaigns that sent hyper-personalized emails and got no results.

It makes logical sense: Most people just don’t do favors for strangers. I’m not saying it doesn’t happen—it does—but rarely will your amazing, hyper-personalized pitch change someone’s mind.

So, don’t spend all your time tweaking your email just to eke out minute gains.

Avoid common templates

My first reaction seeing this email is to delete it:

A bad outreach emailA bad outreach email

Why? Because it’s a template I’ve seen many times in my inbox. And so have many others.

Another reason: Not only did he reference a post I wrote six years ago, it was a guest post, i.e., I do not have control over the site. This shows why finding the right prospects is important. He even got my name wrong.

Templates do work, but bad ones don’t. You can’t expect to copy-paste one from a blog post and hope to achieve success.

A better approach is to use the scoped shotgun approach: use a template but with dynamic variables.

Email outreach template with dynamic variablesEmail outreach template with dynamic variables

You can do this with tools like Pitchbox and Buzzstream.

This can help achieve a decent level of personalization so your email isn’t spammy. But it doesn’t spend all your time writing customized emails for every prospect.

Send lots of emails

When we polled 800+ people on X and LinkedIn about their link outreach results, the average conversion rate was only 1-5%.

Link outreach conversion rates in 2023Link outreach conversion rates in 2023

This is why you need to send more emails. If you run the numbers, it just makes sense:

  • 100 outreach emails with a 1% success rate = 1 link
  • 1,000 outreach emails with a 1% success rate = 10 links

I’m not saying to spam everyone. But if you want more high-quality links, you need to reach out to more high-quality prospects.

Build a brand

A few years ago, we published a link building case study:

  • 515 outreach emails
  • 17.55% reply rate
  • 5.75% conversion rate

Pretty good results! Except the top comments were about how we only succeeded because of our brand:

Comments on our YouTube video saying we succeeded because of our brandComments on our YouTube video saying we succeeded because of our brand

It’s true; we acknowledge it. But I think the takeaway here isn’t that we should repeat the experiment with an unknown website. The takeaway is that more SEOs should be focused on building a brand.

We’re all humans—we rely on heuristics to make judgments. In this case, it’s branding. If your brand is recognizable, it solves the “stranger” problem—people know you, like you, and are more likely to link.

The question then: How do you build a brand?

I’d like to quote our Chief Marketing Officer Tim Soulo here:

What is a strong brand if not a consistent output of high-quality work that people enjoy? Ahrefs’ content team has been publishing top-notch content for quite a few years on our blog and YouTube channel. Slowly but surely, we were able to reach tens of millions of people and instill the idea that “Ahrefs’ content = quality content”—which now clearly works to our advantage.

Tim SouloTim Soulo

Ahrefs was once unknown, too. So, don’t be disheartened if no one is willing to link to you today. Rome wasn’t built in a day.

Trust the process and create incredible content. Show it to people. You’ll build your brand and reputation that way.

Build relationships with people in your industry

Outreach starts before you even ask for a link.

Think about it: People don’t do favors for strangers but they will for friends. If you want to build and maintain relationships in the industry, way before you start any link outreach campaigns.

Don’t just rely on emails either. Direct messages (DMs) on LinkedIn and X, phone calls—they all work. For example, Patrick Stox, our Product Advisor, used to have a list of contacts he regularly reached out to. He’d hop on calls and even send fruit baskets.

Create systems and automations

In its most fundamental form, link outreach is really about finding more people and sending more emails.

Doing this well is all about building systems and automations.

We have a few videos on how to build a team and a link-building system, so I recommend that you check them out.

Final thoughts

Good link outreach is indistinguishable from good business development.

In business development, your chances of success will increase if you:

  • Pitch the right partners
  • Have a strong brand
  • Have prior relationships with them
  • Pitch the right collaboration ideas

The same goes for link outreach. Follow the principles above and you will see more success for your link outreach campaigns.

Any questions or comments? Let me know on Twitter X.



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Research Shows Tree Of Thought Prompting Better Than Chain Of Thought

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Research Shows Tree Of Thought Prompting Better Than Chain Of Thought

Researchers discovered a way to defeat the safety guardrails in GPT4 and GPT4-Turbo, unlocking the ability to generate harmful and toxic content, essentially beating a large language model with another large language model.

The researchers discovered that the use of tree-of-thought (ToT)reasoning to repeat and refine a line of attack was useful for jailbreaking another large language model.

What they found is that the ToT approach was successful against GPT4, GPT4-Turbo, and PaLM-2, using a remarkably low number of queries to obtain a jailbreak, on average less than thirty queries.

Tree Of Thoughts Reasoning

A Google research paper from around May 2022 discovered Chain of Thought Prompting.

Chain of Thought (CoT) is a prompting strategy used on a generative AI to make it follow a sequence of steps in order to solve a problem and complete a task. The CoT method is often accompanied with examples to show the LLM how the steps work in a reasoning task.

So, rather than just ask a generative AI like Midjourney or ChatGPT to do a task, the chain of thought method instructs the AI how to follow a path of reasoning that’s composed of a series of steps.

Tree of Thoughts (ToT) reasoning, sometimes referred to as Tree of Thought (singular) is essentially a variation and improvement of CoT, but they’re two different things.

Tree of Thoughts reasoning is similar to CoT. The difference is that rather than training a generative AI to follow a single path of reasoning, ToT is built on a process that allows for multiple paths so that the AI can stop and self-assess then come up with alternate steps.

Tree of Thoughts reasoning was developed in May 2023 in a research paper titled Tree of Thoughts: Deliberate Problem Solving with Large Language Models (PDF)

The research paper describes Tree of Thought:

“…we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving.

ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.

Our experiments show that ToT significantly enhances language models’ problem-solving abilities…”

Tree Of Attacks With Pruning (TAP)

This new method of jailbreaking large language models is called Tree of Attacks with Pruning, TAP. TAP uses two LLMs, one for attacking and the other for evaluating.

TAP is able to outperform other jailbreaking methods by significant margins, only requiring black-box access to the LLM.

A black box, in computing, is where one can see what goes into an algorithm and what comes out. But what happens in the middle is unknown, thus it’s said to be in a black box.

Tree of thoughts (TAP) reasoning is used against a targeted LLM like GPT-4 to repetitively try different prompting, assess the results, then if necessary change course if that attempt is not promising.

This is called a process of iteration and pruning. Each prompting attempt is analyzed for the probability of success. If the path of attack is judged to be a dead end, the LLM will “prune” that path of attack and begin another and better series of prompting attacks.

This is why it’s called a “tree” in that rather than using a linear process of reasoning which is the hallmark of chain of thought (CoT) prompting, tree of thought prompting is non-linear because the reasoning process branches off to other areas of reasoning, much like a human might do.

The attacker issues a series of prompts, the evaluator evaluates the responses to those prompts and then makes a decision as to what the next path of attack will be by making a call as to whether the current path of attack is irrelevant or not, plus it also evaluates the results to determine the likely success of prompts that have not yet been tried.

What’s remarkable about this approach is that this process reduces the number of prompts needed to jailbreak GPT-4. Additionally, a greater number of jailbreaking prompts are discovered with TAP than with any other jailbreaking method.

The researchers observe:

“In this work, we present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM.

TAP utilizes an LLM to iteratively refine candidate (attack) prompts using tree-of-thoughts reasoning until one of the generated prompts jailbreaks the target.

Crucially, before sending prompts to the target, TAP assesses them and prunes the ones unlikely to result in jailbreaks.

Using tree-of-thought reasoning allows TAP to navigate a large search space of prompts and pruning reduces the total number of queries sent to the target.

In empirical evaluations, we observe that TAP generates prompts that jailbreak state-of-the-art LLMs (including GPT4 and GPT4-Turbo) for more than 80% of the prompts using only a small number of queries. This significantly improves upon the previous state-of-the-art black-box method for generating jailbreaks.”

Tree Of Thought (ToT) Outperforms Chain Of Thought (CoT) Reasoning

Another interesting conclusion reached in the research paper is that, for this particular task, ToT reasoning outperforms CoT reasoning, even when adding pruning to the CoT method, where off topic prompting is pruned and discarded.

ToT Underperforms With GPT 3.5 Turbo

The researchers discovered that ChatGPT 3.5 Turbo didn’t perform well with CoT, revealing the limitations of GPT 3.5 Turbo. Actually, GPT 3.5 performed exceedingly poorly, dropping from 84% success rate to only a 4.2% success rate.

This is their observation about why GPT 3.5 underperforms:

“We observe that the choice of the evaluator can affect the performance of TAP: changing the attacker from GPT4 to GPT3.5-Turbo reduces the success rate from 84% to 4.2%.

The reason for the reduction in success rate is that GPT3.5-Turbo incorrectly determines that the target model is jailbroken (for the provided goal) and, hence, preemptively stops the method.

As a consequence, the variant sends significantly fewer queries than the original method…”

What This Mean For You

While it’s amusing that the researchers use the ToT method to beat an LLM with another LLM, it also highlights the usefulness of ToT for generating surprising new directions in prompting in order to achieve higher levels of output.

  • TL/DR Takeaways:
  • Tree of Thought prompting outperformed Chain of Thought methods
  • GPT 3.5 worked significantly poorly in comparison to GPT 4 in ToT
  • Pruning is a useful part of a prompting strategy
  • Research showed that ToT is superior to CoT in an intensive reasoning task like jailbreaking an LLM

Read the original research paper:

Tree of Attacks: Jailbreaking Black-Box LLMs Automatically (PDF)

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The Lean Guide (With Template)

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The Lean Guide (With Template)

A competitive analysis (or market competitive analysis) is a process where you collect information about competitors to gain an edge over them and get more customers.

However, the problem is that “traditional” competitive analysis is overkill for most businesses — it requires impractical data and takes too long to complete (and it’s very expensive if you choose to outsource). 

A solution to that is a lean approach to the process — and that’s what this guide is about. 

In other words, we’ll focus on the most important data you need to answer the question: “Why would people choose them over you?”. No boring theory, outtakes from marketing history, or spending hours digging up nice-to-have information.

In this guide, you will find:

  • A real-life competitive analysis example.
  • Templates: one for input data and one for a slide deck to present your analysis to others.
  • Step-by-step instructions.

Our template consists of two documents: a slide deck and a spreadsheet. 

The Slide deck is the output document. It will help you present the analysis to your boss or your teammates.

The spreadsheet is the input document. You will find tables that act as the data source for the charts from the slide deck, as well as a prompt to use in ChatGPT to help you with user review research.

Competitive analysis template — spreadsheet sneak peek.Competitive analysis template — spreadsheet sneak peek.

We didn’t focus on aesthetics here; every marketer likes to do slide decks their own way, so feel free to edit everything you’ll find there. 

With that out of the way, let’s talk about the process. The template consists of these six tasks: 

  1. Identify your direct competitors. 
  2. Compare share of voice. 
  3. Compare pricing and features.
  4. Find strong and weak points based on reviews.
  5. Compare purchasing convenience.
  6. Present conclusions.

Going forward, we’ll explain why these steps matter and show how to complete them. 

1. Identify your direct competitors

Direct competitors are businesses that offer a similar solution to the same audience. 

They matter a lot more than indirect competitors (i.e. businesses with different products but targeting the same audience as you) because you’ll be compared with them often (e.g. in product reviews and rankings). Plus, your audience is more likely to gravitate towards them when considering different options. 

You probably have a few direct competitors in mind already, but here are a few ways to find others based on organic search and paid search ads

Our basis for the analysis was Landingi, a SaaS for building landing pages (we chose that company randomly). So in our case, we found these 3 direct competitors. 

Slide 1 — direct competitors.Slide 1 — direct competitors.

Look at keyword overlap

Keyword overlap uncovers sites that target the same organic keywords as you. Some sites will compete with you for traffic but not for customers (e.g. G2 may share some keywords with Landingi but they’re a different business). However, in many cases, you will find direct competitors just by looking at this marketing channel. 

  • Go to Ahrefs’ Site Explorer and enter your site’s address. 
  • Scroll down to Organic competitors
  • Visit the URLs to pick 3 – 5 direct competitors.
Top organic competitors data from Ahrefs.Top organic competitors data from Ahrefs.

To double-check the choice of competitors, we also looked at who was bidding for search ads on Google.

See who’s advertising 

If someone is spending money to show ads for keywords related to what you do, that’s a strong indication they are a direct competitor. 

  • Go to Ahrefs’ Keywords Explorer.
  • Type in a few broad keywords related to your niche, like “landing page builder” or “landing page tool”. 
  • Go to the Ads history report. 
  • Visit the sites that have a high presence of ads in the SERPs (Search Engine Result Pages). 
Ads history report in Ahrefs' Keywords Explorer.Ads history report in Ahrefs' Keywords Explorer.

Once you’re done checking both reports, write down competitors in the deck. 

You can also take screenshots of the reports and add them to your deck to show the supporting data for your argument. 

 Slide 2 — direct competitors by organic traffic. Slide 2 — direct competitors by organic traffic.

2. Compare share of voice

Share of voice is a measure of your reach in any given channel compared to competitors. 

A bigger share of voice (SOV) means that your competitors are more likely to reach your audience. In other words, they may be promoting more effectively than you. 

In our example, we found that Landingi’s SOV was the lowest in both of these channels. 

Organic: 

Slide 3 — share of voice on Google Search.Slide 3 — share of voice on Google Search.

And social media:

 Slide 4 — share of voice on social media. Slide 4 — share of voice on social media.

Here’s how we got that data using Ahrefs and Brand24.

Organic share of voice 

Before we start, make sure you have a project set up in Ahrefs’ Rank Tracker

Create a new project in Ahrefs' Rank Tracker.Create a new project in Ahrefs' Rank Tracker.

Now: 

  • Go to Ahrefs’ Competitive Analysis and enter your and your competitors’s sites as shown below. 
Create a new project in Ahrefs' Rank Tracker.
Create a new project in Ahrefs' Rank Tracker.
  • On the next screen, set the country with the most important market for your business and set the filters like this:
Content gap analysis filter setup.Content gap analysis filter setup.
  • Select keywords that sound most relevant to your business (even if you don’t rank for them yet) and Add them to Rank Tracker
Common keywords found via Ahrefs' Competitive Analysis.Common keywords found via Ahrefs' Competitive Analysis.
  • Go to Rank Tracker, open your project, and look for Competitors/Overview. This report will uncover automatically calculated Share of Voice
Organic share of voice data in Ahrefs.Organic share of voice data in Ahrefs.
  • Add the numbers in corresponding cells inside the sheet and paste the graph inside the slide deck. 
Filling the share of voice template with data.Filling the share of voice template with data.

It’s normal that the numbers don’t add up to 100%. SOV is calculated by including sites that compete with you in traffic but are not your direct competitors, e.g. blogs. 

Social share of voice 

We can also measure our share of voice across social media channels using Brand24.

  • Go to Brand24.
  • Start a New project for your brand and each competitor. Use the competitors’ brand name as the keyword to monitor. 
  • Go to the Comparison report and compare your project with competitors. 
Using Brand24's Comparison tool for competitive analysis.Using Brand24's Comparison tool for competitive analysis.
  • Take a screenshot of the SOV charts and paste them into the slide deck. Make sure the charts are set to “social media”.
Social media tab in share of voice report.Social media tab in share of voice report.

3. Compare pricing and features

Consumers often choose solutions that offer the best value for money — simple as that. And that typically comes down to two things: 

  • Whether you have the features they care about. We’ll use all features available across all plans to see how likely the product is to satisfy user needs.
  • How much they will need to pay. Thing is, the topic of pricing is tricky: a) when assessing affordability, people often focus on the least expensive option available and use it as a benchmark, b) businesses in the SaaS niche offer custom plans. So to make things more practical, we’ll compare the cheapest plans, but feel free to run this analysis across all pricing tiers.

After comparing our example company to competitors, we found that it goes head-to-head with Unbounce as the most feature-rich solution on the market. 

Slide 5 — features vs. pricing.Slide 5 — features vs. pricing.

Here’s how we got that data. 

  • Note down your and your competitors’ product features. One of the best places to get this information is pricing pages. Some brands even publish their own competitor comparisons — you may find them helpful too. 
  • While making the list, place a “1” in the cell corresponding to the brand that offers the solution.
Filling data in the spreadsheet.Filling data in the spreadsheet.
  • Enter the price of the cheapest plan (excluding free plans). 
Adding pricing data inside the spreadsheet.Adding pricing data inside the spreadsheet.
  • Once finished, copy the chart and paste it inside the deck. 

4. Find strong and weak points based on user reviews

User reviews can show incredibly valuable insight into your competitors’ strong and weak points. Here’s why this matters:

  • Improving on what your competitors’ customers appreciate could help you attract similar customers and possibly win some over.
  • Dissatisfaction with competitors is a huge opportunity. Some businesses are built solely to fix what other companies can’t fix. 

Here’s a sample from our analysis: 

 Slide 6 — likes and dislikes about Competitors. Slide 6 — likes and dislikes about Competitors.

And here’s how we collated the data using ChatGPT. Important: repeat the process for each competitor.

  • Open ChatGPT and enter the prompt from the template.
ChatGPT prompt for competitive analysis.ChatGPT prompt for competitive analysis.
  • Go to G2, Capterra, or Trustpilot and find a competitor’s reviews with ratings from 2 – 4 (i.e. one rating above the lowest and one below the highest possible). Reason:

businesses sometimes solicit five-star reviews, whereas dissatisfied customers tend to leave one-star reviews in a moment of frustration. The most actionable feedback usually comes in between.

  • Copy and paste the content of the reviews into ChatGPT (don’t hit enter yet). 
  • Once you’re done pasting all reviews, hit enter in ChatGPT to run the analysis.
Sample of ChatGPT output with charts.Sample of ChatGPT output with charts.
  • Paste the graphs into the deck. If you want the graphs to look different, don’t hesitate to ask the AI. 

There’s a faster alternative, but it’s a bit more advanced. 

Instead of copy-pasting, you can use a scraping tool like this one to get all reviews at once. The downside here is that not all review sources will a have scraping tool available. 

5. Compare purchasing convenience

Lastly, we’ll see how easy it is to actually buy your products, and compare the experience to your competitors. 

This is a chance to simplify your checkout process, and even learn from any good habits your competitors have adopted.

For example, we found that our sample company had probably nothing to worry about in this area — they ticked almost all of the boxes. 

Slide 7 — purchasing convenience.Slide 7 — purchasing convenience.

Here’s how to complete this step:

  • Place a “1” if you or any of your competitors offer convenience features listed in the template. 
  • Once done, copy the chart and paste it into the deck.

Step 6. Present conclusions

This is the part of the presentation where you sum up all of your findings and suggest a course of action. 

Here are two examples: 

  • Landingi had the lowest SOV in the niche, and that is never good. So the conclusion might be to go a level deeper and do an SEO competitive analysis, and to increase social media presence by creating more share-worthy content like industry surveys, design/CRO tips, or in-house data studies.
  • Although the brand had a very high purchasing convenience score, during the analysis we found that there was a $850 gap between the monthly full plan and the previous tier. The conclusion here might be to offer a custom plan (like competitors do) to fill that gap. 

We encourage you to take your time here and think about what would make the most sense for your business. 

Tip

It’s good to be specific in your conclusions, but don’t go too deep. Competitive analysis concerns many aspects of the business, so it’s best to give other departments a chance to chime in. Just because your competitors have a few unique features doesn’t necessarily mean you need to build them too.

Final thoughts 

A competitive analysis is one of the most fruitful exercises in marketing. It can show you areas for improvement, give ideas for new features, and help you discover gaps in your strategy. It wouldn’t be an exaggeration to say that it’s fundamental to running a successful business. 

Just don’t forget to balance “spying” on your competitors with innovation. After all, you probably don’t want to become an exact copy of someone else’s brand. 

In other words, use competitive analysis to keep up with your competitors, but don’t let that erase what’s unique about your brand or make you forget your big vision. 

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