SEO
How to Achieve Product-Market Fit (5 Steps)

Startups experience a never-ending stream of problems and challenges. Survival in such a scenario is an art of choosing between what to focus on, put on hold, or simply ignore.
But that is never easy: differences in opinions, time and money running out, and the false notion that a truly great business idea should sky-rocket immediately are common issues that startups willΒ face.
Product-market fit is a concept that aims to solve various startup problems by aspiring to be βthe only thing that matters.β Focusing on this concept should put any startup on the right track, no matter the circumstances.Β
In this article, weβre going to take a closer look at this widely discussed concept. Hereβs what weβll address:
Product-market fit (PMF) is when a business has confirmed signals that its product can satisfy an existing demand in a market with high potential.Β
The usual sign of achieving PMF is when people are willing to buy the product (even if itβs not perfect yet), actively use it, and recommend it to others.Β
Building a successful product is a matter of doing the right things in the right order and focusing on what truly matters.Β
Just as houses need to be built from the ground up, businesses should build good foundations before going any further.Β
Before hiring more people or scaling customer acquisition, startups should confirm two things: First, there are enough people willing to pay for the product; second, the market itself shows a potential for growth.Β
Simply put, without PMF, there is no sustainable growth.Β
There are a few possible scenarios for achieving PMF. Some companies find a good, initial idea that they build upon. Others need to change their business completely (pivot) to become profitable. So letβs look at some examples of businesses finding theirΒ PMF.Β
Ahrefs
Ahrefs is an all-in-one SEO toolkit that comprises multiple tools designed to grow organic search traffic, analyze competition, and tackle technical SEO issues.Β

Ahrefsβ five coreΒ tools.
But in the early days, Ahrefs was just a single tool built for backlink analysis (which is only one aspect ofΒ SEO).Β
Recommended reading: SEO: The Complete Guide for Beginners

Ahrefsβ value proposition inΒ 2011.
Ahrefsβ founding team focused solely on customer satisfaction of the first product. There was no marketing or sales team in the beginning.Β
That strategy allowed the company to get clear signals of PMF. Consequently, thanks to the organic growth of its customer base, Ahrefs was able to build more successful SEO tools and scale itsΒ team.
Itβs important to note Ahrefs didnβt stop at the initial PMF. To stay competitive and on top of the marketβs demand, it expanded the functionality from a single-purpose SEO tool to a full-blown SEO toolkit.Β
Slack
Meet Glitch, the progenitor of Slack that wasnβt even a messaging app. Glitch was a browser-based online multiplayer game launched in 2012, and it looked likeΒ this:

That chat window on the right is what later became the Slack we all know today. The rest was dropped by the company and released under an open-source license for anyone to takeΒ over.Β
Slack found its PMF by turning into a completely different product. In startup lingo, that is called a βpivot.β
The Glitch game didnβt see the desired success. But in light of its spin-offβs success, that doesnβt matter at all. The important part is to understand your lesson quickly and focus on things that actually work.
Play-Doh
Play-Doh is a classic toy that has been a must-have in any toy store for some 50 years. The brand is hugely successful, but its journey of searching for PMF is a lot less smooth sailing.
First of all, Play-Doh originally had a completely different application for a completely different target audience than today. Going by the name of Kutol, this product was a wall cleaner made especially for washing off the black residue on coal heatersβa common problem in theΒ 1930s.Β
The business was great until the coal heaters were substituted by βcleanerβ gas and oil heaters. Thatβs how the company lost its original PMF.Β
But it didnβt give up. Legend has it that one of the foundersβ relatives had been using Kutol with children in art and crafts classes. That relative suggested something similar for a new, official product application. So the producers of Kutol took a leap of faith and rebranded the product as Play-Doh, a modeling compound for children.Β
With that, the company discovered its new PMF and has held on to it everΒ since.Β

Before and after. In the 1950s, the wall cleaner βKutolβ tried to regain its PMF. In doing so, it became a successful toy.
The underlying idea behind the process of finding PMF is similar to the scientific method. To make a discovery (i.e., what product to build), you need to research the problem well enough to propose a hypothesis and then design an experiment that will prove or disprove the hypothesis.Β
If you want to learn about measuring PMF for an already existing product, jump to stepΒ four.
Step 1. Formulate the value hypothesisΒ
A value hypothesis is an assumption explaining why a customer is likely to buy your product. In other words, you need to specify what value your product would introduce to the userβs life.Β
A value hypothesis may look something likeΒ this:
- Buying books online provides a better experience than buying books in physical stores.
- SEO professionals need a tool for automated technical SEO diagnosis.
- Email communication is less productive than real-time online chatting.Β
Your value hypothesis will later be tested in confrontation with real users interacting with your minimum viable product (MVP).
Great products solve real, meaningful problems. To identify those problems and the potential customers in need of a solution, you need to perform market research.
Market research can be a really time-consuming process. But the good news is that a good portion of market research can be done online without breaking theΒ bank.
For example, by using an SEO tool like Ahrefs, you can gauge market demand by looking for signs of search demand in search engines, as they are often correlated.Β
Letβs say your startup wants to offer an online solution for delayed and canceled flight compensation. Since this will be an online product, youβll want to see how often people search for queries related to that problem. With Ahrefsβ Keywords Explorer, that information is only a few clicks away.Β

βFlight delay compensationβ gets around 1.6K searches per month globally and seems like a common problem. Also, we can see the search demand was heavily impacted by the pandemic.
Step 2. Specify the features of your minimum viable productΒ
Once youβve clearly defined the problem you want to solve, the next step is identifying the set of features that will solve the customerβs problem.
Building too few features will result in an incomplete solution. But having too many features is not good as well, as this can dilute the core value of the product and increase the risk of overspending on your prototype.Β
This is where an MVP comes in. Building an MVP is about the balance of the right kind and the right number of features needed to verify the hypothesis.Β
Itβs probably a good idea to use multiple sources and types of research to discover what needs to be built. You can combine conclusions from your competitive analysis, surveys, observations, and industry reports.Β
Also, SEO tools can come in handy. For example, you can get a pretty good idea of which features are generating the most value for your competitors by identifying webpages that they drive paid traffic to and top pages by organic search traffic.Β
Suppose you want to build a project management tool. Letβs use Ahrefsβ Site Explorer and look at the Paid pages report for asana.com:

And letβs see the landing pages for product features that get the most organic traffic in the Top pages report:Β

You can infer that building forms and timeline features into your project management app is something to seriously consider. If itβs good for your competitor, it could be good for your product as well (on top of your unique value proposition).Β
Step 3. Build your minimum viable product
If you look at some of the most successful MVPs, you will see there are many ways to build them. They donβt even have to be fully functional products.
To illustrate, Buffer started as a landing page that collected sign-ups for a product that did not existΒ yet.Β

Dropbox validated its business idea by creating two videos about the product. The first video didnβt even show the product.Β
https://www.youtube.com/watch?v=w4eTR7tci6A
Now, letβs look at a few ideas on how you can build an MVP (besides developing a fully functional product):
- Ad campaigns β You can produce various types of creative content for specific audiences to see which aspects of your product appeal to them theΒ most.Β
- Prototypes β You can use design tools like UXPin, Figma, or Marvel to create an interactive, high-fidelity prototype of your solution and show it to people.Β
- Landing pages β Similar to how Buffer started, you can create a landing page to pitch your idea and collect email addresses through a waiting list sign-up form. Later on, you can use those emails to gather feedback. Also, you can run A/B tests on the site to test different hypotheses.
- Customer interviews β This method allows you to dive deeper into how your prospects would react to your product with the least possible effort.
- βWizard of Ozβ MVP β Customers think they are experiencing a real product, but you deliver the service through manual work thatβs βbehind the scenes.βΒ
- Concierge MVP β A manual-first MVP similar to the βWizard of Oz,β but you donβt fake the product.Β
- Kickstarter/pre-order pages β You can test market demand by getting early birds of your product to signΒ up.Β
However you choose to build your MVP, remember that it needs to help verify your value hypothesis.Β
Step 4. Test your minimum viable product with realΒ usersΒ
In this stage of finding PMF, you need to gather as much feedback for your product as you can. Donβt stop at positive or negative feedback. Try to dig deeper and understand what your users like or dislike and what they want to change.Β
Platforms like SurveyMonkey, UserTesting, or Remesh can help you reach the right demographics to conduct user testing and/or interviews.Β
You can also post your MVP to online communities relevant to your productβs target audience or share it on platforms like Product Hunt.Β
For products existing on the market, depending on how long your product has been around, signs of PMF (or the lack of it) can be seen in customer satisfaction and engagement. Consider these indicators:
- NPS score β Run a Net Promoter Score survey among your users. If your users are not satisfied with the product, itβs unlikely they will recommend it to their friends. Hence, you have a low chance of growing organically via word ofΒ mouth.Β
- The 40% rule β Like the NPS survey, this is about just one question: βHow would you feel if you could no longer use [product]?β The possible answers are a) very disappointed, b) somewhat disappointed, c) not disappointed (it isnβt really that useful), and d) N/AβI no longer use [product]. If at least 40% of your users choose the first option, there is a great chance youβve achieved PMF.
- Cohort retention rate β This is reserved only for products designed to be used over a longer period of time. The idea is this: If your paid customers stay with your product, thatβs a sign of PMF. The ideal retention rate can vary, depending on the type of product and industry. Read more about retention rate benchmarks here.
Step 5. Learn from your users and iterate
At this final stage of the process, you should be able to answer two questions: Did your MVP prove or disprove the value hypothesis? And what can you do to make your future product better?
A negative result of your value hypothesis experiment is not the end of the world. Depending on the feedback, you may make some tweaks and restate your hypothesis. Then, start the process all overΒ again.Β
One famous example of that is bubble wrap. It didnβt catch on as a new type of wallpaper or house insulation.Β

Also, some business ideas can just be ahead of their time (e.g., due to technical reasons or current legislation). You may need to try again some otherΒ time.Β
However, some business ideas are just bad, and thereβs really nothing you can do about it. On the bright side, if you discover this issue early, youβll save yourself a great deal of time andΒ money.Β
But if you are right all along and your MVP survives the confrontation with users, then youβll have a good chance of succeeding. In other words, itβs likely you foundΒ PMF.Β
Before you turn your MVP into a fully functional product and deliver it to the market, itβs a good idea to make a couple more iterations to tackle all of the feedback youβve received. What you want to hear from your users is your MVP is easy to use and provides a valuable solution.
Finally, as weβve seen with our PMF examples, PMF can be a temporary thing. You may lose at some point, like Kutol (Play-Doh), or may need to expand on your initial idea like Ahrefs.Β
Final thoughts
The market always wins. A great product will fail in an unfavorable market, and a bad product will sooner or later be marginalized in a thriving market.Β
Thatβs why choosing a market where users have a real, meaningful problem, launching the product quickly, and iterating it based on the feedback matter so much. Succeeding at that stage is a sign you can start working on the next steps: hiring more people and acquiring more customers.Β
On a final note, itβs good to keep in mind that not all user feedback is created equal. You donβt need to make all of your usersβ wishes come true. Focus on doable improvements and things that go along with your product vision.Β
Got questions or comments? Ping me on Twitter.
SEO
4 Ways To Try The New Model From Mistral AI

In a significant leap in large language model (LLM) development, Mistral AI announced the release of its newest model, Mixtral-8x7B.
magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%https://t.co/uV4WVdtpwZ%3A6969%2Fannounce&tr=http%3A%2F%https://t.co/g0m9cEUz0T%3A80%2Fannounce
RELEASE a6bbd9affe0c2725c1b7410d66833e24
β Mistral AI (@MistralAI) December 8, 2023
What Is Mixtral-8x7B?
Mixtral-8x7B from Mistral AI is a Mixture of Experts (MoE) model designed to enhance how machines understand and generate text.
Imagine it as a team of specialized experts, each skilled in a different area, working together to handle various types of information and tasks.
A report published in June reportedly shed light on the intricacies of OpenAIβs GPT-4, highlighting that it employs a similar approach to MoE, utilizing 16 experts, each with around 111 billion parameters, and routes two experts per forward pass to optimize costs.
This approach allows the model to manage diverse and complex data efficiently, making it helpful in creating content, engaging in conversations, or translating languages.
Mixtral-8x7B Performance Metrics
Mistral AIβs new model, Mixtral-8x7B, represents a significant step forward from its previous model, Mistral-7B-v0.1.
Itβs designed to understand better and create text, a key feature for anyone looking to use AI for writing or communication tasks.
New open weights LLM from @MistralAI
params.json:
β hidden_dim / dim = 14336/4096 => 3.5X MLP expand
β n_heads / n_kv_heads = 32/8 => 4X multiquery
β “moe” => mixture of experts 8X top 2 πLikely related code: https://t.co/yrqRtYhxKR
Oddly absent: an over-rehearsed⦠https://t.co/8PvqdHz1bR pic.twitter.com/xMDRj3WAVh
β Andrej Karpathy (@karpathy) December 8, 2023
This latest addition to the Mistral family promises to revolutionize the AI landscape with its enhanced performance metrics, as shared by OpenCompass.
What makes Mixtral-8x7B stand out is not just its improvement over Mistral AIβs previous version, but the way it measures up to models like Llama2-70B and Qwen-72B.
Itβs like having an assistant who can understand complex ideas and express them clearly.
One of the key strengths of the Mixtral-8x7B is its ability to handle specialized tasks.
For example, it performed exceptionally well in specific tests designed to evaluate AI models, indicating that itβs good at general text understanding and generation and excels in more niche areas.
This makes it a valuable tool for marketing professionals and SEO experts who need AI that can adapt to different content and technical requirements.
The Mixtral-8x7Bβs ability to deal with complex math and coding problems also suggests it can be a helpful ally for those working in more technical aspects of SEO, where understanding and solving algorithmic challenges are crucial.
This new model could become a versatile and intelligent partner for a wide range of digital content and strategy needs.
How To Try Mixtral-8x7B: 4 Demos
You can experiment with Mistral AIβs new model, Mixtral-8x7B, to see how it responds to queries and how it performs compared to other open-source models and OpenAIβs GPT-4.
Please note that, like all generative AI content, platforms running this new model may produce inaccurate information or otherwise unintended results.
User feedback for new models like this one will help companies like Mistral AI improve future versions and models.
1. Perplexity Labs Playground
In Perplexity Labs, you can try Mixtral-8x7B along with Meta AIβs Llama 2, Mistral-7b, and Perplexityβs new online LLMs.
In this example, I ask about the model itself and notice that new instructions are added after the initial response to extend the generated content about my query.


While the answer looks correct, it begins to repeat itself.


The model did provide an over 600-word answer to the question, βWhat is SEO?β
Again, additional instructions appear as βheadersβ to seemingly ensure a comprehensive answer.


2. Poe
Poe hosts bots for popular LLMs, including OpenAIβs GPT-4 and DALLΒ·E 3, Meta AIβs Llama 2 and Code Llama, Googleβs PaLM 2, Anthropicβs Claude-instant and Claude 2, and StableDiffusionXL.
These bots cover a wide spectrum of capabilities, including text, image, and code generation.
The Mixtral-8x7B-Chat bot is operated by Fireworks AI.


Itβs worth noting that the Fireworks page specifies it is an βunofficial implementationβ that was fine-tuned for chat.
When asked what the best backlinks for SEO are, it provided a valid answer.


Compare this to the response offered by Google Bard.


3. Vercel
Vercel offers a demo of Mixtral-8x7B that allows users to compare responses from popular Anthropic, Cohere, Meta AI, and OpenAI models.


It offers an interesting perspective on how each model interprets and responds to user questions.


Like many LLMs, it does occasionally hallucinate.


4. Replicate
The mixtral-8x7b-32 demo on Replicate is based on this source code. It is also noted in the README that βInference is quite inefficient.β


In the example above, Mixtral-8x7B describes itself as a game.
Conclusion
Mistral AIβs latest release sets a new benchmark in the AI field, offering enhanced performance and versatility. But like many LLMs, it can provide inaccurate and unexpected answers.
As AI continues to evolve, models like the Mixtral-8x7B could become integral in shaping advanced AI tools for marketing and business.
Featured image: T. Schneider/Shutterstock
SEO
OpenAI Investigates ‘Lazy’ GPT-4 Complaints On Google Reviews, X

OpenAI, the company that launched ChatGPT a little over a year ago, has recently taken to social media to address concerns regarding the βlazyβ performance of GPT-4 on social media and Google Reviews.

This move comes after growing user feedback online, which even includes a one-star review on the companyβs Google Reviews.
OpenAI Gives Insight Into Training Chat Models, Performance Evaluations, And A/B Testing
OpenAI, through its @ChatGPTapp Twitter account, detailed the complexities involved in training chat models.


The organization highlighted that the process is not a βclean industrial processβ and that variations in training runs can lead to noticeable differences in the AIβs personality, creative style, and political bias.
Thorough AI model testing includes offline evaluation metrics and online A/B tests. The final decision to release a new model is based on a data-driven approach to improve the βrealβ user experience.
OpenAIβs Google Review Score Affected By GPT-4 Performance, Billing Issues
This explanation comes after weeks of user feedback about GPT-4 becoming worse on social media networks like X.
Idk if anyone else has noticed this, but GPT-4 Turbo performance is significantly worse than GPT-4 standard.
I know it’s in preview right now but it’s significantly worse.
β Max Weinbach (@MaxWinebach) November 8, 2023
There has been discussion if GPT-4 has become “lazy” recently. My anecdotal testing suggests it may be true.
I repeated a sequence of old analyses I did with Code Interpreter. GPT-4 still knows what to do, but keeps telling me to do the work. One step is now many & some are odd. pic.twitter.com/OhGAMtd3Zq
β Ethan Mollick (@emollick) November 28, 2023
Complaints also appeared in OpenAIβs community forums.


The experience led one user to leave a one-star rating for OpenAI via Google Reviews. Other complaints regarded accounts, billing, and the artificial nature of AI.


A recent user on Product Hunt gave OpenAI a rating that also appears to be related to GPT-4 worsening.


GPT-4 isnβt the only issue that local reviewers complain about. On Yelp, OpenAI has a one-star rating for ChatGPT 3.5 performance.
OpenAI is now only 3.8 stars on Google Maps and a dismal 1 star on Yelp!
GPT-4βs degradation has really hurt their rating. Hope the business survives.https://t.co/RF8uJH1WQ5 pic.twitter.com/OghAZLCiVu
β Nate Chan (@nathanwchan) December 9, 2023
The complaint:


In related OpenAI news, the review with the most likes aligns with recent rumors about a volatile workplace, alleging that OpenAI is a βCutthroat environment. Not friendly. Toxic workers.β


The reviews voted the most helpful on Glassdoor about OpenAI suggested that employee frustration and product development issues stem from the companyβs shift in focus on profits.


This incident provides a unique outlook on how customer and employee experiences can impact any business through local reviews and business ratings platforms.


Google SGE Highlights Positive Google Reviews
In addition to occasional complaints, Google reviewers acknowledged the revolutionary impact of OpenAIβs technology on various fields.
The most positive review mentions about the company appear in Google SGE (Search Generative Experience).


Conclusion
OpenAIβs recent insights into training chat models and response to public feedback about GPT-4 performance illustrate AI technologyβs dynamic and evolving nature and its impact on those who depend on the AI platform.
Especially the people who just received an invitation to join ChatGPT Plus after being waitlisted while OpenAI paused new subscriptions and upgrades. Or those developing GPTs for the upcoming GPT Store launch.
As AI advances, professionals in these fields must remain agile, informed, and responsive to technological developments and the publicβs reception of these advancements.
Featured image: Tada Images/Shutterstock
SEO
ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites

ChatGPT Plus subscriptions and upgrades remain paused after a surge in demand for new features created outages.
Some users who signed up for the waitlist have received invites to join ChatGPT Plus.

This has resulted in a few shares of the link that is accessible for everyone. For now.
Found a hack to skip chatGPT plus wait list.
Follow the steps
β login to ChatGPT
β now if you click on upgrade
β Signup for waitlist(may not be necessary)
β now change the URL to https://t.co/4izOdNzarG
β Wallah you are in for payment #ChatGPT4 #hack #GPT4 #GPTPlus pic.twitter.com/J1GizlrOAxβ Ashish Mohite is building Notionpack Capture (@_ashishmohite) December 8, 2023
RELATED: GPT Store Set To Launch In 2024 After βUnexpectedβ Delays
In addition to the invites, signs that more people are getting access to GPTs include an introductory screen popping up on free ChatGPT accounts.


Unfortunately, they still arenβt accessible without a Plus subscription.


You can sign up for the waitlist by clicking on the option to upgrade in the left sidebar of ChatGPT on a desktop browser.


OpenAI also suggests ChatGPT Enterprise for those who need more capabilities, as outlined in the pricing plans below.


Why Are ChatGPT Plus Subscriptions Paused?
According to a post on X by OpenAIβs CEO Sam Altman, the recent surge in usage following the DevDay developers conference has led to capacity challenges, resulting in the decision to pause ChatGPT Plus signups.
we are pausing new ChatGPT Plus sign-ups for a bit π
the surge in usage post devday has exceeded our capacity and we want to make sure everyone has a great experience.
you can still sign-up to be notified within the app when subs reopen.
β Sam Altman (@sama) November 15, 2023
The decision to pause new ChatGPT signups follows a week where OpenAI services β including ChatGPT and the API β experienced a series of outages related to high-demand and DDoS attacks.
Demand for ChatGPT Plus resulted in eBay listings supposedly offering one or more months of the premium subscription.
chatgpt plus accounts selling ebay for a premium π«‘πΊπΈ https://t.co/VdN8tuexKM pic.twitter.com/W522NGHsRV
β surya (@sdand) November 15, 2023
When Will ChatGPT Plus Subscriptions Resume?
So far, we donβt have any official word on when ChatGPT Plus subscriptions will resume. We know the GPT Store is set to open early next year after recent boardroom drama led to βunexpected delays.β
Therefore, we hope that OpenAI will onboard waitlisted users in time to try out all of the GPTs created by OpenAI and community builders.
What Are GPTs?
GPTs allow users to create one or more personalized ChatGPT experiences based on a specific set of instructions, knowledge files, and actions.
Search marketers with ChatGPT Plus can try GPTs for helpful content assessment and learning SEO.
Two SEO GPTs I’ve created for assessment + learning ππ
1. Content Helpfulness and Quality SEO Analyzer: Assess a page content helpfulness, relevance, and quality for your targeted query based on Google’s guidelines vs your competitors and get tips: https://t.co/LsoP2UhF4N pic.twitter.com/O77MHiqwOq
β Aleyda Solis ποΈ (@aleyda) November 12, 2023
2. The https://t.co/IFmKxxVDpW SEO Teacher: A friendly SEO expert teacher who will help you to learn SEO using reliable https://t.co/sCZ03C7fzq resources: https://t.co/UrMPUYwblH
I hope they’re helpful ππ€©
PS: Love how GPT opens up to SO much opportunity π€― pic.twitter.com/yqKozcZTDc
β Aleyda Solis ποΈ (@aleyda) November 12, 2023
There are also GPTs for analyzing Google Search Console data.
oh wow. I think this GPT works.
Export data from GSC comparing keyword rankings before and after an update and upload it to ChatGPT and it will spit out this scatter plot for you.
It’s an easy way to see if most of your keyword declined or improved.
This site was impacted by⦠pic.twitter.com/wFGSnonqoZ
β Marie Haynes (@Marie_Haynes) November 9, 2023
And GPTs that will let you chat with analytics data from 20 platforms, including Google Ads, GA4, and Facebook.
Google search has indexed hundreds of public GPTs. According to an alleged list of GPT statistics in a GitHub repository, DALL-E, the top GPT from OpenAI, has received 5,620,981 visits since its launch last month. Included in the top 20 GPTs is Canva, with 291,349 views.
Β
Weighing The Benefits Of The Pause
Ideally, this means that developers working on building GPTs and using the API should encounter fewer issues (like being unable to save GPT drafts).
But it could also mean a temporary decrease in new users of GPTs since they are only available to Plus subscribers β including the ones I tested for learning about ranking factors and gaining insights on E-E-A-T from Googleβs Search Quality Rater Guidelines.


Featured image: Robert Way/Shutterstock
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