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How to Achieve Product-Market Fit (5 Steps)



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 will face.

Product-market fit is a concept that aims to solve various startup problems by aspiring to be “the only thing that matters.” Focusing on this concept should put any startup on the right track, no matter the circumstances. 

In this article, we’re going to take a closer look at this widely discussed concept. Here’s what we’ll address:

What is product-market fit?

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. 

The usual sign of achieving PMF is when people are willing to buy the product (even if it’s not perfect yet), actively use it, and recommend it to others. 

Why is product-market fit important?

Building a successful product is a matter of doing the right things in the right order and focusing on what truly matters. 

Just as houses need to be built from the ground up, businesses should build good foundations before going any further. 

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. 

Simply put, without PMF, there is no sustainable growth. 

Examples of product-market fit

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 let’s look at some examples of businesses finding their PMF. 


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. 

Ahrefs' value proposition

Ahrefs’ five core tools.

But in the early days, Ahrefs was just a single tool built for backlink analysis (which is only one aspect of SEO). 

Recommended reading: SEO: The Complete Guide for Beginners

Ahrefs' value proposition in 2011

Ahrefs’ value proposition in 2011.

Ahrefs’ founding team focused solely on customer satisfaction of the first product. There was no marketing or sales team in the beginning. 

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 its team.

It’s important to note Ahrefs didn’t stop at the initial PMF. To stay competitive and on top of the market’s demand, it expanded the functionality from a single-purpose SEO tool to a full-blown SEO toolkit. 


Meet Glitch, the progenitor of Slack that wasn’t even a messaging app. Glitch was a browser-based online multiplayer game launched in 2012, and it looked like this:

Glitch app

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 take over. 

Slack found its PMF by turning into a completely different product. In startup lingo, that is called a “pivot.”

The Glitch game didn’t see the desired success. But in light of its spin-off’s success, that doesn’t matter at all. The important part is to understand your lesson quickly and focus on things that actually work.


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 heaters—a common problem in the 1930s. 

The business was great until the coal heaters were substituted by “cleaner” gas and oil heaters. That’s how the company lost its original PMF. 

But it didn’t give up. Legend has it that one of the founders’ 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. 

With that, the company discovered its new PMF and has held on to it ever since. 

Kutol wall cleaner

Before and after. In the 1950s, the wall cleaner ‘Kutol’ tried to regain its PMF. In doing so, it became a successful toy.

Five steps to achieve product-market fit

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. 

If you want to learn about measuring PMF for an already existing product, jump to step four.

Step 1. Formulate the value hypothesis 

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 user’s life. 

A value hypothesis may look something like 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. 

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 the 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. 

Let’s say your startup wants to offer an online solution for delayed and canceled flight compensation. Since this will be an online product, you’ll want to see how often people search for queries related to that problem. With Ahrefs’ Keywords Explorer, that information is only a few clicks away. 

Delayed flight compensation search volume

Flight delay compensation’ 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 product 

Once you’ve clearly defined the problem you want to solve, the next step is identifying the set of features that will solve the customer’s 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. 

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. 

It’s 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. 

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. 

Suppose you want to build a project management tool. Let’s use Ahrefs’ Site Explorer and look at the Paid pages report for

Asana landing pages

And let’s see the landing pages for product features that get the most organic traffic in the Top pages report: 

Top paid landing pages for Asana

You can infer that building forms and timeline features into your project management app is something to seriously consider. If it’s good for your competitor, it could be good for your product as well (on top of your unique value proposition). 

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 don’t 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 exist yet. 

Buffer MVP

Dropbox validated its business idea by creating two videos about the product. The first video didn’t even show the product.

Now, let’s look at a few ideas on how you can build an MVP (besides developing a fully functional product):

  • Ad campaigns – You can produce various types of creative content for specific audiences to see which aspects of your product appeal to them the most. 
  • Prototypes – 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. 
  • Landing pages – 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 – This method allows you to dive deeper into how your prospects would react to your product with the least possible effort.
  • “Wizard of Oz” MVP – Customers think they are experiencing a real product, but you deliver the service through manual work that’s “behind the scenes.” 
  • Concierge MVP – A manual-first MVP similar to the “Wizard of Oz,” but you don’t fake the product. 
  • Kickstarter/pre-order pages – You can test market demand by getting early birds of your product to sign up. 

However you choose to build your MVP, remember that it needs to help verify your value hypothesis. 

Step 4. Test your minimum viable product with real users 

In this stage of finding PMF, you need to gather as much feedback for your product as you can. Don’t stop at positive or negative feedback. Try to dig deeper and understand what your users like or dislike and what they want to change. 

Platforms like SurveyMonkey, UserTesting, or Remesh can help you reach the right demographics to conduct user testing and/or interviews. 

You can also post your MVP to online communities relevant to your product’s target audience or share it on platforms like Product Hunt. 

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 – Run a Net Promoter Score survey among your users. If your users are not satisfied with the product, it’s unlikely they will recommend it to their friends. Hence, you have a low chance of growing organically via word of mouth. 
  • The 40% rule – Like the NPS survey, this is about just one question: “How would you feel if you could no longer use [product]?” The possible answers are a) very disappointed, b) somewhat disappointed, c) not disappointed (it isn’t really that useful), and d) N/A—I no longer use [product]. If at least 40% of your users choose the first option, there is a great chance you’ve achieved PMF.
  • Cohort retention rate – 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, that’s 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 over again. 

One famous example of that is bubble wrap. It didn’t catch on as a new type of wallpaper or house insulation. 

Bubble wrap wallpaper

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 other time. 

However, some business ideas are just bad, and there’s really nothing you can do about it. On the bright side, if you discover this issue early, you’ll save yourself a great deal of time and money. 

But if you are right all along and your MVP survives the confrontation with users, then you’ll have a good chance of succeeding. In other words, it’s likely you found PMF. 

Before you turn your MVP into a fully functional product and deliver it to the market, it’s a good idea to make a couple more iterations to tackle all of the feedback you’ve received. What you want to hear from your users is your MVP is easy to use and provides a valuable solution.

Finally, as we’ve 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. 

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. 

That’s 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. 

On a final note, it’s good to keep in mind that not all user feedback is created equal. You don’t need to make all of your users’ wishes come true. Focus on doable improvements and things that go along with your product vision. 

Got questions or comments? Ping me on Twitter.

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4 Ways To Try The New Model From Mistral AI




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.

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 OpenAI’s 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 AI’s new model, Mixtral-8x7B, represents a significant step forward from its previous model, Mistral-7B-v0.1.

It’s designed to understand better and create text, a key feature for anyone looking to use AI for writing or communication tasks.

This latest addition to the Mistral family promises to revolutionize the AI landscape with its enhanced performance metrics, as shared by OpenCompass.

Mixtral-8x7B: 4 Ways To Try The New Model From Mistral AI

What makes Mixtral-8x7B stand out is not just its improvement over Mistral AI’s previous version, but the way it measures up to models like Llama2-70B and Qwen-72B.

mixtral-8x7b performance metrics compared to llama 2 open source ai modelsmixtral-8x7b performance metrics compared to llama 2 open source ai models

It’s 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 it’s 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-8x7B’s 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 AI’s new model, Mixtral-8x7B, to see how it responds to queries and how it performs compared to other open-source models and OpenAI’s 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 AI’s Llama 2, Mistral-7b, and Perplexity’s 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.

mixtral-8x7b perplexity labs playgroundScreenshot from Perplexity, December 2023mixtral-8x7b perplexity labs playground

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

mixtral-8x7b errorsScreenshot from Perplexity Labs, December 2023mixtral-8x7b errors

The model did provide an over 600-word answer to the question, “What is SEO?”

Again, additional instructions appear as “headers” to seemingly ensure a comprehensive answer.

what is seo by mixtral-8x7bScreenshot from Perplexity Labs, December 2023what is seo by mixtral-8x7b

2. Poe

Poe hosts bots for popular LLMs, including OpenAI’s GPT-4 and DALL·E 3, Meta AI’s Llama 2 and Code Llama, Google’s PaLM 2, Anthropic’s 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.

poe bot for mixtral-8x7b firebaseScreenshot from Poe, December 2023poe bot for mixtral-8x7b firebase

It’s worth noting that the Fireworks page specifies it is an “unofficial implementation” that was fine-tuned for chat.

When asked what the best backlinks for SEO are, it provided a valid answer.

mixtral-8x7b poe best backlinks responseScreenshot from Poe, December 2023mixtral-8x7b poe best backlinks response

Compare this to the response offered by Google Bard.

Mixtral-8x7B: 4 Ways To Try The New Model From Mistral AIMixtral-8x7B: 4 Ways To Try The New Model From Mistral AI

Mixtral-8x7B: 4 Ways To Try The New Model From Mistral AIScreenshot from Google Bard, December 2023Mixtral-8x7B: 4 Ways To Try The New Model From Mistral AI

3. Vercel

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

vercel mixtral-8x7b demo compare gpt-4Screenshot from Vercel, December 2023vercel mixtral-8x7b demo compare gpt-4

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

mixtral-8x7b vs cohere on best resources for learning seoScreenshot from Vercel, December 2023mixtral-8x7b vs cohere on best resources for learning seo

Like many LLMs, it does occasionally hallucinate.

mixtral-8x7b hallucinationsScreenshot from Vercel, December 2023mixtral-8x7b hallucinations

4. Replicate

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

Mixtral-8x7B: 4 Ways To Try The New Model From Mistral AIScreenshot from Replicate, December 2023Mixtral-8x7B: 4 Ways To Try The New Model From Mistral AI

In the example above, Mixtral-8x7B describes itself as a game.


Mistral AI’s 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

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OpenAI Investigates ‘Lazy’ GPT-4 Complaints On Google Reviews, X




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 “lazy” performance of GPT-4 on social media and Google Reviews.

Screenshot from X, December 2023OpenAI Investigates ‘Lazy’ GPT-4 Complaints On Google Reviews, X

This move comes after growing user feedback online, which even includes a one-star review on the company’s 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.

chatgpt openai a/b testingScreenshot from X, December 2023chatgpt openai a/b testing

The organization highlighted that the process is not a “clean industrial process” and that variations in training runs can lead to noticeable differences in the AI’s 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 “real” user experience.

OpenAI’s 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.

Complaints also appeared in OpenAI’s community forums.

openai community forums gpt-4 user feedbackScreenshot from OpenAI, December 2023openai community forums gpt-4 user feedback

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.

openai google reviews star rating Screenshot from Google Reviews, December 2023openai google reviews star rating

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

openai reviewsScreenshot from Product Hunt, December 2023openai reviews

GPT-4 isn’t the only issue that local reviewers complain about. On Yelp, OpenAI has a one-star rating for ChatGPT 3.5 performance.

The complaint:

yelp openai chatgpt reviewScreenshot from Yelp, December 2023yelp openai chatgpt review

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

google review for openai toxic workersScreenshot from Google Reviews, December 2023google review for openai toxic workers

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

openai employee review on glassdooropenai employee review on glassdoor

openai employee reviewsScreenshots from Glassdoor, December 2023openai employee reviews

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

openai inc google business profile local serps google reviewsScreenshot from Google, December 2023openai inc google business profile local serps google reviews

Google SGE Highlights Positive Google Reviews

In addition to occasional complaints, Google reviewers acknowledged the revolutionary impact of OpenAI’s technology on various fields.

The most positive review mentions about the company appear in Google SGE (Search Generative Experience).

Google SGE response on OpenAIScreenshot from Google SGE, December 2023Google SGE response on OpenAI


OpenAI’s recent insights into training chat models and response to public feedback about GPT-4 performance illustrate AI technology’s 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 public’s reception of these advancements.

Featured image: Tada Images/Shutterstock

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ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites




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.

Screenshot from Gmail, December 2023ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites

This has resulted in a few shares of the link that is accessible for everyone. For now.

RELATED: GPT Store Set To Launch In 2024 After ‘Unexpected’ 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.

ChatGPT Plus Upgrades Paused; Waitlisted Users Receive InvitesScreenshot from ChatGPT, December 2023ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites

Unfortunately, they still aren’t accessible without a Plus subscription.

chatgpt plus subscriptions upgrades paused waitlistScreenshot from ChatGPT, December 2023chatgpt plus subscriptions upgrades paused waitlist

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

ChatGPT Plus Upgrades Paused; Waitlisted Users Receive InvitesScreenshot from ChatGPT, December 2023ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites

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

ChatGPT Plus Upgrades Paused; Waitlisted Users Receive InvitesScreenshot from OpenAI, December 2023ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites

Why Are ChatGPT Plus Subscriptions Paused?

According to a post on X by OpenAI’s 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.

The decision to pause new ChatGPT signups follows a week where OpenAI services – including ChatGPT and the API – 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.

When Will ChatGPT Plus Subscriptions Resume?

So far, we don’t 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 “unexpected delays.”

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.

There are also GPTs for analyzing Google Search Console data.

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.


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 – including the ones I tested for learning about ranking factors and gaining insights on E-E-A-T from Google’s Search Quality Rater Guidelines.

custom gpts for seoScreenshot from ChatGPT, November 2023custom gpts for seo

Featured image: Robert Way/Shutterstock

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