Startup Metrics That Help You Prove Product-Market Fit

Imagine posting your iOS app on the App Store. In a month, your startup metrics look as follows: 375k downloads and 180k active users! Seems impressive, doesn’t it? Does it mean there is a product-market fit for your app? Those metrics are from a startup called Fling. The investors put $21 million into it, but the app did not generate any revenue at all. In fact, there were a few instances it got removed from the App Store for failed moderation of sexually explicit content. On top of that, they also failed to pay their moderation team because they ran out of money. According to Alex Schultz:

“The number one problem I’ve seen for startups, is they don’t actually have product/market fit, when they think they do.”

Why is this so important? When a startup thinks that they have achieved product-market fit, it moves to the next set of items.  They start thinking about upselling, new marketing campaigns, inventing extras, expanding monetization, and so on. All these things fail as they are based on a non-existent PMF. As a result, they exhaust a startup’s resources.

In this blog post, we’ll unpack the concept of PMF and key performance indicators to confirm your startup’s hit PMF. 

Product-Market Fit: Definition

For a digital product, PMF occurs when a package of your UX, feature set, and value proposition finds a market with underserved needs and a clearly defined target customer.

The concept of PMF was developed by Andy Rachleff, and here is how it goes:

“A value hypothesis is an attempt to articulate the key assumption that underlies why a customer is likely to use your product. Identifying a compelling value hypothesis is what I call finding product/market fit. A value hypothesis identifies the features you need to build, the audience that’s likely to care, and the business model required to entice a customer to buy your product. Companies often go through many iterations before they find product/market fit, if they ever do.”

Actually, in our previous article on the value of early UX research, one of the findings was that hypothesis-driven development constitutes only 5-6% of all development practices. It is the least applied development methodology!

So, according to the quote from A. Rachleff, finding product-market fit is an iterative process. Overall, it can be broken down into these 4 steps:

• Step 1 – Formulate a hypothesis about the value proposition – What pain point does your product solve?
• Step 2 – Clearly identify a target customer – Who do you solve this problem for?
• Step 3 – Develop a feature set with the UX – Does it deliver the hypothesized value proposition clearly?
• Step 4 – Evaluate economic outcomes – Does the customer value your product enough to actually pay?

Let’s align these steps with startup metrics that will confirm PMF with a high degree of confidence.

Startup Metrics and PMF

For each of these steps of achieving PMF, there are different startup metrics:

• Value proposition can be measured with the activation metrics.
• Target customer implies a set of acquisition metrics.
• Feature set and UX are assessed with the help of engagement/retention metrics.
• Finally, economic viability is measured with monetization metrics.

Certainly, the set of metrics will vary depending on the kind of business. For instance, Facebook reached PMF when it was a college student network. They still did not have a viable monetization at that point, but for a social media app, the viability lies in virality and network effects. 

Fling Case Study: Missing Key Performance Indicators

At the start of the article, we presented startup metrics from the Fling case study. Let’s now examine how the Fling startup should have gone about verifying the PMF. We’ll align it with the 4-Step Framework from above. Just a note that we’ll switch the order of steps 1 and 2 to match the traditional marketing funnel: acquisition => activation => retention => revenue. 

PMF Key Performance Indicators for Acquisition

For the Fling case study, downloads were a vanity metric. Similarly, it would be a vanity metric for any B2C app that offers a free tier. It might be an install out of curiosity or hype, or even a plain accident. Downloads, in most cases, simply indicate the intent to try something out, and not to use it. Plus, you generally can’t tell if that is a target user who downloads your app. 

However, if your app is a premium app, a user is already showing a willingness to pay. For a paid app, downloads are a relevant PMF metric, not a vanity one. For SaaS apps, even if it is a pilot sign-up, it is indicative of the intent to use and make your product a part of their workflows. If your digital product operates under a license, like a sort of algorithm or paid API, sign-ups are also quality acquisition metrics. 

However, for most B2C businesses like marketplaces or social apps, simply downloads or sign-ups would be vanity metrics. For social apps, it is better to track the K-factor. For other B2C apps, the NPS score is the way to go. 

In short, these startup metrics are valid for determining acquisition PMF:

• downloads/sign-ups for paid apps, licensed software, paid APIs/algorithms, SaaS, and other B2B digital products;
• K-factor for social and social media apps;
• NPS for other B2C apps such as marketplaces, consumer platforms, and online services.

PMF Startup Metrics for Activation

In the previous step, you confirm that your product is reaching the right user with the existing problem your product is trying to solve. In this step, you are checking how fast the user derives the value from your app in connection to their problem. Again, simply completing onboarding is not an indicator of PMF activation. If you installed the Uber app and checked that there are cars available, it is not an activation. Activation is when you actually complete a ride. 

In the Fling case study, we talked about the ‘core loop’. For a social app, it can be a chain of meaningful actions. So, it can be: take a photo => send it => receive ‘hi’ from those you sent to => have at least a 20-message conversation with one of those who responded. Think about the experience of the value: which actions would constitute fully experiencing the value of your app?

Overall, you should tailor this metric specifically to your business. In general, % of users who complete the core loop is a key startup metric to represent the value-tied functionality. 

• In a banking app, it would be % of users completing their transaction or placing a deposit.
• In a marketplace, it would be % of users completing a purchase.
• On a telehealth platform, % of users who received a consultation or paid for a doctor’s answer to a plain text question.
• For a fitness app, it can be % of users who completed a full workout session.

Key Performance Indicators to Evaluate the Feature Set and UX for PMF

After you evaluate the target user and the initial experience of value, you need to know: Does your existing feature set and user experience promote repeat engagement? For instance, after a user completes the core loop once, but then logs in just to check for updates. Maybe there is some sort of dashboard to check what others are doing. Or maybe the users simply return to explore other features. Many startups measure key performance indicators such as pages visited per session, screens viewed, or simply time spent in the app. These do not fairly represent the repeat value experience. Similar to the Fling’s downloads metrics, these would be vanity metrics as well. 

Instead, focus on the startup metrics to confirm PMF for retention/engagement. Here are such PMF metrics explained:

• Ratio of active sessions versus passive ones (e.g., those where users repeat the core loop, versus those where they are simply browsing, checking for updates, or returning based on notification).
• For collaborative tools, you can measure the number of collaborative interactions per session and the subsequent number of such quality return sessions. 
• For social apps, you might continue measuring the number of users completing the core loop on D7 and D30, or to additionally track the depth of engagement. For instance, maybe a user would not want new connections, but will continue experiencing value through developing an already-made connection.  

Monetization Startup Metrics for Product-Market Fit

Finally, having proved that you can reach your target audience with the value they need on a repeated basis, check the economics. Generally speaking, the economics of PMF do not yet imply a big revenue stream or a solid revenue model. Monetization while finding your PMF is more about proving that the target market does not simply enjoy your solution but is willing to pay for it as well. 

For instance, in the Fling case study, there was no revenue at all. Even low revenue, but from a substantial mass of users, would have been sufficient to prove the PMF. For consumer apps, even 1% to 5% of users count as substantial. So, even if 5% of the Fling users had spent $5 to $10 on stickers or virtual gifts, it would have been around 67.5k a month. 

For a B2B SaaS company, for investors and VCs, even only 10 paid users would be enough to demonstrate that there is a willingness to pay. Especially, if these 10 paid customers come from an overall small pool of users, it signals that the conversion is strong. Moreover, if you retain these 10 customers from month to month, your PMF signals a scalable business model. In plain terms, few users who have tried your product show willingness to pay and retain.

In terms of key startup metrics, it is often about:

• Conversion – the percentage of free users becoming paid ones.
• NRR – how much revenue comes from the retained users, or net revenue retention.
• ARPU – how much revenue each user generates on average.

Summary

Many startups do not yet benefit from hypothesis-driven development and use misleading metrics to determine product-market fit. The Fling case study demonstrated the misuse of the downloads metrics, active users count, and missing opportunities to confirm willingness to pay. While downloads can be a solid PMF indicator, it depends on the nature of the business. So, for a paid premium app, licensed software, or B2B business – downloads or signups will qualify as PMF-proofing key performance indicators. Overall, there are 4 steps in building your PMF discovery, and each step needs to align with the relevant metric. 

FAQ: Startup Metrics That Help You Prove Product-Market Fit