10 Metrics to Determine Product Market Fit

When you’ve got a product that your customers love and use repeatedly, over a period of time, you’ve got Product Market Fit. (And these customers cannot be just your dear mother, best friend, and the one neighbor you threatened with dire consequences.) Obviously, for strangers to use your product repeatedly, your product should provide enough VALUE i.e solve their problem in a satisfactory manner. And hopefully, the market (all your potential customers) is big enough to make it worth your effort.

The consequences of not knowing if you have product market fit or not is that you might scale too early. If you scale too early, you’ll be wasting your money because you’re still going to be dealing with a leaky bucket. On the other end, if you have product market fit but don’t know, you may miss your opportunity to capitalize on your market.

What to track?

To avoid being overwhelmed by what to measure and track, or to avoid being overcome by the desire to track everything (if you’re an engineer), here’s a good place to start

  1. Start with top level KPI & the drivers for the top level KPI (you learnt about it in growth goals)
  2. Funnel for these drivers
  3. CAC, LTV

In the beginning, you will track these as aggregates (again, start simple). But as you work on more nuanced optimizations, you'll branch into tracking the same metrics above for various segments of users. Some of these segments could be based on source/referrer, ad campaigns, landing page (users who arrived at your product app for different purposes will behave differently), sign up time (if you'd made product changes, different cohorts would've experienced your product differently, and hence might exhibit different behaviour), A/B test buckets (ex: if two sets of users had vastly different onboarding experience, their retention might be different)

Here’s the video that explains what metrics to track (slides) along with a handy dashboard ( that helps you see the most important components of your business in a single sheet (one view). And here’s how to get the data (slides) using various analytics tools like Mixpanel and Amplitude

Qualitative Test:

Sean Ellis 40% P/M Fit Test - At early stages, you might not have enough users for meaningful quantitative metrics, so this is a good place to start. Ask 50 customers (people who pay for or use your product consistently) how disappointed would they be if they could no longer use your product or feature. If at least 40% respond with “very disappointed” you may have product market fit.

Although the test above works for B2B as well, Sean Jacobsohn has created a set of questions and scoring system to evaluate the stage of your startup. And VC Christoph Janz shares his criteria for evaluating PMF for B2B startups, across several different parameters, depending on your target customer.

Quantitative Test:

Brian Balfour’s The Never Ending Road To Product Market Fit talks about tracking retention. As your volume of users increases, quantitative testing becomes meaningful, and a more accurate way to check P/M Fit. When you analyse the retention rate of a cohort over a period of time, if a significant portion of your early users are still engaged and using your product after a period of time, then you have a more quantifiable, and more reliable method, for measuring PMF.

Common Mistakes Founders Make

  • Tracking too many things (especially, if you’re an engineering team) overwhelming yourself with tracking - making it a priority -  becoming data driven instead of being data informed in early stages when you don't have enough data - - analysis paralysis
  • Spending unnecessary money on advanced analytics tools that don’t matter as this stage
  • Tracking vanity metrics

Fictitious Example

A couple of sample KPI dashboards &

Ten Metrics that Matter

The scope would revolve around 10 metrics that determine platform-market fitness. Monitoring these metrics helps you assess your traction (this is what you use with investors):

- cohort retention curves that flatten (stickiness);

- actives/reg > 25% (validates TAM);

- power user curve showing a smile -- with a big concentration of engaged users (you grow out from this strong core);

- viral factor >0.5 (enough to amplify other channels);

- dau/mau > 50% (it's part of a daily habit);

- market-by-market (or logo-by-logo, if SaaS) comparison where denser/older networks have higher engagement over

time (network effects);

- D1/D7/D30 that exceeds 60/30/15 (daily frequency);

- revenue or activity expansion on a per user basis over time -- indicates deeper engagement / habit formation;

- >60% organic acquisition with real scale (better to have zero CAC);

- for subscription, >65% annual retention (paying users are sticking);

- and >4x annual growth rate across topline metrics.

Related Links

Do you have P/M Fit, it's all about Retention - Casey Winters, Growth at Pinterest

Getting the Product Market Fit First & Then Growth  Hiten Shah, Co-Founder at Kissmetrics

Growth for Pre vs Post Product Market Fit Startups  Chandini Ammineni, Partner at 500 Startups

Achieving Product/Market Fit    Sean Ellis, CEO at GrowthHackers

10 Steps To Product Market Fit   Ash Maurya - Author and Creator of Lean Canvas

The Only Metric That Matters  Josh Elman, Partner at Greylock

Shake, Mix, Stir, KABAM! How One Startup Grew from $0 to $360MM in 4 Years  Holly Liu, Co-Founder & Chief of Staff at Kabam

Zero to Product/Market Fit Andrew Chen, Growth at Uber

Analytics for Startups Andy Young, EIR at 500 Startups 

Lean Analytics 101  Benjamin Yoskovitz, Founding Partner at Highline Beta

16 Startup Metrics   Andreessen Horowitz Blog

Measuring For Growth: Understanding Your Data & Next Steps  Jonathan Hsu, CFO at AppNexus

Measuring For B2B Engagement: How To Collect & Track Data Across Web & Mobile Devices  Diana Smith, Director of Marketing at Segment

Measuring for Revenue Attribution: Strategies for Tracking User Visits, Conversions & Spend Rate  Jamie Quint, Co-Founder at Interstate Analytics

Related Keywords: Key Performance Indicator, Vanity Metrics, Monthly Recurring Revenue (MRR),Annual Recurring Revenue (ARR), Lifetime Value(LTV), Month over month growth (MoM growth), churn, OMTM, Customer Acquisition Cost (CAC), Mixpanel, Kissmetrics, Retention, Cohort Analysis, Product Market Fit, Vanity Metrics, Leaky Bucket Theory,

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