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
- Start with top level KPI & the drivers for the top level KPI (you learnt about it in growth goals)
- Funnel for these drivers
- 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 (bit.ly/kpi-sheet) 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
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.
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
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.
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,