Key Ideas

• Product-market fit can be viewed through three standardized analyses:
  • Growth Accounting
    • Categories that revenue can be bucketed into:
      • New: Gained from customers were first active in the present time period.
      • Churned: Lost when a customer who was active in the previous time period has no revenue in the present one.
      • Resurrected: Gained from customers who had churned at some point in the past (and thus generated no revenue in the previous time period) but resumed in the present.
      • Expansion: Gained from customers increasing revenue relative to the previous time period.
      • Contraction: Lost from customers decreasing (but not to zero, otherwise they would be churned) revenue relative to the previous time period.
      • Retained: Carried over by customers from the previous time period to the present one.
    • Revenue in current time period - Revenue(t) = retained(t) + new(t) + resurrected(t) + expansion(t)
    • Revenue in past time period - Revenue(t-1) = retained(t) + churned(t) + contraction(t)
    • Change in revenue across a time period - Revenue(t) – Revenue(t-1) = new(t) + expansion(t) + resurrected (t) – churned(t) – contraction(t)
    • Growth_rate ~ New_rate + Resurrected_rate + Expansion_rate – Contraction_rate – Churn_rate
      • Gross retention = retained(t) / revenue(t-1)
      • Quick ratio = [new(t) + resurrected(t) + expansion(t)] / [churned(t) + contraction(t)]
      • Net_Churn = [churned(t) + contraction(t) – resurrected(t) – expansion(t)] / revenue(t-1)
      • Growth_rate ~ New_rate – Net_churn
    • Revenue is only one quantity that we analyze using growth accounting.
  • Customer Cohorts
    • Lifetime value (LTV): the cumulative activity of a customer cohort at a fixed time period (e.g. 12 months) after the customer first uses the product.
    • Revenue retention: the percentage of a cohort’s initial activity that is retained at a fixed time period afterwards.
    • Customer (logo) retention: the percentage of a cohort’s initial customers that are retained (i.e. still using/paying for the product) at a fixed time period afterwards.
    • For all of the metrics above, there are two ways to visualize them as line graphs: a plot of each cohort as it ages, or a plot of the trend across cohorts at a fixed age.
  • Distribution of Product-Market Fit
    • In the case of revenue, this is typically done by inspecting the cumulative distribution function of monthly revenue.
    • Average contract value (ACV) is used at times but this quantity tends to be dragged around by outliers and often doesn’t illustrate the “typical” customer.
    • We want to know both the ACV as well as the overall distribution of contract values and the CDF enables us to see the median and other percentiles/quantiles.
• Product-market fit is not a binary black-and-white question, it is a spectrum. If you have high growth with healthy cohorts and reasonably distributed demand then you probably have product-market fit.