The Golden Metric for SaaS – $1 Burned for $1 of Recurring Revenue

Thinking more about the post from a couple weeks ago titled Evaluating a Startup Based on Cash Burned vs Recurring Revenue and how the same idea was brought up again two days ago in Bessemer’s 2017 State of the Cloud Report, I’ve come to believe that $1 of cash burned for $1 of net new recurring revenue is the Golden Metric for SaaS.

As an idea, it’s easy to understand.

As a metric, it’s easy to track.

As a way to create value, it’s excellent.

As a benchmark for entrepreneurs to measure against, it’s perfect.

Some startups will choose to burn more than $1 for each $1 of new new recurring revenue, but most won’t have that luxury. Startups that achieve scale, and burn $1 (or less!) for every $1 of net new recurring revenue, will do well for all stakeholders involved.

What else? What are some more thoughts on the Golden Metric for SaaS being $1 of cash burned for $1 of net new recurring revenue?

Bessemer’s 2017 State of the Cloud Report

There was so much good content at the SaaStr Annual that it’s going to time to get through it all. Next of the list is Bessemer’s 2017 State of the Cloud Report.

Here are a few notes from the Bessemer slide deck:

  • 40% of the market cap of publicly traded SaaS companies has already been acquired representing greater than $300 billion in value
  • Key questions from top CEOs:
    • How fast should I be growing?
    • How much should I burn?
    • How do I scale?
  • How fast should I be growing?
    • Dropbox is the fastest SaaS company ever to hit $1B in run rate (did it in eight years)
    • The pace is quickening for SaaS companies going from $1M – $100M in recurring revenue (5.3 years for top 25%, 7.3 years median, 10.6 years bottom 25%)
    • BVP Growth Benchmark for ARR
      • Good
        • $1 – $10M in four years
        • $1 – $100M in 10 years
      • Better
        • $1 – $10M in three years
        • $1 – $100M in 7 years
      • Best
        • $1 – $10M in two years
        • $1 – $100M in five years
  • How much should I burn?
    • Rule of 40 = % Annual Revenue Growth + % Profit Margins
    • Efficiency Score = % Annual CARR Growth + % Burn
    • BVP Efficiency Rule (> $30M ARR)
      • Expansion ($30 – $60M ARR) – 70% efficiency score
      • IPO (~$100M ARR) – 50% efficiency score
      • Public (>$150M ARR) – 30% efficiency score
    • BVP Efficiency Rule (< $30M ARR)
      • Net New ARR / Net Cash Burn > 1
      • Meaning, for every dollar burned, company needs $1 or more net new dollars of ARR
  • How do I scale?
    • Customer Acquisition Cost (CAC) Payback = Total Sales and Marketing Costs Last Quarter / New CMRR Added Last Quarter * % Gross Margin
    • Understanding Your Sales Model
      • SMB
        • CAC Payback 3-6 months
        • AVG ACV < $12k
        • Churn/Upsell < 3% monthly
      • Midmarket
        • CAC Payback 12 months
        • AVG ACV $12 – $50k
        • Churn/Upsell 1% monthly
      • Enterprise
        • CAC Payback 3-6 months
        • AVG ACV $50k+
        • Churn/Upsell < 1% monthly, upsell

Thanks to the team at Bessemer for putting together the great information. Every SaaS entrepreneur should read Bessemer’s 2017 State of the Cloud Report.

SaaS Numbers that Actually Matter

Continuing with 12 Key Levers of SaaS Success from David Skok at SaaStr, Mamoon Hamid gave an excellent presentation Numbers that Actually Matter. Finding Your North Star.

Here are a few notes from the presentation:

  • Quick Ratio (QR) = New MRR + Expansion MRR / Churned MRR + Contraction MRR
  • Goal is a Quick Ratio greater than 4
  • Product-market fit happens one customer at a time one month at a time
    • Mostly ignored any product-market fit metrics
  • Churn/Expansion/Contraction MRR is a lagging indicator of product-market fit
  • MRR is the price that the customer pays, the North Star is the value that they get
  • Focus on a leading indicator of the MRR decision
  • Your North Star measures the value you deliver
  • Bad: Mostly measuring price paid as opposed to value delivered
    • MRR, paid seats
  • Good: Measures value delivered in bulk
    • MAU, DAU, messages sent
  • Better: Unquestionably indicates Product Market fit has been reached with the customer
    • Number of users with L28 >= 16
    • Messages sent w/in 30 days in signup

Read the presentation Numbers that Actually Matter. Finding Your North Star. and figure out the North Star for your product.

12 Key Levers of SaaS Success from David Skok

David Skok of forEntrepreneurs and Matrix Partners has a great new slide deck up from his presentation at SaaStr Annual titled 12 Key Levers of SaaS Success.

Here are the 12 key levers:

  1. Product/market fit
  2. Top of the funnel flow
  3. Conversion rate
  4. CAC (customer acquisition cost)
  5. Number of sales people
  6. PPR (productivity per rep)
  7. Getting enough leads
  8. Pricing
  9. Customer retention rate
  10. Dollar retention rate
  11. Months to recover CAC
  12. Recruiting, onboarding & management

Haven’t read it yet? Head on over to 12 Key Levers of SaaS Success and read it now.

Evaluating a Startup Based on Cash Burned vs Recurring Revenue

In the SaaS world, one of the common best practices is to have the cost of customer acquisition be equal to or less than the first year’s revenue (or even better would be gross margin). So, if on average it costs $5,000 in fully loaded sales and marketing expense to acquire a customer that pays $5,000 per year, things are going well. After learning that heuristic, and working with a number of entrepreneurs, I’ve come to take it one step further and judge the success-to-date of a startup based on the amount of money burned all-time vs the annual recurring run-rate today, especially if it’s one to one.

While burning $1 to get $1 of recurring revenue might not sound like much, it’s actually really good. Think of a company that’s growing fast at $5 million recurring on $5 million burned all-time. In today’s market, that company is likely valued at $30M .- $40M (6-8x run-rate). Spending $5M to build a company worth that much is likely a good scenario for everyone involved including founders, employees, and investors. A common phrase in the startup world is “if the company sells for 10x the amount of money raised, everyone does well.” While a valuation of 10x the capital raised is excellent, consider the ratio of capital burned all-time to current recurring revenue as another metric to evaluate the success of a startup.

What else? What are some more thoughts on evaluating a startup based on cash burned vs recurring revenue?

Machine Learning and Marketing Automation

Continuing with yesterday’s post Machine Learning and the Startup World, a friend asked what machine learning applications might look like for marketing automation platforms (e.g. Pardot). Good question. Considering marketing automation platforms collect so much information about leads through email opens, form submissions, web page visits, ebook downloads, webinar signups, etc., there’s a tremendous amount of training data for machine learning to find insights.

Marketing automation is essentially human-defined rules based on what they think is best (e.g. send email A, wait two days, send email B, etc.) Machine learning “learns” over time and can figure out patterns humans can’t because there’s too many dimensions to analyze. Here are a few ideas on how to use machine learning in marketing automation:

  • Email Send Time – Analyze when leads opened emails in the past and automatically schedule future emails to be sent at the same time (time of day, day of week, etc.)
  • Email Message to Trigger – Analyze what emails (or other types of content) were most closely associated with pushing the lead through the current phase of the lifecycle (e.g. engaging with a sales rep) and trigger the email automatically (as opposed to human-defined static rules)
  • Lead to Opportunity Probability – Analyze every piece of historical data for leads that turned into opportunities (the training data or learning set) and come up with a probability for all other leads that haven’t turned into an opportunity based on how their behavior matches the training data
  • Lead to Customer Probability – Like the lead to opportunity probability, do the same thing for leads that became customers (not just those that had an opportunity in the pipeline associated with them) and come up with a probability that any given lead will become a customer

Machine learning has applications in all fields, especially marketing automation. Look for existing vendors to add this type of functionality as well as new vendors to emerge that take advantage of this new technology.

What else? What are some more ways machine learning can be applied to marketing automation?

The SaaS Metrics Framework

Updata Partners released their new SaaS Metrics Framework and it’s excellent. SaaS companies have a number of business model elements that are consistent from one company to another such that it’s possible to run them through a process and see how they stack up fairly quickly. Updata’s framework is one such model.

Here are a few notes from the SaaS Metrics Framework:

  • Two SaaS metrics that matter most: Gross Margin Payback Period (GMPP) and Return on Customer Acquisition Cost (rCAC)
  • GMPP is the number of months required to break even on the cost of acquiring a customer
  • rCAC incorporates the element of customer churn/retention into the equation and calculates the multiple of the acquisition cost provided by the lifetime gross profit of a customer
  • Good is GMPP under 18 months and rCAC above 3x
  • Great is GMPP under 12 months and rCAC above 5x
  • Cohort level analysis is necessary and must be run across at least three critical dimensions: Vintage, Product, and Channel
  • Metrics and sequence of analysis
    1. MRR – Monthly Recurring Revenue
    2. tCAC – Total Customer Acquisition Cost
    3. RGP – Recurring Gross Profit
    4. GMPP – Gross Margin Payback Period
    5. eLT – Expected Lifetime
    6. LTF – Lifetime Value
    7. rCAC – Return on Total Customer Acquisition Cost

One big takeaway: SaaS companies need to be thinking about many of the popular metrics like the SaaS Magic Number in the context of gross margin, not revenue. And, thankfully, gross margin should improve with scale. Want to understand SaaS unit economics better? Head over to SaaS Metrics Framework.

What else? What are some more thoughts on Updata’s SaaS Metrics Framework?