Before we start going deep into SaaS Math, it’s important to level set on terminology. So without further ado, here are the key terms used. If you think I have missed any, please let me know.

AARRR: A pirate war cry or more importantly, an acronym coined by Dave McClure to summarize the flow of SaaS users from first activation to monetization and referral.

Activation: The first time someone uses your service.

Acquisition: A new user sign up. This does not necessarily mean a paid customer. It means a new user on a free trial or permanently free version. If you don’t have a free trial or free product and the only way for someone to use your product is to pay then acquisition for you is a new paid customer. In this series “user” will refer to people that don’t pay and “customers” will be people that do pay.

ARPU: Average Revenue Per User: Total revenue / # of paying customers.

CAC: Customer Acquisition Cost. Total costs of customer acquisition / # of new customers acquired. This should be calculated both for gross new customers and net new. Net new is net of customers that you lost in the period.

Churn: The % of users / customers that abandon the service over time. This can be measured weekly, monthly, quarterly, etc. You will want to measure churn for users and churn for customers (assuming you have a free trial or freemium product).

Customer churn: % of paying customers that cancel their subscription.

User churn: % of free users that stop using the service.

CLTV: Customer Lifetime Value. The expected total revenue from a customer over their lifetime less the cost of generating that revenue less the cost to acquire that customer.

Cohort: Also called cohort analysis or class analysis. A cohort is a group of users that are grouped together based on a common attribute. That could be the month they signed up, the source through which you acquired them, the method in which they use your service (web vs. mobile vs. desktop app), etc. Say, you’re looking at cohorts based on month of sign up. You can then look at usage and monetization patterns for those users over time. For example all users signing up in January are a cohort. You can then look at the % of them that use, subscribe for, churn out, cancel their account etc. in February, March, April, etc.

Conversion: Every time a user moves forward a step in your funnel from visitor (just visiting your web site) to user (signed up) to customer (paying you money) to referrer (helping bring you new users).

UV conversion: % of new unique visitors that become users.

Active conversion: % of users that use the service for the 1st time.

Paid conversion: % of free users that upgrade to a paid account.

Engagement metrics: These are softer metrics that are specific to your application that don’t measure core conversion but measure specific feature uses and overall engagement with your service. Examples include # of likes, session length, # of comments, # of connections, etc.

Freemium: A goto market strategy where you have a permanently free base version of your service. This, hopefully, replaces the need for a big marketing budget and reduces friction for user sign up enabling you to acquire lots of users. From that large user base you convert a small portion to a paid premium version. There are other freemium scenarios such as free content monetized by ads but in SaaS this is the primary meaning for freemium.

K Factor: Also known as “viral co-efficient“. For every active user how many new users do they bring on. If your K factor is > 1 then your user base grows virally or exponentially. This applies well for social games and freemium services that have a built in viral aspect that introduces the game or service to new potential users.

Retention: Subsequent usage of your service. Any usage after the initial (activation use). As you will learn, retention is the most important aspect of a successful SaaS business.

Retention rate: The % of users that continue using the service over time. This can be measured weekly, monthly, quarterly, etc.

Tenure: The # of months or years that you keep a paid customer. Calculated as 1 / churn rate.

Upgrade %: The % of customers that upgrade from you basic plan to a higher plan.

Category:
SaaS, SaaS Math
  • http://twitter.com/ramin Ramin

    Thanks, super-helpful!

  • http://PodiumVentures.com Cory Cleveland

    I answered part of my question when I found this! Thanks again. – C
    http://www.startupcfo.ca/2011/05/saas-math-slides

  • http://PodiumVentures.com Cory Cleveland

    Hey Mark,

    I really like this post. One question I have is have you seen any examples where these metrics have been used for valuation… I think we all too often hear "how many user's do you have". From a finance perspective, I'd rather be involved with a co. that has a low CAC, low churn and a high K factor…

    That said, do you have or know of any examples we can compare? Thanks! – C

    • http://startupcfo.ca Mark MacLeod

      Valuations are heavily driven by monthly recurring revenues. Usually a multiple of those with premium multiples given to companies that are growing MRR fast.

  • http://chaotic-flow.com Joel York

    Hi Mark,

    Nice list…

    Will be very interesting to see what you do with the cost/profitability of freemium customers and viral coefficient. I think the viability of a freemium model is a key challenge for a lot of SaaS startups.

    Only items I see missing are the complementary revenue/cost figures to CAC.
    ASP Average Sale Price (for both initial an upgrade purchases)
    ACS Average cost of service (to calculate profitability of freemium and get to CLTV)
    ………could also treat this as a fixed + variable component.

    Cheers,

    Joel

    • http://startupcfo.ca Mark MacLeod

      Joel,

      Great to hear from one of the authorities on SaaS. Thanks for the additions. Will update the post.

      And will definitely be tackling freemium viability head on. Being mostly on the b2b/ prosumer side my k factor experience is limited. So will draw on some outsiders when we get there.

      Mark

  • http://www.startable.com/ Healy Jones

    Cohort can be beyond time, it can also measure by user source. I track the users who came in from itunes as a cohort to see how they compare to Android or web users.

    • http://startupcfo.ca Mark MacLeod

      You're right Healy. Cohorts can be run based on any common attribute. I will update the post.

      • http://www.mediabadger.com Giles

        Would a "cohort" also be a market segment or do you see that defined differently?

  • http://robertsaric.com Rob Saric

    Nice summary list!