KPIs and Metrics for SaaS Success
Nov 2, 2018
“In any business, you want to start with the end in mind. Start with your goals, then design activities and tasks and programs to reach those objectives. That’s analytics-first business strategy.”
—Kirk Borne, Principal Data Scientist at Booz Allen Hamilton
Transforming data into business insights is a matter of knowing which performance indicators matter most to your company. The metrics you prioritize will depend on your business model, company size, outlook, and growth goals, but it’s helpful to have a baseline for what’s considered standard practice.
That’s why we compiled the following list of SaaS KPIs: to help you evaluate your own strategy for the coming year and beyond. This list of metrics used by actual SaaS companies is segmented by department for easy skimming, and each metric is accompanied by a handy formula.
To learn more about the companies who have found success with these and other SaaS performance metrics, check out this excellent and exhaustive article by Cobloom. (Don’t see your north star metric on the list? Tell us about it in the comments!)
Monthly Recurring Revenue (MRR): Measures the predictable and recurring revenue components of your subscription business over the course of a month. For any given month, sum up the recurring revenue generated by that month’s subscriptions.
Annualized Run Rate or Annual Recurring Revenue (ARR): Measures the predictable and recurring revenue components of your subscription business over the course of a year. For any given year, sum up the recurring revenue generated by that year’s subscriptions.
Customer Churn: Measures the rate at which existing customers cancel their subscription. For a given period t, divide the number of customers that cancelled their subscriptions in that period by the total number of customers at the start of the period.
% Churn = (# Churned Customerst) / (Total Customers at Startt)
% Annual Customer Churn Rate = (1- (1 - Monthly Churn Rate))12
Revenue Churn: Measures the rate at which monthly recurring revenue as a result of customer churn. Find the difference between current MRR for time t and past MRR, dividing that by the past MRR.
% Revenue Churn Rate = (MRRt-1 - MRRt) / MRRt-1
Bookings: Measures the total value of all new subscriptions made during a given period. Sum the value all acquired deals during period t, including future payments due per the agreement.
Bookingst = SUM(Value of New Dealst)
MRR Growth Rate: Measures the percent new MRR in a given period. Find the difference between the MRR for a given time t and the MRR for t-1, dividing by the latter and multiplying by 100 to get the percentage.
MRR Growth Rate = ((MRRt - MRRt-1) / MRRt-1) x 100
Net New MRR: Often more reliable than MRR growth rate, net new MRR shows the amount of new revenue generated each month (rather than the percentage). To find this, add the revenue gained from new subscriptions for time t to the revenue gained as a result of successful upselling and cross-selling for the same period. Subtract from that the churn MRR lost during that period.
Net New MRR = New MRR + Expansion MRR - Churn MRR
Burn Rate: Gross burn rate measures the rate of expenditure over time, and net burn rate measures revenue loss over time.
Gross Burn Ratet = Revenue Spentt
Net Burn Ratet = Gross Burn Ratet - Revenuet
Gross Margin: Measures the percentage of revenue remaining after the cost of service is subtracted. For a SaaS company, cost of service might include support expenses, application hosting fees, and software licensing fees.
Gross Margin % = (Revenuet - Cost of Servicet) / Revenuet
Unique Website Visitors: The number of distinct people who visit a site in a given period.
Email Subscribers: Number of individuals who have signed up to receive regularly scheduled emails or newsletters.
Marketing Qualified Leads (MQLs): The number of individuals or companies who have demonstrated an interest in the product and given identifying information.
Sales Qualified Leads (SQLs): The number of individuals or companies who have demonstrated an interest in the product and entered into the sales process, usually by requesting a demo or engaging in a sales conversation.
Opportunities: SQLs that have been vetted and found a good fit for the product.
Paying Customers: The number of companies or divisions of a company that have signed subscription contracts.
Free Trials and Demo Requests: The number of individuals or companies requesting to either try the product first-hand or see it in use.
Conversion Rates: The number of individuals or companies who make it to the next stage in the sales funnel. Examples of this include, but are not limited to: Visitor to Lead, Lead to Customer, and MQL to SQL conversions. In all cases, the conversion rate is the latter number (the smaller number) divided by the former, larger number.
Visitor to Lead conversion rate= Number of Leads / Number of Visitors
Lead to Customer conversion rate= Number of Customers / Number of Leads
MQL to SQL conversion rate= Number of SQLs / Number of MQLs
Month-On-Month SQL or MQL Growth Rate: Measures the rate of lead increase over time. Using SQLs in the example below, know the same formula applies to MQLs.
MoM SQL Growth Rate = (SQLst - SQLst-1) / SQLst-1 x 100
Annual Contract Value (ACV): Value of a customer’s contract over a 12-month period.
Total Contract Value (TCV): Value of a customer’s contract over its lifetime.
Average Revenue Per Account (ARPA): Measures how much revenue the average customer generates in a given period.
ARPA - MRR / Number of Active Customers
Average Selling Price (ASP): The average dollar amount a contract sells for. This is especially helpful if your company has a sliding scale or a range of subscription plans.
ASP = SUM(Contract Revenue) / Number of Contracts
Customer Acquisition Cost: Measures the amount of money it takes to acquire a customer. To calculate, add up all marketing and sales expenditures for a given period and divide by the number of new customers acquired during that period. If you have both free and paying customers, calculate their CACs separately and average them together for a more accurate metric.
CACt = Sales and Marketing Costt / New Customerst
CAC Payback Period: How long it will take a customer to make up for their acquisition cost. To calculate, divide your CAC by the per-customer MRR.
CAC Payback Period = CAC / MRR per customer
Gross Margin Adjusted Payback Period: Measures how long it will take a customer to make up for their acquisition cost, controlling for the ongoing cost of providing and maintaining service.
Gross Margin Adjusted Payback Period = CAC / (MRR per customer x Gross Margin)
Customer Lifetime Value (CLV): Measures the total revenue generated by a single customer from the time they first begin paying to the time they stop, which can include several separate contracts and licenses. CLV does not include referrals that result in contracts with other customers.
LTV = ARPA / Customer Churn Rate
LTV:CAC: LTV and CAC mean the most when formulated as a ratio, since an LTV is only good if it’s significantly greater than CAC. David Skok at For Entrepreneurs advocated an ideal LTV:CAC of at least 3:1.
Win Rate: Similar to conversion rates above, win rate measures the percent of total opportunities that became customers.
% Win Rate = Won Opportunitiest / Won Opportunitiest + Lost Opportunitiest
Sales Commision:ACV: If your sales team earns commission, it’s valuable to know what percent of the annual contract value those rates are. According to a 2015 Pacific Crest survey of private SaaS companies, the median sales commission to ACV ratio is about 9%.
Sales Efficiency (a.k.a. the SaaS “Magic Number”): Measures how much revenue you generated in a year compared to the cost of generating it. A sales efficiency ratio of 1 means you’ve recuperated your customer acquisition costs in one year and after that year can expect a profit.
Sales Efficiency = (Revenue x Gross Margin %) / Sales & Marketing Costs
Revenue Per Lead: The average contract value divided by the number of leads for a given period.
Revenue per Lead = ACVt / Number of Leadst
Lead Velocity Rate: Measures the percent in new leads each month.
LVR = (Qualified Leadst - Qualified Leadst-1) / Qualified Leadst-1 x 100
Customer Success Metrics
Daily Active Users (DAU): The number of active users on a given day
Monthly Active Users (MAU): The number of active users in a given month
Net Promoter Score (NPS): Measures the likelihood of your customers to recommend your product to others in an effort to gauge their overall satisfaction. To calculate NPS, you need specific data. Survey customers, asking them how likely, on a scale of 1-10, they are to recommend your product to a friend or colleague. Individuals who respond with an 8 or higher are labeled “promoters,” and those in the 1-6 range are “detractors.” (All others are “passives.”) A positive NPS is considered good, and an NPS over 50 is considered exceptional.
NPS = % Promoters - % Detractors
Customer Satisfaction Score (CSAT): Also a survey-based rating system, CSAT measures individuals’ satisfaction with the product. Customers are asked “How would you rate your overall satisfaction with the service you received?” and prompted to respond on a scale of 1 to 5. To calculate, find out how many points you received out of the possible total.
CSAT = SUM(Customer Responses) / (5 x Number of responses) x 100
Upsell and Cross-sell Rate: Measures what percentage of a period’s revenue was the direct result of either a cross-sell or an upsell.
Upsell Rate = ACV of Upsellst / Total ACVt
Viral Coefficient: Measures the growth of your customer base as a result of successful customer referrals. A viral coefficient of 1.3 means that every customer you acquire will land you another 1.3 customers.
Viral Coefficient = (# of Users x Average # of Referrals x Referral Conversion Rate) / Number of Users
Referral Revenue: The sum of all the revenue garnered as a direct result of successful referrals.
Referral Return on Investment: If your company offers customers referral incentives like discounts or gift cards, you can factor them into your referral revenue this way.
Referral ROI = (LTV - Referral Incentive) / Referral Incentive
Development and Support Metrics
Lead Time: The time taken from when an issue is logged or reported to when its resolution is delivered to the customer. Finding average lead time is made easier by classifying defects by either complexity or severity so that like projects can be compared to one another.
Cycle Time: The time taken from when a developer starts working on an issue to when the code is declared ready for delivery.
Code Churn: The percentage of a developer’s code that he/she later edits or deletes. The higher the churn rate, the less efficient the developer. Code churn is usually calculated with the help of an IDE.
Code Churn = Edited or Deleted Lines of Codedev / Total Lines of Codedev
Percentage Automated Test Coverage: The percentage of the source code that is executed when a set of automated quality assurance tests is run. Higher percentages indicate a greater likelihood of finding defects before the code is delivered.
Burndown Chart: Displays the amount of work left to do on a given project or release.
- X Axis: Span of time from the project’s start to it’s end, often represented in days
- Y Axis: Workload, usually represented in hours or days and calculated based on the number of developers, their efficiency rates, the number of tasks comprising a project, and their estimated cycle time.
- Ideal Tasks Line: A straight line running from 100% workload at project start to 0% workload at project end, the slope of which represents the ideal ratio of tasks-to-time over the course of the project.
- Actual Tasks Line: Line representing the actual progression of work over the project’s duration, with the line falling below the idea as well as rising above at times.
Defect Density: The ratio of defects to quantity of code for a given project. The size of the source code is measured in thousands of lines of code (KLoC).
Defect Density = Defect Count / Size of Release in KLoC
Crash Rate: Measures the frequency of application crashes over a given period.
Crash Rate = Number of Crashest / Number of Total Instancest
Cost Per Release: Measures the cost—including work hours, salary, licenses, equipment, etc.—per release.
Throughput: The number of tasks (or task groups) that can be completed in a given period of time. To count towards throughput, the task must have been completed in the given timeframe but doesn’t necessarily have to have been started then. For example, a task begun in January and completed in February would count towards February’s monthly throughput.
Defects Found: The number of defects found in a given release can be segmented by: the number found during development and/or QA, the number found after release by company employees, and the number found by clients or customers.
Support Ticket Velocity: Measures the percent increase in the number of new support tickets arriving each month (t).
Support Ticket Velocity= (# New Ticketst - # New Ticketst-1) / # New Ticketst-1 x 100
Active Days: The number of days in a given timeframe that a developer contributes code to a project. Can be a useful means of discovering workflow interruptions.
Average Ticket Response Time: The average time it takes for a support agent to respond to a new support ticket. Can also factor in response times to all other replies from the customer.
Average Resolution Time: The average time it takes for tickets to reach resolution as defined by your team.
Successful Resolution Rate: The rate at which agents are able to successfully resolve customer issues compared to the total number of requests in a given period.
Successful Resolution Rate = # Resolved Ticketst / # Opened Ticketst
Ticket Escalation Rate: The rate at which support tickets escalate tickets to a specified authority or supervisor.
Ticket Escalation Rate = # Escalated Ticketst / # Opened Ticketst
Channel Attribution: The percent of tickets arriving to you through various channels, which might include chat widgets, your knowledge base, your support email alias, or other departments in your company.
The Average Number of Replies Per Ticket: Measures how much back-and-forth typically occurs on a ticket. A reply usually constitutes a message, either from the support agent or from the client.