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Analytics | Nov 19
Gasper Vidovic on July 11, 2019 (last modified on August 20, 2019) • 25 minute read
Most SaaS companies struggle to achieve predictable revenue growth, and even public SaaS companies struggle to achieve profitability.
To make a SaaS company successful, you can’t just change your software delivery model to the web and expect it all to work. You must make thoughtful, data-driven decisions when it comes to your marketing, sales, and customer success operations.
To make data-driven decisions, you have to track the right SaaS metrics and KPIs.
The SaaS economic model is unique.
In comparison to the enterprise software firms of yesteryear that could rely on large, upfront fees to get a quick payback, the SaaS business model relies on small amounts of recurring revenues.
And unlike a services businesses where you can pay for your new equipment after the first few jobs—or a consulting business with no overhead that can close big upfront contracts from the get-go—revenue in a SaaS business is built one small sale at a time, paid in small increments.
This makes SaaS difficult in terms of marketing, sales, and customer success.
SaaS marketing is difficult because you need to find ways to find and attract a high volume of quality leads, and then find ways to increase lead volume for years to come—all on a small budget.
SaaS sales is also difficult. You need to find ways to make your salespeople more efficient so they can close more deals more quickly. But every investment you make in productivity increases your payback period even further. And often, you can’t afford to hire experienced salespeople to help you figure it out.
SaaS customer success is difficult because it’s just another upfront expense you must justify within an already limited budget. But it’s critical because you might not get any payback on your marketing, sales, or customer service if customers cancel before break-even occurs.
But even though scaling a SaaS company is hard, it’s not impossible. Fortunately for all of us, a few successful SaaS pioneers have shared parts of their playbooks:
Together, these companies, investors, and consultants have created and named a set of SaaS metrics and KPIs that every SaaS employee would be wise to understand—and every SaaS executive should monitor closely.
Since we’re a data-driven SaaS company—and one that also helps other companies monitor their most important performance metrics—we’ve compiled a list of the 18 SaaS metrics and KPIs all companies should track.
Monthly unique visitors is a count of the number of unique individuals who visited your website in a given month. If someone visits your site multiple times, he/she will only be counted as one unique visitor (assuming the same device and browser is used for each visit and the visitor doesn’t clear his/her cookies between visits).
While this metric alone doesn’t provide many insights, it’s a great reflection of the size of your audience, and it’s a good measure of the impact of your overall marketing efforts. Plus, by measuring the volume of unique visitors from each source, you can also measure the effects of marketing on different channels.
And while growth in unique monthly visitors is a great gauge of the effectiveness of your top-of-funnel marketing, don’t forget to look at engagement metrics like average time on site, average pages visited, repeat visits, number of comments, downloaded content, email subscriptions, etc.
These metrics will tell you about the quality of your traffic, which is just as important as quantity.
You can also grab the free Google Analytics Acquisition Snapshot dashboard below to quickly view how your marketing campaigns are performing across all of your target channels.
Not every SaaS product offers a free trial or a self-service option. Many force you to talk to a salesperson before trialing the software. But self-service is perhaps the best way to lower the cost of customer acquisition.
For self-service SaaS companies, signups is probably the most important metric.
Whether you offer a free trial or a freemium plan, marketing’s goal should be to drive signups.
In an ideal world, the user can learn the software on his/her own, begin using it regularly, and find enough value to convert to a paying customer.
There are many ways to increase signups, including writing helpful, educational content for both prospective and existing users and optimizing your website’s conversion rate.
Use tools like Google Analytics, Adobe Analytics, HubSpot, or Mixpanel to measure signups. HubSpot users can also grab the free HubSpot Sources Report dashboard below to track how your marketing efforts on every channel are contributing to each stage of your funnel.
Tomasz Tunguz, a venture capitalist at Redpoint Ventures, defines PQLs as “potential customers who have used a product and reached pre-defined triggers that signify a strong likelihood to become a paying customer.”
For freemium business models, a PQL is the new MQL (or marketing-qualified lead). It helps SaaS businesses pre-qualify potential customers based on their product usage.
At Databox, PQLs are one of the most important metrics we track. Our free product allows users to connect three data sources for free, add up to three users, and access fewer features than our paid products.
We’ve defined PQL criteria for our business based on a user’s interactions with the product: number of features used, time spent in the product, and frequency of usage. Our developers then run experiments to increase our PQL volume.
We determine whether a user meets our PQL thresholds using Intercom.
Once you’ve documented your PQL (or MQL) definition, you need to calculate how many new PQLs you need each month.
Knowing your qualified-lead-to-customer conversion ratio, work backward from your revenue target to calculate the volume of leads needed.
All would be great in the world if you could snap your fingers and start generating the lead volume you need to exceed your revenue target. But since that’s not realistic, plan to increase your lead volume every month so that you’re comfortably hitting your annual revenue target by the end of the year.
Why should you obsess over LVR? Since it’s just a matter of time before some percentage of your qualified leads convert, LVR is a great indicator of future sales attainment.
To calculate LVR, use the following formula:
For example, imagine you created 1,100 qualified leads this month and 1,000 qualified leads last month. You are growing LVR at 10% month-over-month.
Assuming the quality of your leads stays the same, use your average sales cycle to forecast new sales revenue in future months.
Organic traffic metrics include visitors who arrive from a non-paid (organic) listing in the search results. Paid traffic metrics, on the other hand, include visitors who arrived from paid search results like pay-per-click (PPC) ads.
Where you invest your marketing dollars should depend on how quickly you need results and how much money you have:
Of course, if you intend to be in business in the future, it’s always smart to invest in growing your organic traffic regardless of where you focus your efforts up front.
If you can do both, do both. Don’t waste money, though. Make sure you’re converting your traffic (especially your paid traffic) into revenue.
No matter where you invest, it’s essential to measure the volume of traffic, leads, and customers you are generating from your organic and paid traffic channels.
To track the performance of your paid and organic channels, you’ll need a few tools to get a full picture:
If you don’t want to have to pull metrics from multiple tools and enter them all into a spreadsheet to get a complete picture of the ROI of your organic and paid search efforts, Databox can help.
Connect multiple data sources from 70+ integrated tools—including Google Ads, Bing Ads, Google Analytics, Google Search Console, Ahrefs, SEMrush, and more—to get a centralized view of all of your most important KPIs. Grab the free Google Ads dashboard template below to get started.
Word-of-mouth marketing cannot be beaten. When your existing customers help you acquire new customers, growth can be exponential.
Perfected by consumer internet companies as early as Hotmail (before it was bought by Microsoft), Airbnb, and Gmail—as well as newer internet software darlings like Dropbox, Slack, and every successful social network ever—virality is every SaaS startup’s dream.
To measure virality, calculate your viral coefficient. The formula is simple:
As an example, a virality coefficient of 1.5 means that every signup brings 0.5 additional signups, so for 100 signups, you actually get 150.
The greater your viral coefficient, the faster your company will grow.
To model your viral growth rate, download this calculator from David Skok’s blog.
Depending on how you’ve defined your marketing and sales process, you might have different definitions for different types of leads. You might have:
You might even use all of these definitions.
At Databox, we pay close attention to the number of PQLs who convert to customers, but we also measure the overall number of new users who convert to customers, too. As an example, we measure our PQL to customer conversion rate as the percentage of PQLs that end up converting to paid customers.
Here’s our simple formula:
Whatever lead-to-customer conversion rate you measure, make sure you define different lead types and consistently calculate your conversion rates.
Your conversion rate is a benchmark for how good of a job you are doing at turning leads into customers. By increasing your conversion rate to customer, you’re directly increasing your revenue.
To measure your conversion rates, many companies use a marketing automation platform like HubSpot or Marketo and/or a CRM like Salesforce or Pipedrive. We’re pulling PQL volume from Intercom and sales data from Stripe, then doing the calculation inside of Databox.
If you use HubSpot’s Marketing and CRM products, grab the free Pipeline Performance dashboard below to get a quick view of your visitor-to-contact, contact-to-MQL, contact-to-SQL, and contact-to-customer rates.
Average revenue per account (ARPA), also known as average revenue per user/unit (ARPU), is a measure of the revenue generated per account (usually per month as most subscription businesses operate monthly). But you can always calculate it yearly or quarterly according to your plans and billing options.
A simple way to calculate ARPA is to calculate the total MRR you have at the end of the month and divide it by the number of active customers at that time, like so:
A good practice is measuring ARPA for new and existing customers separately to have a sense of how your ARPA is evolving—or if new customers behave differently than existing ones. Some companies also calculate this as average sales price (ASP) to separate the impact of upselling from the price at the initial sale.
To track your ARPA, you need to rely on your billing or accounting system. Many SaaS companies use Stripe to manage billing, but you may also need to pull data from PayPal or QuickBooks if you are using those systems.
If you’re using multiple payment methods and have not consolidated your data into a centralized accounting system, you might need to pull from multiple systems and add the numbers together.
David Skok calls CAC the startup killer because a very large number of startups have failed (even those that solved the product/market fit problem) because they did not find a way to acquire customers at a low enough cost.
Understanding how much it costs to acquire new customers—and identifying the most profitable marketing and sales channels—is the key to profitably scaling a SaaS business.
To calculate it, divide your total sales and marketing costs by the number of deals closed within a given period. If you’re following an inside, channel, or field sales model, make sure to include salaries. If your product sells itself without salespeople, you can calculate your costs without the headcount costs.
Follow this formula:
A viable business model for a SaaS company comes down to balancing two variables:
In an unbalanced business model, CAC exceeds LTV, whereas in a balanced model, CAC is significantly smaller than LTV.
Skok gives us two rough rules of thumb regarding CAC:
Per Skok, CAC changes drastically based on the complexity of your selling model. Freemium or self-service business models have CACs between $0 and $200. Light- and high-touch inside sales inflate CACs to between $300 and $8,000. With a field sales team, your CAC can go from $20,000 to even up to $200,000.
To reduce CAC, try A/B testing to improve conversion rates, minimize the level of touch required to complete a sale, or make your product easier to use more quickly via an improved trial process.
To calculate CAC, you’ll need to aggregate all of your costs from something like QuickBooks or by manually tracking in a spreadsheet. Also, you’ll need to track the number of new paid users you acquire during each period.
Recurring revenue is the lifeblood of any SaaS business. Your MRR is a single and consistent number to track no matter how many pricing plans and billing cycles you have.
But how do you calculate MRR?
The easiest way is to summarize all the revenue you receive from your paying customers per month. Another way is to multiply the total number of paying customers by the average revenue per user (ARPU).
For example, say you have five customers. Three of them are paying $100/month, one is paying $200/month and one is paying $960/year. MRR in this example is $580: (3 x $100) + $200 + $80. When divided by the number of customers (5), you get the ARPU ($116 in this case).
While that calculation sounds simple (and it is), SaaS companies need to calculate multiple MRR numbers, depending on the complexity of their businesses. For example, all SaaS companies should measure new MRR and churned MRR to calculate net MRR.
If you’ve built a pricing and packaging strategy that enables you to generate additional revenue from existing customers, you should also calculate add-on MRR and factor that into net MRR.
Here’s a list of different MRR numbers you should calculate:
If you measure a higher churn MRR than your new MRR, you are likely losing as many customers as you are gaining each month. That is a recipe for going out of business fast.
When your add-on MRR is higher than your churn MRR, that means you’ve figured out how to have positive retention (or negative churn). Enough of your existing customers are upgrading, countering the revenue lost from the customers who are canceling. In this scenario, the average new customer you acquire will grow your revenue.
Money companies focus more on measuring annual recurring revenue (ARR). As you’d imagine, ARR is the annual value of recurring revenue and is the monthly recurring rate multiplied by 12.
Once you achieve positive retention rates, ARR gives you an estimation of how much revenue you’ll generate in a year, not including customers you’ll likely book during the remainder of the year.
Stripe users can simplify the process of tracking MRR by grabbing the free Stripe MRR and Churn dashboard below.
It’s inevitable to receive a certain number of complaints, questions, and suggestions from your customers. The number of support tickets created is a measure of how many customers are requesting help.
Better than just tracking an absolute number of support tickets, you should keep an eye on the trend of average numbers of daily, weekly, and monthly tickets in case you see an escalation of tickets.
A good practice is to tag tickets by types (bugs, feature requests, questions, suggestions and other). If you stay organized with tags, you’ll be able to quickly determine whether there’s a problem, usability issues, or just active customers requesting new capabilities when ticket volumes inevitably spike.
Average first response time is the average amount of time it takes for customer support to respond to a case after it’s submitted by a requester (customer).
Because people don’t like waiting to have their problems solved, average first reply time correlates with customer satisfaction. In other words, the lower your average first response time, the more satisfied and engaged your customers will be.
Plan your staffing carefully to scale with your customer count. When SaaS companies are growing quickly, it’s a mistake to underestimate future support ticket volume. When planning your budget, hire and train support people before you need them.
To track average response time, use your help desk software. Help Scout users can also grab the free Help Scout for Customer Support dashboard below to get a quick, consolidated view of first response times, resolution times, and percent of tickets solved on the first reply. Investigate when you see response times lag.
While it’s important to respond to users quickly, it’s critical to resolve issues quickly. Average resolution time is the average amount of time it takes your support team to completely resolve or close a ticket.
Average resolution time is a stronger corollary to customer satisfaction and retention than first response time. After all, it doesn’t matter how quickly you respond to a ticket if you don’t resolve the ticket, too.
Calculate your average resolution time with the following formula (if your software doesn’t calculate it for you):
You can measure customer satisfaction using customer surveys, and in particular, the Net Promoter Score. NPS is the most popular metric to measure customer satisfaction and loyalty.
But don’t just use NPS to measure your customers’ happiness with your products. Customer ratings are also necessary to evaluate your support team’s effectiveness. Also, it’s smart to measure your NPS after product updates to see which changes triggered a positive or negative response.
The NPS tells us the likelihood of a person to recommend a company or its product to someone else. NPS typically uses the 0-10 scale, where zero means they won’t ever recommend the product and 10 means they definitely would. The higher your NPS the better, as it indicates satisfied users who will likely stay with you over time.
Three categories of people can be distinguished:
Active users means the number of people that are actively using your product. This metric is a benchmark to determine the health of a SaaS company’s customer base. More usage by more users is a strong sign of a healthy SaaS app.
But since usage patterns and frequency is appropriately different for different companies, there’s no universal measure of “good” versus “bad” usage. In other words, every company must define the usage that defines an active user:
Unfortunately, there are no standard answers; every company is different.
As a best practice, you should define a user as active when they do something from which they have derived undeniable value. For most apps, that means they should do something beyond just logging in before counting them as active.
Depending on your business, you might want to define usage differently for your mobile and web apps. At Databox, our active users metric is split into “active web app users” and “active mobile users.” We count those separately as usage patterns are very different in each app.
Active users can be measured as daily active users (DAU), weekly active users (WAU) or monthly active users (MAU).
Improvement in these numbers is a good signal that your business is moving in the right direction. These numbers can help you identify the impact of key initiatives, including new marketing channels, sales approaches, and product enhancements.
But some call DAU and MAU vanity metrics. They argue that a better indicator of success is retention, which tells you how sticky your product is and how many users return to it after their first experience.
One way to measure stickiness is to divide your daily active users by your monthly active users, as in the formula below:
A DAU/MAU ratio of 50% means your customers return 15 out of 30 days in a month. If it makes sense that using your software should be a daily habit, this is an excellent metric to calculate.
It’s quite obvious that you should build a product that people love and want to continue using. You won’t renew deals if your customers don’t use your product.
Customer retention rate can be defined as a metric that indicates the proportion of customers that have continued to use your product for a while. The opposite of retention is churn—also known as customer attrition.
To calculate customer retention rate at the close of a month, look at repeat orders from repeat customers in the past month and compare these orders to numbers from two months before. Do not count new customers you acquired in the previous month.
For example, if the number of customers subscribed at the beginning of last month is 200, and the customers who continued subscribing at the end of last month is 170, the customer retention rate is 170 ÷ 200, or 85%.
Churn rate is the proportion of customers or subscribers who leave during a given time period. It is often an indicator for customer dissatisfaction, cheaper and/or better offers from the competition, aggressive and successful marketing from the competition, or reasons outside of your control like business failure or strategy shifts.
Some customer churn is to be expected. But the greater the churn, the more capital is required for the business just to maintain its revenue. Churn can quickly sink a SaaS company.
What is a good churn rate? For SaaS companies, a 5-7% annual churn is considered acceptable. SaaS companies that sell to smaller businesses should expect higher churn, while SaaS companies that sell to enterprises should strive for very low customer churn.
Acquiring new customers is usually way more expensive than retaining existing ones. So, keep a sharp eye on customer churn rate and identify the reasons for it:
Your goal should eventually be to achieve negative revenue churn. To accomplish this, prevent customers from unsubscribing while also finding ways to increase revenue from your existing customers. The increased revenue from existing customers should offset the revenue lost when others cancel.
Negative churn happens when the expansion revenue from existing customers is higher than lost revenue from churning customers. This is the case when your existing customers expand their use of your product by:
If a company loses 5% of customers to churn each month, but the 95% of remaining customers are so successful with the product that they purchase additional services and increase their spends by 5%, revenue retention would be 110% compared to the previous month.
To calculate churn, you must use data from your accounting and/or CRM system. Ideally, these two systems are integrated so that you can pull data from either place.
Customer lifetime value (LTV) is one of the most important metrics for understanding your customers. It helps you make business decisions about sales, marketing, product development, and customer support.
To calculate customer LTV, you first have to calculate your customer lifetime:
For example, if your monthly customer churn is 5%, then your customer lifetime is 20 months (1 / 0.05). If your annual customer churn is 25% your customer lifetime is four years (1 / 0.25).
Once you know your customer lifetime, you can calculate your customer LTV. The simplest formula is:
If your ARPA is $100 and the customer lifetime is 20 months, your LTV is $2000.
As you can see, churn has a direct impact on LTV. If you can halve your churn rate, it will double your LTV.
LTV is also important in discovering if a business model for a SaaS company is viable or not. In an out-of-balance business model, CAC exceeds LTV, whereas in a balanced model, CAC is significantly less than LTV.
Steering one’s business without measurable success indicators is like steering a car in the dark without dashboard lights: you might be going the right direction, but you have no idea how fast or when you’ll get there.
If these SaaS KPIs are the most important metrics for your business, you may want to be able to check them anytime, anywhere. And you’ll also want to know if something changes, when it changes, and why it changes.
However, that’s hard for most SaaS businesses. At Databox, we sometimes feel like we spend as much time building software to run our SaaS business as we do working on the SaaS product we sell. But we don’t have to do that for reporting, and we don’t think you should either.
With Databox, you can pull all your dispersed data quickly into one place and have all your data available wherever you want it. There are 70+ one-click integrations, you can connect to SQL databases like AWS Redshift, and MySQL, or you can bring your data from spreadsheets.
You can even get a daily or weekly scorecard with your most important KPIs, and for more dynamic metrics, you can define a set of alerts that let know when something goes wrong—or when something goes wonderfully!
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