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Integrations | Aug 24
Gasper Vidovic on October 24, 2016 • 29 minute read
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 services operations.
Why? In comparison to enterprise software firms of yesteryear who could rely on large, up front fees to get a quick payback, SaaS business model relies on small amounts of recurring revenues. The SaaS economic model is unique from almost any other. 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 marketing hard 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.
Sans big deals, SaaS sales is hard. 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. Further, you can’t afford to hire experienced salespeople to help you figure it out.
SaaS customer success is hard 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 services 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 playbook. Companies like HubSpot and Marketo invented and then shared their modern marketing and sales playbooks as well as their financials as public companies — leaving little doubt of it’s effectiveness. Zendesk certainly eats their own cooking, showing companies how to manage customer success. Of course, the trailblazer themselves, Salesforce, has shown us what it takes to keep growing and growing. Even relative new comers like Buffer are leading the way, transparently exposing their inner workings even during their adolescent SaaS phase. And of course, we have investors like David Skok and Jason Lemkin who have seen the playbook from inside many a SaaS board room and shared it with the world in detail. And that’s not to mention the services companies like inside sales consulting firm, The Bridge Group and inbound marketing agency Kuno Kreative that have made a name for themselves by helping SaaS companies scale and sharing their best practices in their reports and on their blog.
Together, these companies, investors and consultants have created and named a set of metrics that every SaaS employee would be wise to understand, and every SaaS executive should monitor closely.
Being a data-driven SaaS company ourselves who also helps companies monitor their key performance metrics, we’ve compiled a list of key metrics every SaaS company should track.
We’ve divided them up into three categories: marketing, sales and customer success.
Let’s start with marketing…
Monthly unique visitors is the number of unique individuals visiting your website each month. If a person is visiting the site multiple times, they will be counted as one unique visitor as long as they use the same device and browser for the visits or do not clear their cookies in between visits with the same device and browser. While this metric alone doesn’t provide many insights, it is a great reflection of the size of the audience and is a good measure of the impact of your overall marketing efforts.
By measuring the volume of unique visitors from each source, you can also measure the the effects of all your marketing campaigns and activities. And while growth in unique monthly visitors is a great gauge of the effectiveness of your top of the 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 and other such metrics. These metrics will tell you about the quality of your traffic, which is equally important as quality.
Use tools like Google Analytics or Adobe Analytics to measure unique visitors. Unfortunately, HubSpot Analytics does not measure unique visitors. They reason that it’s not a perfect measure since users can delete cookies and use multiple devices. Therefore, they track visits. Google Analytics and Adobe Analytics measure both and since Google Analytics is free, most SaaS companies start there.
Not every SaaS software offers a free trial or a self-service option. Many force you to talk to a salesperson before seeing the software. But, self service is perhaps the best way to lower cost of customer acquisition.
So, 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 costumer.
There are many ways to increase signups. We could write a whole blog post just on that. But, the most obvious ones are growing traffic by writing helpful, educational content for both prospective and existing users, and optimizing your website’s conversion rate.
Just like visits, you can use analytics tools like Google, Adobe and HubSpot to measure this metric. But, with more than 20,000 companies monitoring more than 500,000 apps and projects, many SaaS companies turn to Mixpanel to measure user acquisition and track users once they sign up.
Tomasz Tunguz, 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 with 3 data sources for free, allows up to 3 users and allows access to fewer features than our paid products. We’ve defined PQL criteria for our business based on a user’s interaction (features used, time spent and frequency of usage) with the product. Our developers then run experiments to increase our PQL volume.
Like many other SaaS companies, we determine whether a user meets our PQL thresholds inside of Intercom.
Once you’ve documented your PQL (or MQL) definition, you need to calculate how many you need each month.
Knowing your qualified lead to close ratio (See Sales section below), work backwards from your revenue target to calculate the volume of leads needed. (Here’s a helpful revenue to lead calculator if you want some help running that math.)
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. In fact, “there’s no reason leads can’t grow every single month like clockwork” says Jason Lemkin, the creator of the LVR ratio.
Why should you obsess over LVR? Since it is just a matter of time before some percentage of your qualified leads close to a customer, LVR is a great indicator of future sales attainment.
To calculate LVR, use the following formula:
As an 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 quality of your leads stayed the same, use your average sales cycle to forecast new sales revenue in future months.
Organic traffic comes from your organic rankings in the search engines, whereas paid traffic comes from Pay-Per-Click (PPC) ads, sponsored links, or purchased ads. But which one drives better results?
Organic search results are 8.5x more likely to be clicked on than paid search results, as searchers consider organic results more trustworthy. Once your content is ranking organically, every click is free too. How can you beat that?
Well, it takes time and effort to grow organic traffic and even then it converts at a bit lower rate than paid clicks.
So, where you invest depends on how quickly you need results and how much money you have. If you need results now and have budget, paid search is the right place to focus. If you don’t need immediate results, focus as much of your time and budget towards content creation to steadily grow organic traffic over time.
Of course, if you intend to be in business in the future, it’s always smart to invest in growing your organic traffic. And companies that have been producing higher volumes of content for longer time periods have a distinct advantage. So, the longer you wait, the harder it’ll be.
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. You can also gain insight into which keywords or ad campaigns are driving better results and focus more effort and budget on the ones working and less on the ones that aren’t.
To track the performance of your paid and organic channels, you’ll need a few tools to get a full picture. You’ll need an analytics package like HubSpot or Google Analytics to measure your traffic, lead and customer acquisition volume by these sources. Unless you’re selling online, a package like HubSpot will help you track your clicks all the way to revenue. They call it closed loop marketing analytics.
You should also connect with your ad platform to see paid search performance more granularly.
Regarding tracking your organic rankings more closely, Google has made it harder in the last few years to optimize organic search. While there are tools that will report your rank or placement in search engine result pages (SERPs) for each of your target keywords, it’s technically against Google’s terms of service. Google has also encrypted many SERPs so that analytics packages (including their own) no longer report traffic by keyword for many searches. Instead, focus on optimizing volume of organic search in the beginning. Explore using services like SEMRush, Ahrefs, Moz and Google’s own Search Console once you’ve made some traction.
Word of mouth marketing can not be beat. When your customers help you acquire new customers, growth can be exponential.
Perfected by consumer internet companies as early as hotmail.com (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:
Invites = Number of invitations the average user sends.
Conversion percent = The percentage of invitees that convert to customers
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 and faster your company will grow.
To model your viral growth rate, download this calculator from David Skok’s blog.
At Databox, a few months ago, we pivoted from an outbound, enterprise sales model to an inbound, freemium-driven model selling to a wider range of company sizes. Our development team has had a huge impact already on our revenue generation and we plan to improve it even more by tracking and improving the metrics below.
Depending on how you’ve defined your marketing and sales process, you might have different definitions for different types of leads. You might have subscribers who simply subscribed to your blog, leads that simply filled out a form on your website to download an ebook, marketing qualified leads who fit a strict definition of “fit” and “interest” based on how they’ve interacted with your site and what you know about the contact and their company, or product qualified leads who are using parts of your free product. You might even use all of these definitions.
At Databox, we pay closest attention to the number of PQLs who convert to customers, but we also measure the overall number of new users who convert to customer 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 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 HubSpot. We’re pulling PQL volume from Intercom.io and pulling sales data from Stripe, then doing the calculation inside Databox.
Average Revenue per Account (ARPA), also known as Average Revenue per User or per 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 this is to divide the total MRR you have at the end of a 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 compared to 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 need to pull data from Paypal or Quickbooks if you are using those systems too. 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,” as 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 cost 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, where you don’t need to grow your headcount as fast as customer acquisition scales, 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: 1) CAC and 2) the ability to monetize those customers (or LTV; which stands for Lifetime Value of a Customer). In an unbalanced business model CAC exceeds LTV, where 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 No Touch self-service business models have CAC between $0 and $200. Light & High touch inside sales inflate CAC to between $300 and $8,000, and 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 by making your product easier to understand 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. The 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 also to multiply the total number of paying customers by the average revenue per user (ARPU).
For example, say you have 5 customers. Three of them are paying $100/month, one is paying $200/month and one is paying $960/year. First, the yearly billing period needs to be normalized into a monthly amount (dividing by 12, which is in this case $80/month). 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 business. 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 calclate add-on MRR and factor that into NET MRR too. 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). This means enough of your existing customers are upgrading, countering the revenue lost from the customers who are cancelling. In this scenario, the average new customer you acquire will grow.
Money companies focus more on measuring Annual Recurring Revenue (ARR). As you’d imagine, ARR is the annual value of recurring revenue and means 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.
One of the crucial parts of managing your customer relationships is keeping track of how your services team is performing. Customer Success is the foundation for every SaaS company, and excellent customer service is one of the biggest driving forces behind it.
The services team is also a linchpin between many of your teams. They work with sales to drive upsells, cross sells and renewals. They work with marketing to do product marketing. They help sales ensure that expectations are set properly with customers before they buy and they work with the product team to identify ways to improve or enhance the product. Because they are such a critical team, it’s important to ensure they’re focused on the customer, first and foremost. The following metrics will ensure that focus.
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, to respond 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 like with tags, you’ll be able to quickly determine whether’s there’s a problem, usability issues or just active customers requesting new capabilities, when ticket volume inevitably spikes.
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. Investigate when you see response time lag.
While it’s important to respond to users quickly, it’s critical to resolve issues quickly. Average resolution time is the average time it takes your Support team to completely resolve or “close” a ticket. It 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 tickets too.
Calculate 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 0 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: Detractors (a 0 to 6 score), Passives (a 7 or 8 score) and Promoters (respondents giving a 9 or 10 score). Calculating NPS isn’t as simple as averaging the ratings. So, use software to do it.
Probably obviously, 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” vs “bad” usage. In other words, every company must define the usage that defines an active user. Is it certain features? Is it certain features used at a certain frequency? Must active users use a combination of features to be considered active? Unfortunately, there’s no standard answers; every company is different.
As a best practice, you should define a user “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 different for your mobile and webapp. At Databox, our “Active users” metric is split into “Active Webapp Users” and “Active Mobile users”, as we count those separately as usage patterns are very different in each app. Users tend to configure datacards and datawalls inside our web app, while they tend to view them more frequently in our mobile app. For example, An Active Webapp User has done something of the following: created, edited, deleted, duplicated, reordered, or renamed a Datacard or Datawall, invited a user, made any changes to Datacards or Datawalls in the Designer, connected a new data source, previewed a Datacard or Datawall. An Active Mobile User has not only opened the app, but also viewed alerts, favorites, notifications, messages or scrolled/swiped on any of the Datacards.
Active users can be measured as Daily active users (DAU), Weekly active users (WAU) or Monthly active users (MAU) as the amount of unique users who are “active” within a given amount of time. Improvement in these numbers is a good signal that you’re 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. Holding nothing back, Mixpanel calls them “Bullshit 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”, though 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 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 use. 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 Customer 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 number 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 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. “Churn is a measure of how efficient a company is at retaining customer revenue” (as explained in The Maximum Viable Churn Rate For A Startup by Tomasz Tunguz).
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? Some sectors have significantly higher rates of customer churn than others. For SaaS companies, a 3% or lower churn is considered to be good. 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 the customer churn rate and identify the reasons for it. Get in touch with your previous users and try to get as much info as you can why they are leaving. Get ahead of churn by defining the usage levels that correlate to retention and do your best to drive customers to that level of usage. Always seek new ways to deliver more value to your customers as a way to improve retention.
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 this 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 purchasing add-ons, upgrading to higher plans and increasing their Lifetime Value to such an extent that it makes up for any revenue lost to other customers churning. If a company loses 5% of customers to churn each month, but the 95% of remaining customers are so successful (i.e. customer retention is only 95%) with the product that they purchase additional services, increasing their spend 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 is one of the most important metrics for understanding your customers. For some, it’s the only one that matters. It helps you make business decisions about Sales, Marketing, Product Development, and Customer Support.
First, we need to calculate out Customer Lifetime which is:
If you have a monthly Churn rate, then the Lifetime will be for the same time period (in months). Let’s take two examples: If your monthly customer Churn is 5%, then your Customer Lifetime is 20 months (1 / 0.05). If you take the 25% annual customer churn rate, your Customer Lifetime is 4 years (1 / 0.25).
Once we have the Customer Lifetime, we can calculate our Customer LTV. The simplest formula is:
If your ARPA is $100 and the Customer Lifetime is 20 months, we get the LTV which 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.
The 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.
If these KPIs are the important metrics for your business, you may want to be able to check them anytime, anywhere – in the middle of the night, if needed. And you also want to know if something changes, when it changes and why it changes.
However, that’s hard for most SaaS businesses given the number of software systems most use. 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. Obviously for us, 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 (accessible on your mobile phone, desktop, Apple Watch, office screen, or even inside Slack). There are hundreds of datasources that work out of the box, you can connect to any SQL database like AWS Redshift, MySQL… or bring your data from spreadsheets or custom built software behind your firewalled applications.
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 which let you be the first to know when something goes wrong or when something goes wonderfully!
Sign up for a free account today and let us know how we can help you grow your SaaS business as we grow ours.
Want to help us write the book on the next generation of SaaS metrics? Tell us what metrics you are measuring and how you’re measuring them. We’ll update this post as we hear from you.
Integrations | Aug 24