There are tons of metrics you could be tracking for your SaaS company. How do you decide where to start?
In this video, Shaun McAvinney, Sales Engineer at RJMetrics joins Tristan Handy, VP of Marketing at RJMetrics, to give you a crash course on SaaS analytics and teach you how to turn data into growth opportunities.
Tristan: Hey everyone, thanks so much for joining us on the 30 minute guide to SaaS Analytics. My name is Tristan Handy, and I'm the VP at marketing here at RJMetrics. RJMetrics is a SaaS business, and I get to get my hands dirty in our sales marketing and financial metrics all the time. I'm excited to talk to you about how you can do the same for your SaaS business. Before we jump into the material, I just have a couple housekeeping notes that I want to cover. First we'll have a Q&A session at the end of today's presentation, and if you have questions at any point please feel free to submit them via the chat window, and we'll make sure to get to them at the end. Second, we are definitely recording today's webinar, and we will make that available to everyone within 24 hours of the event. We'll shoot that to you in an email. So with all of the details out of the way I'd like to introduce my co-presenter. I'm here with my colleague today, Shaun McAvinney, Shaun do you want to introduce yourself?
Shaun: Yeah, thanks Tristan. I'm a sales engineer here at RJMetrics, and a big part of my job is helping perspective clients evaluate the technical challenges of data consolidation and management. I'll get to that a little bit more later on down the road.
Tristan: Thanks so much Shaun. I just want to do a quick overview of RJMetrics cloud BI for everyone attending. We're a hosted analytics platform for online businesses. We help SaaS, e-commerce and mobile companies use their data to make better decisions. We'll talk a little bit about cloud BI towards the end of this presentation, but first let's talk about SaaS metrics. We're going to focus on three things during today's webinar. Number one, the really important metrics that every SaaS business should be tracking. Two, the three approaches to solving the challenge of data management and consolidation. I know that sounds fascinating, but just wait until we get there. Three, we're going to show you some examples of how SaaS companies are using the RJMetrics to measure and manage their businesses. So let's get started.
One of the aspects of SaaS companies that makes them so appealing to investors is their predictability, and this predictability comes directly from a set of foundational SaaS metrics. Test businesses have a ton of data at their fingertips, and they can reveal a bunch of the inner workings of those businesses. The SaaS business is a complicated machine, with a ton of knobs and dials, and your SaaS metrics are what direct you to know what's going well and what you need to change. There are the meters on those that help you change the knobs and dials. The core data sets that every SaaS business needs to measure are financial and product usage. Measure your MRR, and MRR growth rate, your churn, and churn rate obsessively. Measure your active users, and what they're doing within your product, and make sure to analyze user activity within the context of cohorts.
The only way to make sure that you're comparing your apples to apples. But these core data sets aren't the only places to get valuable data about your business. Data from marketing, sales, customer success and engineering are all super important to understanding the functions of your SaaS machine and knowing how to keep it humming. Analyzing all of this data is an awesome challenge to have since it means you have a growing business, but it can also be a little overwhelming. With that in mind, one of the most common questions we hear from new clients is, "Where do we get started?" My answer is to start with the most important foundational metrics. The ones that will tell you the most about the health of your business. For every SaaS company out there that's going to be three key metrics. MRR, ARR to CAC, and LTV to CAC.
Let's start with MRR, monthly recurring revenue. This is subscription revenue for a SaaS business, and it's deceptively complicated to calculate. You can't just go and add up all of the money you took in in a given month. Subscription payments don't actually work that way. So here's an example. If a customer pays you $120 for an upfront payment for a 12 month subscription. That actually needs to be recognized as $10 increments over the following 12 months. A lot of people ignore this, but it's really important to do the math this way, or you're really setting yourself up for pain.
Next one ARR to CAC, annual recurring revenue minus customer acquisition costs, and you're going to see customer acquisition costs come up in both of these next metrics. It just represents how much money you spent in sales and marketing to acquire a customer. The ratio ARR to CAC is a near term one year metric. It shows you how quickly you can get paid on your customer acquisition spend. If you spend more than a year's worth of revenue to acquire a customer that means you're going to end up growing more slowly. You want to pay back as quickly as possible. Investors are looking for a one to one ratio here, that's considered healthy. In LTV to CAC, this is a longer term profitability metric for each one of your customers. LTV is a challenging SaaS metric because you need to subtract up service and support costs, but it's really, really important. An LTV to CAC ratio of three is considered excellent when you're talking to the investor community. These are also the investors that you are going to need to talk to... Sorry, the metrics that you need to talk to investors about. Make sure never leave them out of your fundraising deck, or they will immediately get questions.
I'm sure that a lot of you are familiar with these metrics, but what you need to keep in mind is that each of these metrics break down into their set of component inputs. For example, LTV to CAC ratio can be broken down into new MRR, expansion MRR, debooked MRR and churned MRR. This is really true for any of these high level ratios. If you compare these ratios to something like new leads today, or new customers this quarter, these high-level ratios just encapsulate a lot more of your business in them, so that when you monitor them they just tell you more at a glance. If the high-level ratio is changing you start by determining whether or not it's your LTV that's dropping, or if your marketing spend is increasing.
If it's because of LTV then you want to track down what's causing that problem. By decomposing the main metrics and tracking each input you end up with a really complicated view of your business. It allows you to quickly identify where problems are occurring. The same process can be applied to each of your core metrics, and it ends up with a very highly actionable model of your business. One of my favorite blog posts that I ever wrote was an infographic that shows the relationships between each one of the SaaS metrics, and we'll be sure to send that to you after the webinar. This was the information, I just said a lot of words. I just want to pause for a minute and get a sense for what part of your business you're looking to analyze.
Shaun: Great, so you guys should all be seeing a poll right now about which part of your business you're working is the hardest to improve. We have some things here around sales of marketing, sales, caring about your capital, your engineering team, customer success, or high-level performance metrics. If you guys could all just fill that in we'll share the results just a moment here. Like from my side when I'm talking with perspective customers I'm typically seeing... Because they're already investigating BI tool for their business, it usually is around sales and marketing. Marketing sales. They have these systems in place CRM, or marketing RMA systems, and they try to figure out the best way to tie those things together with their actual billing and get an idea of where they're at as a business in terms of marketing sales and customer success. That's kind of what I'm seeing, but I'm really interested to see what you guys have to show as well. Let's go over to the results. Yes, it does seem like marketing and sales are there with a pretty, actually pretty good split there with engineering as well, so that's cool. You guys should be able to see this after the event as well to reference back to it.
Tristan: It's clear that based on the answers that we're going to need to collect data from a bunch of different places. Let's talk for a second about how we do that. Here's an example, we just talked about LTV to CAC. In order to get LTV to CAC you actually need three different data sources all pulling data into that ratio. You're going to need a payment provider, we used Braintree as an example. A CRM, we used Salesforce as an example and an ad channel. Without all of that data you're not going to be able to actually calculate your LTV to CAC. Another quick example of this. In order to get your advertising ROI by campaign you need at least three data sources. You need a payment provider to actually get the transactions. You need a marketing automation platform, and you need an ad channel so that you can actually get your ads spent by campaign. Of course if you're also running ads on Facebook and Twitter, you're just going to have to add two more sources there.
One more example. If you want to get into some one off analysis like exploring the correlation between customer support requests and churn rate you'll need to add helpdesk data to the mix, and that's a really common analysis that we see. Just want to talk about our business for a second, because we're very much a SaaS business, and we have a lot of these same challenges. We use Salesforce instead of CRM. We use Partout for our marketing automation. Zendesk Resumator for hiring, we're using ReadyTalk for this webinar, and this is just a tiny little subset. Really you have to solve this challenge of getting this data from the systems that you use to run your business into one place so that you can actually report on the SaaS metrics. Shaun, I'd love it if you spent a minute talking about how you help people solve this challenge.
Shaun: Yeah sure. Essentially, there are three options. You can continue down the Excel path exporting CSVs and matching data together in Excel. It pretty much works fine for early stage companies. Eventually you're going to outgrow that option. Your engineers are going to get sick of interrupting their work building your product to run sequel queries. Your analysts are going to get tired of running the same reports over and over again, and that's when you really need a more sustainable solution. Then if you're kind of wondering what are some of the indicators to when it's time to upgrade to Excel. There's kind of a few reasons why a company will move off of Excel. Often I'll see companies that have just raised around of investment, or are at the fundraising process now.
Their investors are asking to get reports, or if they need to share reports with the board of directors, or new team members that they're hiring after a fundraiser. We actually also saw a big need when we were building our sales team. Right when we went from two to four sales members is when Salesforce reporting really wasn't cutting anymore for our small sales team, and also our larger team as well. We needed to pull all of that data together with our finance and product data sources to share across the team, and have more granular and accurate insights. At that point, that's when you're going to need a data pipeline, and you can build that yourself. Years ago that was the only option, or you can buy a data pipeline, and that's what RJMetrics does, that's what we do. Let me just clarify that that first option the spreadsheets option is kind of terrible. As soon as you're outside of the very early stage of your business you should be looking for a sustainable solution to the data problem.
Here's some considerations as you think about the build versus buy decision. On almost all fronts buying is going to get you further ahead. It's going to be cheaper over the long term. It won't drain your development resources to build and maintain. The risk of failure is much lower, and you'll have much higher analytical power because you're working with someone who spends all of their time optimizing data management processes. The one place where building yourself can be an advantage is if you're looking for a high degree of technical control.
For example if your business like Etsy or Uber, managing data is a core part of your business model, so it makes sense for them to have a massive engineering team dedicated for that challenge. But if your core business is building and selling great software products then you don't really need the technical control that Uber or Etsy need, and your engineering team should be 100% focused on building your product. The good analogy I like to bring up in the build versus buy comparison is, it's kind of like the email service provider. You're probably using Google apps or something similar, and you probably aren't running your own email servers and building a web mail client. That problem has already been solved, and that's where data pipeline product and BI tools are as well. Great, so here's kind of like the big webinar reveal. We're big fans of the buying solution.
Our team here at RJMetrics have spent the past five, six years building a powerful complete analytics platform that's designed to solve the exact challenges we've been talking about. Right now, I'm going to give you guys a sneak peek about how this all works. Let me go ahead and share my screen here. I didn't check the check box, there we go. You should all be able to see my screen now, and if not just say out loud that you can't see it. I'm just kidding, we can't hear you. So this is RJMetrics, and this is RJMetrics cloud BI. What we see here is founder and CEO dashboard. I've already gone ahead and taken the time to replicate the relevant data sources into a cloud BI data warehouse, make the relevant connections between those data sources, and build out this initial dashboard.
We're just going to kind of go through some of the things here that you would typically want to see if you were a founder, and CEO of a SaaS business. Frankly, what you probably are looking at now, but maybe just not automated. We have things like new and total MRR, we have total ARR, new customers by plan and then things like CAC, and customer ad loss, and percent of customer churn. All of that stuff is typical SaaS analytics, and then we have something down here that I kind of want to dig into a little bit more. This is the ARR to CAC ratio and something that investors would call the SaaS magic number or the magic ratio for SaaS businesses. I'm just going to show you how this works, and how you can replicate it yourself if you have all of the relevant data sources and how you could do it in RJMetrics.
You can see I'm pulling in the client's MRR. I'm typing in their ARR which is essentially taking the MRR, and kind of just multiplying it by 12 in dumbest sense. I'm bringing all of the ads spends, and the marketing sales spends. This our ad spend coming from those various outsources, and our marketing sales spend includes all of the budgeting for the marketing sales team. That's salaries, those are compensation packages. All of that stuff, that all goes into the cost that you spend to acquire a customer. We add those two things together and we get CAC. Then we can just compare those two numbers together.
We spent about $806,000 to acquire customers, and then that's all time. Our total ARR is about 5,5 million. We were making much more money in the first 12 months of the client than we're spending to acquire them. What you'll typically see is you'll trend us out over time and you'll bring this ratio down to specific averages per client, and in this example what we're seeing is, we have pretty high ARR to the cost to acquire a customer for this company, and for a growing business. If you're in a growth business you probably want this a little bit closer to one. Or maybe even a little less than one if you're seeing high growth. You can see towards the end here maybe we made a slight adjustment and we're getting a little bit closer to 2.5, in the 2.5 range. This is just a really quick and dirty example of how RJMetrics can work with SaaS data, and the types of things you can do in a platform that was specifically built for full stack analysis in mind. Okay, great let me just get back to the ReadyTalk and Tristan it's all you.
Tristan: Great, so I mentioned that we are heavy users of our own product, and I just wanted to share a couple of different ways that we use cloud BI internally to report on our business. The first one is the chart that our marketing team has been using for, I should say probably about three years now. At the beginning of every quarter, we agree with the sales team about how many leads we want to bring in, and set goals around that. Then over the course of the quarter we report on how many leads we actually generate, and measure that against our goal. We actually do that via a Google spreadsheet connector, and by hooking into Google Analytics. That's the first one.
We also care a ton about churn rate. I'm sure that's something that's very much on everyone on this call's minds. The process we go through here, we actually pull in all of the different cash points we have with our customers, and we combine it into a single retention health score. That retention health score prioritizes basically all of the activities that our account managers go through on a daily basis, and it's all data driven. Our product team also really carefully tracks engagement with new product features that we release. The report building process that you just saw Shaun walk through, that report builder was a new release, I don't know about 18 months or 2 years ago, and on this chart we're actually showing the adoption over time. The dark green is the new, and the light green is the old. We watched this change over the weeks and months after we released the report builders to make sure that it was getting the adoption that we wanted to see, and that's a rap. Thanks for joining us for this webinar, and we're going to open it up for questions. Shaun I would love to answer any questions you have about SaaS metrics in general or about SaaS metrics for your business.
Shaun: Excellent, thanks Tristan. I'm just pulling up some of the questions now. If you guys have any more questions feel free to add them in now. We'll keep rolling through some of them. The first question I see here is, "What is the metric you think is most important and how do you calculate it?" From the sales side, the things that we're kind of seeing from the sales engineering side, our big metrics is our close percentage for the trials that we start with on the sales engineer side, so these are like implementations and onboarding projects. What percent of those actually make it to become a paying client. We're really trying to optimize that ratio right now of the amount of work that we put into onboarding projects, and making sure that we're letting clients through the door into free trials that will be a good fit, and will be equip to make a decision on our product at the end of the trial. Tristan, what about you guys?
Tristan: Gosh, it's a really hard question to answer. Here's how I'm going to answer. If I am a SaaS business just getting my metrics in order, the things that I care about measuring first are just the straightforward, how many customers do I have, what's my MRR, what's my growth rate of MRR and what's my turn rate. It sounds like a really basic answer, but it's actually it's not that easy to measure something as straight forward as MRR would seem well. Doing it well is actually very important. I think it's really important for SaaS companies to build a really strong foundation when it comes to calculating their metrics because otherwise the people that rely on those metrics are not going to trust them and you're not going to end up building a data driven culture.
Shaun: Great, thanks Tristan. Another question here, "What's the difference between revenue churn and customer churn, and how should this effect the other metrics?" Actually, I'll let Tristan answer, I think he knows a lot about this.
Tristan: Yeah, revenue churn and customer churn, the big discrepancy there is that some customers are paying you more than others. It may look like it torpedoed your entire quarter if you have a very high value customer churn, but then looking at your customer churn number will show that the impact is much more muted. There's not a right answer as to which one of those is more important. The important thing is that you know both of them. They both tell you slightly different things.
Shaun: Great, thanks. One question about the build versus buy it says, "Are build and buy the only two options for data consolidation?" I would say no to that obviously. Often we'll see that people have a hybrid approach to this. They might half build something, so they've already pre-consolidate some data in a database somewhere, but there's other sources out there that they don't have yet, so they want a partner like RJMetrics to come in and help them get that other data in there. There's also kind of like just the build nor buy solution which is just the manual work of exporting CSV to these various systems, and matching them up together in CSVs. Which we kind of already talked about, it works pretty well in the short term, but once you get to a little bit more of a sizable business it's going to become a pain. Do you have something else Tristan or?
Tristan: I was just going to add that there's probably another solution, which is the open source solution. There are some tools out there, but I've played around with a couple of those before, and have been a little overwhelmed with just the level of complexity involved in configuring, and maintaining, and all of that, but it's another option.
Shaun: Good point. Another question we have here is, "What are the top mistakes you see SaaS businesses making when defining these metrics?" Something that I see a lot is that there's no cohesive, or consistent definition across teams, so you'll have sales and marketing kind of defining what a ready opportunity, or a worthwhile opportunity is. That's a big problem when you're talking about consistent goals across teams. Where marketing might be think that they're hiding it, and sales thinks that marketing is not hiding it, and that can cause a lot of issues. Luckily we don't have that problem here right Tristan?
Tristan: Yeah, I was going to say I don't know what you're talking about.
Shaun: But these are other companies.
Tristan: The thing that I'd say that I probably run into the most is people that don't think enough about how their different data sets are related together. The process of joining Salesforce data to your ad networks for example, or for joining any of those to your accounting data it's not magical. You have to define those relationships where you can point one thing to another, and those relationships are going to be the thing that... Essentially, they're like the scaffolding that all of your analytics get built on. Sometimes it means that you actually need to institute business processes to make sure that those relationships hold true. That's one of those areas where you really need executive buy in to make sure that you can actually run a data driven organization.
Shaun: Great. I want to say this is the last question. "Does RJMetrics have any plans for accommodating the needs for small startups?" Well that's a good question. It really depends on how small is small, and it really depends on the need of your business at a given time. Really when you're coming up against the wall with that buy versus build decision, and you're kind of spending too much time on Excel, or Google sheets for that matter... I see a lot of people just doing stuffs in Google sheets instead of Excel. When you're at that point where you don't have the time to spend to do those reports every week or every month even to send to your board of directors, or going through that round of fundraising, and you're going to make a decisions.
Most of the time when we start working with companies they are a little bit smaller, and they grow into us, and we grow into them. Our platform is designed to be business user friendly, so you don't need an analyst or a developer on board to help design your dashboards, or start kind of really getting into your data and making decisions. Most of our top power users on the SaaS side are marketers, salespeople, and CEOs, and co-founders. So small startups work really well with RJMetrics, and that's a great place to get your feet wet into data consolidation, and building out meaningful dashboards. You have anything there Tristan or no?
Tristan: No, I think you covered that.
Tristan: Everybody thanks so much for joining us, it's been a lot of fun, and if you have any more questions about SaaS metrics, I'd be happy to answer them on Twitter. I'm at @jthandy, and happy to take the conversation there. Anyway, thanks so much, and we'll be sending you the recording in your email soon. Have a good one.