SaaS Perspectives is a blog series where we interview leading thinkers in B2B SaaS to get their view on the state of business software and how they see the category evolving. This week, Ray Rike (Benchmarkit & SaaS Barometer), sat down with us to answer some of our questions. We will be continuing to publish these interviews over the coming months.
Ray Rike is the Founder and CEO of Benchmarkit, a boutique benchmarking research company, and is also the creator of SaaS Metrics Palooza, SaaS Metrics Executive Summits, the SaaS Barometer Newsletter and two leading podcasts - the SaaS Talk with the Metrics Brothers podcast and the Metrics that Measure Up podcast. He is also the founder of the SaaS Metrics Standards Board. Enabling SaaS and Cloud executives to make metrics-informed and benchmark-validated decisions is his passion. Ray is a first-class analyst of software businesses and some of the topics we touched on in this edition of SaaS Perspectives included:
- The importance of establishing a SaaS Performance Metrics framework.
- The five pillars of enterprise value creation.
- The Magic Number and the challenge of multivariate metrics.
- The three tiers of performance metrics: board-level, department-level, and individual.
Why is it important for a company to establish a SaaS Performance Metrics framework? How early in the life cycle of a SaaS business should they invest in this approach?
Once you establish product-market fit, you should start to track a set of performance metrics aligned with your overall strategy. If your strategy is “we want to maximize penetration in this particular target market” you will have a different approach than if you are trying to grow your business on a self-funded basis. A SaaS Performance Metrics framework helps prioritize which metrics are most aligned to your business strategy, and provides insights into how your performance metrics are impacting your company’s value. I’ve identified five categories of metrics which I think of as the five pillars of enterprise value creation: capital efficiency, operational efficiency, customer acquisition efficacy, customer expansion efficacy, and customer retention efficacy.
The first big category of SaaS performance metrics focus on capital efficiency. For every dollar being invested into your business, what is the anticipated return? Capital efficiency is especially important if your business has raised money from VC or PE investors - they want to look at past capital efficiency and get a sense of what you will do with the money they put into your business. The second key category of metrics focus on operational efficiency. The metrics you are going to rely on to measure operational efficiency will depend on your priorities. Maybe your priority is gross margin and you have decided to aim for an 80% target, or maybe you don’t want to spend more than 35% of revenue on sales and marketing. Priorities will vary by company but as an operator, you need to set goals that tie into the operating efficiency metrics that matter to you.
Beyond capital and operational efficiency, we look towards the other three pillars of enterprise value creation: customer acquisition efficacy, customer expansion efficacy, and customer retention efficacy. Let’s say you are trying to add $5MM ARR this year. You need to know for every dollar invested in acquiring new customers, how much ARR will that generate. That’s why I like the CAC ratio so much - the CAC ratio tells you for every dollar of new ARR you want to add this year, you need to invest $X dollars in sales and marketing. And by understanding retention efficacy, you can forecast that for every dollar of ARR we add this year, X% of those customers will stick around next year. For companies taking a multi-product platform approach, net revenue retention will be the north star and you need to be able to measure and predict that for every new logo you add, you can reliably upsell them by X% in year two.
Another interesting metric is burn multiple, which everyone has been very focused on for the last two years and there has been a big emphasis on getting to that magical 1:1 ratio where net new ARR exceeds cash burn. But you might instead have a goal of growing to $100MM as quickly as possible. In that scenario, you accept that you will have a higher burn multiple and will need to take on more capital, but you also may get a much better exit multiple in a merger or IPO than if the same business had a lower burn multiple but was growing slower. You need to think about how these metrics help inform your higher-level company goals and analyze them accordingly.
You talked about implementing a SaaS Performance Metrics framework when a company achieves product-market fit. PMF is something that investors have wildly different definitions of depending on their stage. What do you think of as product-market fit?
Product-market fit is often ill defined. I am a big fan of something that Mark Roberge talked about at SaaS Metrics Palooza - that’s the PET metric. This is the percentage of time that a new customer does X action over Y time period. Take Slack for example, when that business was taking off they would see 70% of their customers send more than 2,000 messages in their first 30 days. Setting up a PET metric for your company is one way to measure product-market fit very analytically. Of course, you need to make sure you are tracking the right data points for your business.
Once you achieve product-market fit, it’s really important to start implementing customer acquisition efficiency metrics. Number one is your customer acquisition cost (“CAC”) ratio. I prefer CAC ratios where you are looking not just at sales but also at marketing spend - you want to know for every dollar of new ARR that you bring in the door, how much did it cost you in total? Customer acquisition costs will be higher initially but as you get more repeatability and scalability in your customer acquisition motion, the CAC ratio should decrease. I prefer looking at CAC ratio vs. CAC payback period in the early stages of building a company. Then after one or two renewal cycles, you can start measuring your gross revenue retention. Retention only becomes meaningful after 12-18 months, following a few renewal cycles.
When looking at a more mature company, I like to measure both the new logo CAC ratio and expansion ARR. If a founder says, “I am at $10MM ARR and I want to add another $8MM next year,” you need to understand how much of this will come from new customers, how much will come from expansion, and how much churn and down-sells will act as a drag on growth. This enables you to make informed decisions and realize that if you want $6MM of that $8MM to come from net new ARR and $2MM from expansions, you need to allocate internal resources accordingly. It is important to get as much granularity and predictability when growing those different lines of Gross ARR.
There are a number of key software metrics you have written about and different ones matter more or less at different points in the business’s lifecycle - are there one or two that can act as a North Star or that you have seen as the most predictive in terms of revenue acceleration or long-term success?
When trying to find the most impactful metrics for analyzing SaaS businesses, I like to look at the five pillars of enterprise value creation that I mentioned earlier and then bring them together and look for areas of overlap. Number one is growth rate - that still has the highest correlation to enterprise value creation, even at businesses doing >$100MM in revenue. Number two is CAC Ratio. Number three is gross revenue retention. Number four is net revenue retention. And number five - and this doesn’t really become relevant until the $5MM to $10MM ARR range - is the Rule of 40. By $5MM or $10MM, I want the CEO to at least start thinking about profitability. As you get to $10MM ARR, that becomes an important valuation element.
Notice I didn’t say CLTV:CAC. At $10MM or above, as an investor you want to start looking at CLTV:CAC because it tells you how much leverage you are getting from additional investments in customer acquisition. But for an operator, it’s a multivariate metric. If your CLTV:CAC is 2.8x, there can be a number of things driving it: churn rate, gross margin, acquisition costs - those multivariate or compound metrics aren’t as valuable to operators. My least favorite SaaS metric of all time is the SaaS Magic Number for a similar reason. The Magic Number measures net new ARR vs. sales and marketing investment, and it is a high-level view on capital efficiency. But I work mostly with operators, and if you are an operator you know that there are at least four variables impacting just the net new ARR part of that equation: i) New ARR, ii) Expansion ARR, iii) Downsell ARR, and iv) Churned ARR. If you are an operator, you need to know each one of those intimately before you can do any diagnostics and make any decisions and that’s why I don’t like Magic Number.
Which metrics matter the most at different stages of a company life cycle - let’s call it Seed, A, B, and Growth? How do they change over time?
A lot of what we have been discussing so far are lagging indicators (e.g., NRR) and these are really outcome metrics. But it’s the leading indicators that are going to be predictive of these outcomes and they do vary at different stages.
At a Series A company, which you can think of as $1-$5MM ARR, I really want to know my win rate. This tells me how many leads we need to add one new customer which is such an important metric for planning. I also want to know my sales cycle length and my ACV so I can think through if I increase ACV by 5% or decrease sales cycle length by 20 days, what’s the ultimate impact? The leading indicators are the levers that are available to pull as an operator. One other point on win rate that operators should keep in mind - win rate is a non-linear statistic so you need to think about time - my optimal win rate might be at day 60 of a sales cycle, but then from day 61 to 90 my win rate might decrease by 5%, and then by another 10% from day 91 to 120. It’s important to analyze win rate using time-based cohorts. A deal on Day 150 will likely have a very different projected win rate compared to one on Day 72. Most of the good full-funnel analytics and forecasting tools are using logistical regression models to do this type of analysis around every opportunity. They look at all the variables and factor in things like where the lead originated. We know if the source was paid search then that opportunity will have a different performance heuristic than a lead that came via SDR outbound.
As a company gets to Series B, it’s important to start layering on more of the efficiency metrics. Things like the relative CAC of expansion ARR vs. net new ARR. Pipeline coverage ratio also gets very important as a company scales and pipeline coverage is largely based on win rates but it’s also based on the construction of the qualified pipeline. If I just entered a new segment and started selling to SMBs two quarters ago, and they have a lower win rate than my mid-market companies, I need to adjust my pipeline coverage accordingly. You need to segment your pipeline as you enter new markets to keep pipeline coverage ratios accurate as your customer base and product base evolve. Another of the metrics that I like to drill in on in the $5-$10MM range is the percentage of ARR in a given quarter that came from new pipeline sourced in that same quarter. As a company starts investing in its brand and becoming more well known, it will tend to get more inbounds and inbounds typically have a 2-3x higher win rate and close faster than outbound pipeline.
As a company gets to Series C, I begin to emphasize the profitability metrics. This is where you start focusing on things like burn multiple and Rule of 40 because operating profitability becomes important and it’s no longer just about growth and customer acquisition. In short, different stages call for different priorities.
What is an underused SaaS metric that may not be on the radar of investors and operators?
Understanding how inbound vs. outbound is trending is one of the most underused metrics. For all of the inbound ‘handraisers’, I want to know every performance metric I can about a handraiser, as those leads typically close at a much higher rate, in a shorter amount of time, and with a higher average ACV. For deals generated by sales development, I want to know my pipeline coverage ratio, my win rate, and my sales cycle time. These aren’t really different metrics per se, but what is underused is the segmentation of the source of those leads. So many companies don’t measure inbound vs. outbound performance which I think is a mistake. I’ve met companies who have wanted to double their SDR team on the basis that SDRs had generated 50% of the qualified pipeline last year. But as we deconstruct it, and start getting into win rate by source, and look at performance attributes, that segment of the pipeline really underperformed relative to other sources. We know inbound is a great source. Time-series studies are really important for inbound. From the moment someone is raising their hand, to being contacted to schedule a first meeting, to the first meeting acceptance rate - that's another operational metric that many people aren’t on top of and it can make such a difference.
At the macro level - are there any industry wide metrics (e.g., NRR, quota attainment, etc.) that you track as a proxy for the health of the software category? What are they and what are they telling you?
We do a lot of internal benchmarking of software businesses and we always bucket them into 25th, median, 75th percentile ranges. The biggest trend I am seeing today is the spread between the 25th and 75th percentile performers are increasing across almost every metric. What that means is the good performers are getting better and the bad performers are getting worse.
Another trend I have been seeing is around growth rates - investors had become accustomed to the T2D3 framework but that needs to be re-adjusted. Growth rates on the whole have slowed across all percentiles regardless of stage of the company and may not be coming back anytime soon. But, growth rates also need to be looked at across different pricing and delivery models. Today, we see software companies that employ usage-based pricing growing almost twice as fast as those that have a traditional subscription model.
We also see a divergence in growth rates between the B2B software category as a whole compared with the sub-category of AI-native companies. AI companies are growing much faster than traditional SaaS. Part of that is they are being paid for out of the experimental/innovation budget that many enterprises have. Another factor is that many of them are pricing on usage or value-based pricing, so there is less friction around a new customer needing to sign a big contract to get onboarded. In the future, I see hybrid pricing as where a lot of pricing will net out - the subscription piece can provide a floor for forecasting and predictability for investors and CFOs but then the usage component allows the company to get some upside exposure.
Can you give us your perspective on departmental metrics vs. company-level outcome metrics and how the former can obfuscate the latter? What is the solution there?
As a software company, you should build a SaaS Performance Metrics framework for your company with a minimum of three levels. The top level are the company level performance metrics that will be reported to the board. The next level should be the “middle-metrics” - these are metrics which have a correlated or causal relationship to those top metrics we are reporting to the board. For example, if CAC ratio or CAC payback period is something we are going to measure and present to the board, what are the top three marketing performance metrics that have a direct impact on that? It might be my marketing cost per dollar of qualified pipeline or my marketing investment per dollar of new ARR. What’s important is that this second-level has an impact on the top-level metrics, and every executive and department head should have those. So they can say, I know our goals and can work towards them.
And then going down to teams and individuals, they should have measurable objectives that are directly aligned and correlated with the department goals. Make sure every metric cascades down to each individual contributor and make sure they are all measurable and causal. This way I know if I can improve win rate by 5% and ACV by 8%, I know it will decrease my CAC ratio by X dollars. And since these are leading indicators you can adapt - this way if you know win rate is down, and it's going to impact CAC payback for next two quarters, you can think of other levers to pull or adjustments to make.
If you liked “SaaS Perspectives: Ray Rike (Benchmarkit & SaaS Barometer)” and want to read more content from the Bowery Capital Team, check out other relevant posts from the Bowery Capital Blog.
