Is It Time To Ditch the Old SaaS Metrics?
We’ve grown accustomed to the traditional set of SaaS metrics as just part of how to operate a SaaS business. It’s hard to conceive of what to do without metrics like CAC payback, LTV:CAC, average ACV, or the magic number.
Here’s the thing: the traditional SaaS metrics playbook can be extremely misleading when it comes to managing a product-led growth (PLG) or consumption-based SaaS company. (Battery has a helpful visual of this in their latest Software 2021 report.)
Let’s look at a few examples, shall we?
- Product as a growth driver: Atlassian spends only 19% of their revenue on sales & marketing (slower growing New Relic spends 55% for comparison), making its CAC payback appear to be best-in-class. That’s because most of their growth is powered by the product itself through a highly efficient self-service motion. In fact, Atlassian outspends its peers on product & engineering (47% of revenue compared to 25% for New Relic). How do we account for the rise of product as a growth driver?
- Land-and-expand dynamics: Twilio has a developer-first go-to-market motion, allowing new users to sign up with low friction and no upfront costs. They then monetize with usage-based pricing and directly share in the success of their customers. This has attracted a whopping 10M+ developer accounts and 200k+ active customers. Yet Twilio’s revenue is surprisingly concentrated in the handful of winners who achieve outsized success. They have 7 customers spending $10M+ per year and 142 spending $1M+ per year. Companies like Twilio need to treat new signups as a portfolio; many will cluster at the low end with modest (or no) usage and a subset will scale rapidly. How do we think about retention for “whale and tail” businesses?
- Limitless LTV:CAC: Snowflake reported a 168% net revenue retention rate in their latest earnings call. With that type of exponential growth, $1,000 from new customers would in theory turn into >$13,000 per year after 5 years. Should they try to maintain a “healthy” CAC payback period of 18-24 months? Probably not! What’s the point of LTV:CAC if LTV is essentially limitless?
The reality is that the OG SaaS metrics still have a place, especially for companies following the traditional top-down, subscription-based playbook. But we also need to expand the aperture of how we define success for PLG and usage-based businesses. I’d like to share some of my favorite PLG KPIs from both an executive and an operational perspective.
- Return on incremental invested capital (ROIIC): Think of this as a more PLG-centric way of understanding CAC payback that helps you make the right trade-offs between growth investments whether they be in sales, marketing, product, M&A, or elsewhere. ROIIC = (Gross Profit – G&A Expense) / (Sales & Marketing + Product & Engineering Expense). You can measure it either quarterly or over the last 12 months.
- New ARR vs. cash burned: This is a simplified heuristic that’s meant to provide similar insight as ROIIC and can flag whether you need to dive deeper. The more incremental ARR you generate for every $1 you burn, the better. Best in class is considered >1x, but the metric is highly dependent on a company’s size/stage and economic model. (P.S. Here’s a helpful rundown by Nnamdi Iregbulem at Lightspeed.)
- Natural rate of growth (NRG): The goal of this metric is to delineate how fast you’re growing “without trying” (i.e., from non-paid signups who jump straight into the product rather than talking to sales) vs. how much comes from intentional paid efforts (i.e., paid marketing, SDR/BDR efforts, and the like). NRG = 100 x Annual Growth Rate x % Organic Signups x % of ARR that starts in the Product.
- Cohort retention by customer type: Cohort-based metrics, where you track the progression of behavior among a group of signups that start at the same time, are by no means new. But these metrics are increasingly important vs. looking at average values, which can be highly misleading in a PLG business. You’ll want to track cohort-based spend and retention and get granular around behavior by go-to-market channel, number of users, country, and other factors.
- Annuity graph: In Battery’s report, they called out an annuity graph, which combines the realized lifetime value and CAC for different customer segments. The idea here is to be able to visualize and compare the customer journey and associated economic model across things like channel (self-service vs. enterprise), country, or customer size so that you can have a clear sense of where to double down going forward. (P.S. Here’s a helpful Google Doc template they put together.)
- Lead volume: You’re looking to track lead volume over time both in terms of free product signups and traditional lead generation (demo requests for sales, marketing-generated leads).
- Activations: Signups are meaningless if they don’t take any action in the product. The most important product KPI to measure is your activation rate (i.e., how many of your signups take a high-value action in your product in their first 7 or 14 days). Each company will need to create their own definition of activation that fits with their specific product. In my experience it should be something that’s (1) easily achievable by somewhat committed users, (2) able to be completed within the first week from signup, (3) predictive of future conversion/retention, and (4) correlated to business performance. 20-40% activation rates are common.
- Product qualified leads (PQLs): PQLs take activation to the next level, leveraging product behavior to indicate buying intent and usually triggering some sort of sales or customer success outreach. PQLs are usually calculated as a score from 0 (no intent) to 100 (highest possible intent). They may sit alongside traditional MQL scoring so that go-to-market teams engage with product users based on both their product behavior and their marketing qualification (company size, role in the organization, region, and so on).
- 14-day and 90-day conversion rates: A user’s conversion journey isn’t static; it could happen on the same day as signup or not until years later. I recommend looking at both 14-day and 90-day conversion as an indicator of the health of your cohorts. These time-based metrics are important for running experiments and determining whether your interventions actually had the desired outcome.
- Cost per activated lead: Marketing attribution is never easy. Many folks look at marketing’s influence on generating signups or they go all the way down to marketing-generated revenue. I recommend also looking at marketing’s influence on activated signups (i.e., signups who reach your activation milestone). This acts as a great filter to determine whether your marketing campaigns are targeted at users who will actually convert when given enough time. And you’ll know this as soon as 7 days after running a campaign, allowing for faster decision making to ramp up or down spend.
- Signups by channel: Successful product-led businesses generate a disproportionate share of their new users through low-cost channels such as organic search/SEO, word of mouth, product invites, referrals, and channel marketplaces. These low-cost channels also tend to bring in new users who are more likely to stick around. To get started, I recommend looking at these specific channels: organic, paid, sales-generated, product-generated, channel-generated.
- Self-serve generated sales pipeline: For many companies, self-service is only responsible for a small share of their overall revenue. But it plays a much bigger role when you consider users who started with self-service and eventually graduated to the sales pipeline. Make sure to pay attention to both direct self-service revenue and self-serve generated sales pipeline to gain the best visibility into your PLG efforts.
I hope this post helps start a conversation about which metrics matter most for your business. But the SaaS metrics playbook is still being written when it comes to PLG businesses. I’d love to hear from you about which PLG metrics you track and why. Leave a comment here.
More PLG resources
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