Nobody starts budget season planning to end up in front of a corkboard with red string. It happens somewhere around v14_FINAL_FINAL, when a VP wants three heads moved from Q2 to Q1 and you rebuild the model. Again.
Abacum is where that stops. Reforecasts update without you touching a single cell reference. Everyone works in the same version. And when someone floats a hiring change mid-call, you show them the cash impact right there instead of promising numbers by Thursday.
You'll still work through budget season. You just won't need the red string.
Talk to the Abacum team today!
Why CAC Payback Is More Useful Than LTV to CAC
Whenever I’m presented an LTV to CAC number, I think like a credit guy:
"How am I getting f***** tricked on this?"*
Niche banking jokes aside, I was recently speaking with Datadog’s longtime SVP of FP&A, AJ Ljubich, who shared his love of CAC Payback Period, and simultaneous dismay with most LTV to CAC figures.
On a few occasions I've written about my beef with LTV to CAC (Customer Lifetime Value to Customer Acquisition Cost). It's a cauldron of variables (revenue, churn, retention, gross margin) all bubbling into one number, and if the number stinks, you can't call up Doug in marketing and tell him to fix it.
CAC Payback is the metric I actually encourage operators to make decisions off of.
Why CAC Payback is great
"It brings together your top line momentum and results, as well as your investments and the expenses."
It’s very easy to look at an investment decision and say, well, this is adding incremental revenue and it's growing quickly, so therefore it’s successful. Maybe. But you’re leaving off the part of the picture that shows what it took to get there.
Or vice versa. You might say this is cheap to do, it's quick and easy, but okay, is it adding any incremental value?
CAC Payback is one of the few metrics that actually brings those two concepts together.
Why LTV is often misleading
"In comparison to things like LTV or LTV to CAC, the inputs are so sensitive. Especially for a high retention, high gross margin business like Datadog, you can get results that are just silly and sort of hypothetical."
LTV to CAC is both too compound (lots of inputs) and too sensitive (it can swing wildly based on those inputs, especially account churn rates).
AJ again:
"We're trying to make decisions based on the data. If I get something that's very hypothetical and super sensitive to inputs, it doesn't add that much value and frankly doesn't give you much confidence moving forward with a decision."
If the metric changes dramatically every time you touch one assumption, it’s a pretty bad signal to make an operating decision off of.
You can actually benchmark CAC Payback (to an extent)
The payback approach is much more tangible. If I make this investment today, I'm going to recoup my cost in a year or a year and a half (or 96 months if you're Domo).
It's also pretty comparable across companies. You can look at public comps, like the ones below that I update each Sunday, and if you have your non-GAAP sales and marketing expenses, non-GAAP gross margin, and change in revenue, you can get a decent apples-to-apples comparison of how your efficiency stacks up against other companies.
To be fair, there are limits to this type of benchmarking:
"The publicly reported metric is always going to be very high level and an amalgamation of a lot of things. It fluctuates, and every business has a different business model, so it might be less or more applicable depending on the accounting of your revenue, how much services revenue you have, what the gross margin means."
So it's a guidepost. And you need to self-select the companies in your sector (e.g., cyber security) and with your business model (e.g., marketplace). And then, minimally, you can figure out if you are top decile, middle of the pack, or bottom decile.
You get a little bit of the red, yellow, green going on.
You can click down into the organization
CAC Payback is also a rare metric because you can use it beyond the high-level company number. If you’re willing to make some assumptions around allocations, you can really click down into the org.
"For us we look at our sales channel payback to understand how our different go to market engines are working and how the efficiency there compares."
And you can keep going deeper, by region, ultimately even by country, and look at the investments you're making through that same lens.
Take Datadog.
They have two core sales channels. One is a heavy field sales, enterprise motion. The other is characterized by high velocity inside sales, selling to cloud natives and smaller organizations, with the goal of landing the initial customer and handing them off to the customer success team.
AJ has been at Datadog for many years, both pre and post IPO, so he watched the mix shift in real time. As the company got more enterprise-y, there was a natural question of whether their efficiencies were going to degrade over time, since an enterprise sales motion is heavier and more difficult to deploy.
Turns out the two motions are relatively close to each other.
The mix of what you're spending to get that customer dollar is just different. More of the digital marketing spend and advertising dollars go toward the cloud native velocity motion. A heavier sales focus, with larger OTE AEs, goes toward enterprise.
Net net, their CAC Paybacks look relatively similar, although they take different paths to get there.
CAC Payback helps you test operating decisions
Datadog also uses the metric to guide account coverage: how many accounts does each AE own, and over what period of time?
They have a blend of AE groups, some more hunter, some more curator and farmer. One question was whether they'd be better off with their best reps holding accounts longer.
That’s more expensive. It calls for more commission dollars, with more time and attention going to those accounts. The thesis is that it pays off through a healthier long-term customer relationship and more use of the Datadog platform.
Because they already had a benchmark for what good payback looked like, they could test that thesis without just chucking money into the abyss and hoping the enterprise reps figured it out.
They've been able to run pilots with certain groups of AEs holding accounts longer, continue those over time, and measure whether the additional investment actually paid off.
AJ on why this is the part he cares about:
"To me the more interesting part is the one making operational decisions off of. We might want to make an investment and throw some money at something, but we always want to start with a hypothesis of what the actual payoff is going to look like. In a year, if we do this thing, what do we think this is going to look like? What is the incremental revenue that's going to come from it, and when do we expect to recoup that payoff, and does that degrade or improve our efficiencies as a company? And that may be a good thing or a bad thing, but we want to be very conscious with what we're moving forward with."
That’s what I want out of a metric. We spent more money… We expected this much incremental revenue…We expected to earn the money back by this date.
A year later, we can see if we were full of crap.
LTV to CAC is flawed, but money be green
And yet, none of that gets you out of reporting the other metric, as unhelpful as it may be to actually running your business in the moment.
Alex Immerman and his team at a16z ran the numbers and found that a 3x LTV to CAC business carries roughly a 3x higher valuation multiple than a 2x business.
One more turn in the ratio rewards you with three times the multiple, which explains why your investors are obsessed.
LTV to CAC isn't something I'll use within my business day to day, but it's something I'll monitor and report on because it helps raise the equity value of the company.
Therefore, when you build your operating model, you should deconstruct the metric down to the raw inputs, assigning each component to someone internally to own. Then it falls on finance to translate the meaning of the output and quarterback changing the individual inputs to move it in the right direction.
In the short term, LTV to CAC is kinda useless.
But in the long term it's a weighing function.
And at some point it will be time to weigh in to see what we're worth. Best to understand how the scale works before you step on it.
In the meantime, keep track of your calories using CAC Payback Period.
That’s how you get dem gainz.
Our patron saint of gainz: Ronnie Coleman
Run the Numbers Podcast
Weekly Valuation and Efficiency Metrics
Revenue Multiples
Revenue multiples are a shortcut to compare valuations across the technology landscape, where companies may not yet be profitable. The most standard timeframe for revenue multiple comparison is on a “Next Twelve Months” (NTM Revenue) basis.
NTM is a generous cut, as it gives a company “credit” for a full “rolling” future year. It also puts all companies on equal footing, regardless of their fiscal year end and quarterly seasonality.
However, not all technology sectors or monetization strategies receive the same “credit” on their forward revenue, which operators should be aware of when they create comp sets for their own companies. That is why I break them out as separate “indexes”.
Reasons may include:
Recurring mix of revenue
Stickiness of revenue
Average contract size
Cost of revenue delivery
Criticality of solution
Total Addressable Market potential
From a macro perspective, multiples trend higher in low interest environments, and vice versa.
Multiples shown are calculated by taking the Enterprise Value / NTM revenue.
Enterprise Value is calculated as: Market Capitalization + Total Debt - Cash
Market Cap fluctuates with share price day to day, while Total Debt and Cash are taken from the most recent quarterly financial statements available. That’s why we share this report each week - to keep up with changes in the stock market, and to update for quarterly earnings reports when they drop.
Historically, a 10x NTM Revenue multiple has been viewed as a “premium” valuation reserved for the best of the best companies.
Efficiency
Companies that can do more with less tend to earn higher valuations.
Three of the most common and consistently publicly available metrics to measure efficiency include:
CAC Payback Period: How many months does it take to recoup the cost of acquiring a customer?
CAC Payback Period is measured as Sales and Marketing costs divided by Revenue Additions, and adjusted by Gross Margin.
Here’s how I do it:
Sales and Marketing costs are measured on a TTM basis, but lagged by one quarter (so you skip a quarter, then sum the trailing four quarters of costs). This timeframe smooths for seasonality and recognizes the lead time required to generate pipeline.
Revenue is measured as the year-on-year change in the most recent quarter’s sales (so for Q2 of 2024 you’d subtract out Q2 of 2023’s revenue to get the increase), and then multiplied by four to arrive at an annualized revenue increase (e.g., ARR Additions).
Gross margin is taken as a % from the most recent quarter (e.g., 82%) to represent the current cost to serve a customer
Revenue per Employee: On a per head basis, how much in sales does the company generate each year? The rule of thumb is public companies should be doing north of $450k per employee at scale. This is simple division. And I believe it cuts through all the noise - there’s nowhere to hide.
Revenue per Employee is calculated as: (TTM Revenue / Total Current Employees)
Rule of 40: How does a company balance topline growth with bottom line efficiency? It’s the sum of the company’s revenue growth rate and EBITDA Margin. Netting the two should get you above 40 to pass the test.
Rule of 40 is calculated as: TTM Revenue Growth % + TTM Adjusted EBITDA Margin %
A few other notes on efficiency metrics:
Net Dollar Retention is another great measure of efficiency, but many companies have stopped quoting it as an exact number, choosing instead to disclose if it’s above or below a threshold once a year. It’s also uncommon for some types of companies, like marketplaces, to report it at all.
Most public companies don’t report net new ARR, and not all revenue is “recurring”, so I’m doing my best to approximate using changes in reported GAAP revenue. I admit this is a “stricter” view, as it is measuring change in net revenue.
OPEX
Decreasing your OPEX relative to revenue demonstrates Operating Leverage, and leaves more dollars to drop to the bottom line, as companies strive to achieve +25% profitability at scale.
The most common buckets companies put their operating costs into are:
Cost of Goods Sold: Customer Support employees, infrastructure to host your business in the cloud, API tolls, and banking fees if you are a FinTech.
Sales & Marketing: Sales and Marketing employees, advertising spend, demand gen spend, events, conferences, tools.
Research & Development: Product and Engineering employees, development expenses, tools.
General & Administrative: Finance, HR, and IT employees… and everything else. Or as I like to call myself “Strategic Backoffice Overhead.”
All of these are taken on a Gaap basis and therefore INCLUDE stock based comp, a non cash expense.
Companies Included
1. Security & Identity (16 companies) Endpoint, network, IAM, security operations. The CISO budget.
CrowdStrike, Palo Alto Networks, Fortinet, Cloudflare, Zscaler, Okta, SentinelOne, SailPoint, Check Point, Qualys, Tenable, Rapid7, Varonis, Rubrik, Mitek, OneSpan
2. Data & AI Infrastructure (12 companies) Modern data stack, AI/ML platforms, vector and analytics infra, GPU compute. Software-native by design.
Snowflake, Arista Networks, Equinix, CoreWeave, MongoDB, DigitalOcean, Elastic, Akamai, Fastly, Teradata, C3.ai, Cerebras
3. Dev Tools & Observability (10 companies) Anything bought out of the engineering budget.
Datadog, Atlassian, Figma, Dynatrace, Nutanix, GitLab, UiPath, JFrog, AvePoint, PagerDuty
4. Horizontal SaaS & Back Office (18 companies) Software sold across industries to ops, HR, finance, and collaboration teams. Not vertical-specific.
Oracle, ServiceNow, Workday, ADP, Paychex, Paycom, Paylocity, Zoom, DocuSign, Navan, monday.com, Asana, Workiva, BlackLine, RingCentral, 8x8, Box, Dropbox
5. GTM (MarTech & SalesTech) (18 companies) Anything bought out of the revenue org. Marketing automation, sales engagement, CRM, ad tech, customer experience.
Salesforce, Adobe, HubSpot, The Trade Desk, Twilio, Klaviyo, Braze, ZoomInfo, Freshworks, Amplitude, Semrush, Five9, Zeta Global, Wix, Sprout Social, ON24, Yext, Criteo
6. Vertical SaaS (15 companies) Software built for a specific industry without take-rate or transaction economics.
Palantir, Autodesk, Veeva, Samsara, ServiceTitan, Guidewire, Tyler Technologies, Doximity, Procore, AppFolio, CCC Intelligent Solutions, Blackbaud, nCino, CareCloud, CS Disco
7. Take-Rate Platforms (18 companies) Marketplaces and commerce platforms that earn money on transaction volume.
Uber, Airbnb, Shopify, MercadoLibre, DoorDash, eBay, Zillow, CarGurus, Instacart, Etsy, Toast, Lyft, Opendoor, StubHub, Upwork, Coursera, Ethos, Fiverr
8. Payments & Money Movement (10 companies) The rails. Payment processors, payment infrastructure, B2B payments, treasury. Volume game, utility margins.
Intuit, Fiserv, Adyen, PayPal, Block, Shift4, BILL, Flywire, Marqeta, Lightspeed
9. Consumer Fintech, Lending & Crypto (15 companies) The front-end. Consumer-facing financial apps, BNPL, lending platforms, crypto exchanges. CAC-driven, marketing-heavy, totally different unit economics from #8.
Coinbase, Robinhood, SoFi, Chime, Affirm, Upstart, Circle, Bullish, Figure, Klarna, Sezzle, Gemini, Blend, Remitly, LendingClub
Please check out our data partner, Koyfin. It’s dope.
Wishing you trade at a high revenue and EBITDA multiple,
CJ
