The Return of Capital Efficiency

Capital efficiency has long been a desirable trait in early/growth stage businesses. But over the last few years, an abundance of capital combined with a “growth at all cost” mindset, allowed founders to deprioritize efficiency. Ignoring efficiency, however, can lead to making cardinal mistakes like misreading true product-market-fit, over-hiring for the stage you are in and burning through too much money too quickly. Furthermore, growth rate and top-line progress are a function of how much capital a business has consumed to get to that point (i.e. getting to $10M in revenue is less impressive if you spent $50M to get there vs spending $5M to get there.)

In a post-covid world, capital efficiency has returned as king. This is especially true in SaaS (which, as a category, has outperformed almost every other category.) Many of the companies that have outperformed during this time frame have been very efficient businesses (e.g. Twilio, Zoom, Shopify, Datadog, etc.) As the fundraising markets dry up a bit and sales cycles lengthen, founders will increasingly be forced to think more about efficiency and investors will pay a premium for efficient businesses.

But how should SaaS founders think about efficiency? Several years ago, Bessemer put out a simple, but helpful rule-of-thumb called the BVP efficiency score. The efficiency score shows a “good-better-best” framework for thinking about capital efficiency (defined as Net New ARR / Net Burn.) They advised founders (under $30M ARR) to think about good-better-best using the table below:

1

While this is a great high-level framework, efficiency among SaaS businesses is a bit more nuanced depending on stage. In the formative days, finding product-market-fit can take time and money. In the early days of growth, building a scalable and repeatable playbook can require significant up-front investment. As the company moves into expansion-mode, the business benefits from clear economies of scale and an improved gtm playbook. In the later stages, the business should be humming and efficiency ought to be at an all-time high.

The point is: benchmarking efficiency in a meaningful way requires looking more closely at stage/ revenue profile. What we really need is an efficiency score for each stage. Or, put differently, a rubric showing how much capital ought to be consumed (and, yes, there is a difference between “raised” and “consumed”) to achieve various ARR milestones along the journey from $0M to $100M in ARR.

Below are two frameworks for founders to use to help answer this question. These tables were developed based on what I’ve seen in the field over the years and have been triangulated with what several other SaaS investors have also seen. The first table is simply a good-better-best framework for total capital consumed to get to different ARR thresholds. The second is a “stage-adjusted” efficiency score. These two tables are, of course, two sides of the same coin.

2

3

Bear in mind these are simple guidelines / “rules of thumb” and anecdotal in nature. Every business has its own set of nuances and unique circumstances. And there is definitely more variability earlier on depending on the nature of the product (i.e. some companies have to invest a lot more in R&D to get the product to market.) Where you land on the grid is less important than what the trend-line looks like and whether you have managed cash wisely (i.e. been a “good steward of capital.”)

To bring this to life a bit, here are a few “hall-of-fame” worthy examples of companies that scaled past 100M in ARR with record breaking efficiency. Note that we are listing capital raised here as a close proxy in the absence of public data on capital consumed:

  • Veeva raised a total of $7M pre-IPO. Current market cap: $33B
  • Appfolio raised a total of $30M pre-IPO. Current market cap: $5B
  • Ringcentral raised a total of $44M pre-IPO. Current market cap: $24B
  • Wix raised a total of $59M pre-IPO. Current market cap: $11B
  • Salesforce raised a total of $65M pre-IPO. Current market cap: $162B
  • Zendesk raised a total of $86M pre-IPO. Current market cap: $9B
  • Realpage raised a total of $86M pre-IPO. Current market cap: $7B

It is no surprise that almost all of the above examples got going in the “good old days” of the 2000s, when capital was less plentiful, and efficiency much more in vogue. Much of this changed in the 2010s, but I suspect we will see the pendulum swing back to some degree in the decade ahead. Hopefully, this helps provide some useful data points in this return-to-efficiency world we now find ourselves in!

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I’d like to thank Alex Kurland (@atkurland), Brian Murray (@murr), Chetan Puttagunta (@chetanp), Logan Barlett (@loganbartlett), Murat Bicer (@itsbeecher) and Parsa Saljoughian (@parsa_s) for their feedback and help in triangulating the numbers here.

Hybrid B2B Revenue Models…and How to Value Them

Summary:

  • While the 2000s and 2010s gave birth to many B2B SaaS greats, the 2020s will usher in a new wave of winners that have far more heterogenous business models.

0. Josh Kopelman

As we begin to reach a certain level of maturity among cloud applications, it has become increasingly clear that we are now moving beyond the first wave of pure SaaS players that came to define the 2000s and 2010s and produced big B2B wins like Salesforce, Atlassian, Zoom, Hubspot and many others. In more recent times, we’ve migrated from this homogenous SaaS world to a more complex world of hybrid businesses, which generate different types of revenue in their quest to build enduring value. This, of course, has played out in many industries beyond software. Costco, for example, was one of the OGs here with its membership subscription fee + item price revenue model.

In some cases, hybrid models are an evolution over time: an early stage company starts with a wedge software product that customers love and then evolves in the growth stages to include additional features that drive new sources of revenue like lead gen fees, payment transaction revenue, lending revenue, etc. This is the story of Shopify, which originally generated subscription revenue for access to its ecommerce software tools before evolving to include additional revenue sources like payments, transaction fees from apps in its app marketplace and other “store-front fees” like domain registration.

In other cases, mixed revenue streams can happen right from the get-go. Our portfolio company, Sendoso, has operated as a SaaS + Transaction revenue-model from Day 1. Customers pay a subscription fee for access to the platform and a set of integrations into the sales, marketing and customer success stack. Additionally, they then pay a separate transaction fee for physical or virtual items sent through the platform to current customers or prospects.

Shopify and Sendoso are certainly not the first businesses with a hybrid model, nor will they be the last. As we enter a world where mixed-models become more common, the two questions then become:

(1) What will these mixed-models look like?

(2) How do founders think about valuation in the absence of less established rules of thumb?

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SaaS: Established Rules of Thumb

But before we get to answering these two questions, it’s helpful to review the basics behind the most successful B2B business model of the last 2 decades: pure SaaS. It is well understood that the two most important financial drivers impacting the valuations of public SaaS companies are, first and foremost, growth rate and second, to a lesser extent, gross margin (though the latter may increase in importance given the recent times.) Below is a view from a basket of SaaS businesses. For illustrative purposes, this is a snapshot taken from February, before the market volatility caused by coronavirus.

1. Growth Rate

2. Gross Margin

To sum: most public SaaS businesses north of 100M ARR that are growing 30–40% with 70–80% gross margins can command a multiple of ~10–12x on the public markets (or at least they could pre-coronavirus; we will know over the coming months whether the current deflation is temporary or here to stay.)

In the “earlier” venture to growth-stage world, this translates into a number of operating levers that are well understood. This post is not meant to be a review of the literature on SaaS metrics but there are some great resources for further reading on these topics, which I’ve included in an appendix at the end.

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Mixed Revenue Models: Forging into Newer Territory

In today’s world, we are seeing a notable uptick in mixed revenue models. B2B companies chasing additional growth opportunities are realizing that once they have achieved clear customer lock-in with one product, maintaining a high growth rate and expanding their TAM, can often be accomplished by cross selling other ancillary products — many times with different types of revenue. This has taken the shape and form of at least four playbooks:

(1) Software + Services

Selling services in addition to software is of course nothing new. In the on-prem/ perpetual license world, professional services were essential to the delivery and implementation of enterprise software. In the cloud application world, professional services typically play a similar role when selling to large enterprises (e.g. the customer base has a lot of F500 customers.) These customers typically require broad integrations, time-consuming security audits and a white-glove experience. While necessary and incremental to top line, services revenue is broadly viewed as less valuable than SaaS revenue.

Workday and Veeva are two great examples of companies that have continued to excel at growing both SaaS and Services revenue. To this day both companies still have a very significant (and growing) services revenue stream (i.e. hundreds of millions of revenues annually) in addition to the SaaS revenue.

3. WDAY and VEEV

(2) Bundled Financial Services

A common theme we are seeing, especially within FinTech is the bundling of financial services. Typically, a business will find initial PMF around a single product with a single source of revenue — for example payments. Overtime, the business will offer its customers additional financial products generating additional revenue from things like lending, referrals to 3rd parties, % of AUM, interchange and a range of other revenue models.

Stripe is a great example of a company that has executed very well on this playbook. In “Act One,” Stripe created tremendous lock-in around it’s payments platform by enabling companies to process card charges on a 2.9% + $0.30 per transaction basis. But as the company evolved over time they built new products with different revenue models (see here for more info):

(3) Software + Bundled Financial Services

But FinTechs are not the only players to bundle financial services. We have begun to see a number of SaaS businesses use application software as an entry point, create lock-in with recurring revenue and then embed a host of other financial services directly into the platform. In doing so, these businesses can generate incredible momentum, widen their TAMs while also maintaining a broad base of stable recurring revenue.

No one has executed better on this playbook than Shopify, which has grown to over $70B in market cap (accelerating through covid-19 no less) and has commanded a revenue multiple of over 30x at certain times. Shopify’s SaaS business gives merchants access to its ecommerce platform + tools to build storefronts; while it’s Merchant Solutions business (i.e. bundled financial services) generates revenue from customers via lending, payments, shipping and referral fees. In the early days, software was the main driver of revenue growth, but over time the financial services have accelerated in a very impressive way.

4. Shopify

(4) Software + Bundled Financial Services + Hardware

The final hybrid model we have seen is effectively #3 above with the addition of hardware. Hardware stand-alone businesses, of course, are notoriously difficult and very hard to operate successfully at scale. But hardware combined with the margins of SaaS and the extended reach of bundled financial services can be a very powerful business. Toast is a great example of a company that has successfully leveraged all three revenue sources to build a very effective business in the restaurant vertical (see here for more info):

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Hybrids: A Weighted Average Approach to Valuation

Mix-model business are thriving and clearly here to say. But valuing high-growth hybrids is more challenging in the absence of the simple heuristics developed for the SaaS world. My suggestion on how to value these companies in the early/growth stages (~$2-$20M in revenue) is to use a weighted average revenue multiple approach. In other words:

1. Break down the business into its various components based on where it is today from a net revenue perspective

2. Apply specific multiples to each of the distinct parts of the business based on general heuristics associated with underlying characteristics like growth rate, margin profile, usage frequency, etc

3. Add in a “boost” or “mute” for external factors like TAM, LTV, retention, depth of competition, customer profile, how the revenue mix may shift over time, etc. This is a big part of the “magic”

4. Use a Sum-Product function across revenue and revenue multiple

Below is a table that illustrates the valuation equation and some general “rules of thumb” as guidance:

5. Valuation

Example One

SMB SaaS business that helps its customers make payments to vendors and also generates a lead gen fee for referring its customers to new vendors. On the SaaS side (SMB so self-serve and no services), the business seems to be in the early innings of a strong growth trajectory (3x.3x.2x.2x.2x) having grown from 2M ARR to 6M ARR in the last year ($4M in revenue associated with the SaaS ARR.) The business did an additional $4M in payments revenue and $2M in lead gen revenue; for a total of $10M in revenue. The company operates in a large, mostly greenfield TAM and, over time, the payments revenue will grow to be the clear leading driver of revenue while the lead gen revenue becomes less relevant.

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As illustrated above, this is a SaaS + Bundled Financial Services model consisting of subscription revenue, payments revenue and lead-gen revenue. In addition, we applied a relatively high Boost of 0.75 to account for the strong growth profile and large/greenfield TAM; somewhat muted by the lower-multiple payments revenue being the predominant driver of long-term growth. The weighted multiple is ~9x.

Example Two

The second example, a POS terminal business that operates in corporate cafeterias, is also doing $10M in revenue. In addition to charging for the terminals, the company charges an installation fee for set up, generates payments revenue from processed transactions and takes a cut of revenue from any 3rd party apps installed on its devices. However, this business is slower growth due to longer sales cycles (grew < 40% last year.) The company also faces fierce competitors like Square, Toast and Revel.

2

As noted above, this is a Software + Bundled Financial Services + Hardware company. In addition to being comprised of different components than the company in example 1, this is also a lower growth business with 2–3 dominant competitors in market. As such, we added a lower boost scale and the weighted multiple ends up being ~4x.

Template: If you’d like to access these examples, and maybe run a few scenarios yourself, I’ve included a google sheet (here) where you can give it a try. Always open to suggestions on how to improve this so feel free to send my way.

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Final Thoughts

We’re moving into a more heterogenous world, where mixed-model revenue businesses will continue to emerge and thrive. As this new class of companies grow and thrive, founders and investors will need to better understand how to operate, grow and (ultimately) value these businesses. In some cases, it may make a lot of sense to start by valuing a company with one approach (e.g. SaaS) and then layer in other approaches over time as the company evolves. But taking a weighted average approach to valuation in conjunction with a bit of good judgement is a great way to understand valuation for these hybrids.

Appendix: Further Resources on SaaS

Overall SaaS Frameworks:

Growth Rate:

Retention

Sales Productivity / Efficiency

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If you have a different approach, I’d love to hear about it (@MrAllenMiller!) I’d also like to thank Kris (@rudeegraap), Dimitri (@dadiomov), Ian (@iankar_) and Sheel (@pitdesi) for their contributions to this piece.

Metrics that Matter in SaaS

Today, software entrepreneurs are very fortunate to have a wealth of information available on the indicators and metrics to focus on when running a SaaS business. There is so much out there that it can be a bit overwhelming to absorb. With that in mind, I’ve put together a one page summary of the core areas every SaaS founder should focus on when first starting and running a SaaS business.

This is not meant to be an exhaustive list of every KPI but rather an 80/20 “boil-it-down-to-what-matters-most” view of the qualitative and quantitative indicators of the overall health of a SaaS business. This also doubles as a checklist when going out to raise an institutional round of capital (most VCs will ask for these metrics as part of their diligence process.)

capture

The way to think about it is in 4 categories.

  • (1) Qualitative: Indicators in this category, while not as quantitative as the rest on this list, are likely to be the most important for early stage companies. They include a sharp focus on the team and the founder(s). The product/ service itself and early customer feedback are likewise very important.
  • (2) Market Metrics: Venture investors care a lot about the market in which a business is focused on (and entrepreneurs should as well to ensure they are solving a worthy problem!) Key metrics here include the overall TAM and growth (or stagnation/decline) of the industry. In addition the competitive landscape, both the number of competitors and share of each competitor, is key.
  • (3) Financial Metrics: Metrics in this category tend to be a bit more objective – but even here much is dependent on the idiosyncrasies of a particular business, what stage it is in and the market opportunity ahead of it. Here, most financial metrics boil down to 3 things:
    • Top-line revenue and growth: CMRR/CARR is the most accurate predictor here
    • Margin profile: some combination of gross and operating margin
    • Cash position:both burn rate and runway
  • (4) Operating Metrics: Operating metrics tend to be a bit more unique in SaaS than in other business models. A good way to think about operating metrics is through three sub-categories:
    • Customer willingness to pay: a combination of ACV, NPS, expansion revenue, etc. combined with the pricing model employed can help determine overall WTP
    • Sales efficiency: magic number (developed by Scale Venture Partners) is a great metric as are payback period and sales cycle length
    • Churn: gross revenue churn is closely tied to growth but cohort analysis and the quick ratio (developed by Social Capital) are also good metrics to track

As mentioned earlier, there is a wealth of information on all of 4 of these areas as well as best-in-class metrics based on revenue, stage, etc. Some of the best material out there for further reading includes: Byron Deeter’s State of the Cloud report, David Skok’s For Entrepreneurs blog and Jason Lemkin’s content on SaaStr.

Core & Emerging Platforms as we Move into 2017

Innovation at the platform level (whether it be improved hardware, changes in infrastructure or new ecosystems) has always led to new opportunity at the application level for both entrepreneurs and the investors that back them. As 2016 winds down and we look ahead to 2017, it’s as good a time as any to take stock of the innovation we’ve seen at the platform level in the last few years and the trends in tech that will drive new opportunity in application software.

More specifically, I see four core and emerging trends that will continue to dictate opportunity in B2B software: (1) continued dominance of cloud, (2) acceleration of mobile enterprise, (3) increased attention to AI (more specifically machine learning) and (4) the rise of AR & VR – particularly AR in the B2B setting. The figure below provides an overview that will be explained in further detail below:

tech-platforms

(1) Continued dominance of cloud

This is an “old” one but a good one. Of the four platform trends this is the most established one and has produced the most opportunity to-date.

From a horizontal perspective, the cloud has penetrated (though not yet dominated) every function within the enterprise. Salesforce is the prevalent choice for most in the sales / CRM functions. Companies like Workday, Cornerstone and SuccessFactors have gained real traction within HR. Eloqua, ExactTarget and Marketo are widely used marketing tools. NetSuite has a strong presence in ERP while Zendesk is a strong force in customer success. And there are many other more recent horizontal SaaS companies that have made big waves: Slack, Stripe, DocuSign and DropBox are just a few of many that had big years in 2016. And there are many more opportunities remaining in relatively untouched areas like: sales ops, SMB-focused HR tools, inventory management, market intelligence and customer care analytics.

Vertical software, is still very much in its infancy. There have certainly been some early winners like Veeva (life sciences), RealPage (real estate) and Fleetmatics (fleet management), but there are many more industry cloud winners to come. Industries like manufacturing, construction, logistics, agriculture, oil and gas and others have slowly begun moving to the cloud after remaining cloud-allergic for many years. 2017 will be a big year for many of these industries and the vertical-focused, category-winners that reshape them.

(2) Acceleration of mobile enterprise

Aggregate mobile enterprise revenue in 2016 was just under $100B –pretty solid for a platform that didn’t exist 10 years ago. However, this one is also just getting started. Forecasts show this number doubling by 2020 (and I wouldn’t be surprised if the growth rate is higher than that). Part of this growth is fueled by increased vertical software opportunities. Procore is a great example of a company delivering a vertical specific solution (in construction) via mobile enterprise. Industries like education, insurance and real-estate will soon follow.

(3) Increased attention to AI  

2016 really marked THE year when AI (or more accurately, machine learning) really came into focus in the startup and venture community. As seen in the figure above, deals done and investment dollars poured into the sector have grown exponentially in the last 2-3 years. In that time, AI has done a few interesting things:

  • It has re-opened the door in a real way to more horizontal software opportunities giving rise to the “disruption of the disruptors.” Suddenly, machine intelligence has allowed for greater insights and better products and services that opened the door to new entrants looking to enter horizontal spaces.
  • It has allowed for more focused solutions that really benefit from machine learning applied to large data sets to flourish. Little Bird (a market intelligence and data analytics company based out of Oregon) that was recently acquired by Sprinklr is a good example. AI powered point solutions like Little Bird, once bolted onto larger platforms (like Sprinklr’s social media management platform) can exponentially increase the utility to their enterprise customers.
  • It has brought back IBM’s relevance among innovators and early stage companies. Ironically, rightly or wrongly, IBM’s Watson is the most common machine associated with machine learning. Whether IBM is able to harness the potential of AI remains to be seen, but the company attempts to be mounting a bigger challenge to be a dominant presence in the space rather than giving way to the big four (Apple, Facebook, Amazon and Google) as it did with consumer devices, social, ecommerce and search.

Expect AI to be a powerful trend in 2017 and beyond, with both startups and established players getting involved, especially as the technological innovation becomes more advanced.

(4) The rise of AR & VR

AR and VR are the furthest off in terms of real platform potential and 2016 was largely a pretty big disappointment for these platforms. The biggest thing in AR/VR in 2016 was Pokémon Go, which was an entirely consumer play (and appears to largely have been a fad). I expect VR to still be a few years away from going mainstream –and even when it does, it will continue to be a consumer play.

That being said, I do think in 2017 we will see the start of some AR-based software applications that will gain traction among enterprises. And by 2020 forecasted revenues in AR will near $120B. Some of the important early verticals AR will start with will be healthcare, manufacturing, defense and architecture among others. Some of the early startups playing in these spaces, that I’ll be following in 2017 include: CrowdOptics, APX Labs and Pristine.

Reassessing account coverage models in B2B Sales

Early stage B2B SaaS companies, and tech companies more broadly speaking, are typically very focused on adding new accounts, growing existing accounts (up-sell and cross-sell) and serving their account base as best they can (customer success). These activities drive revenue growth, which in turn drives valuation, market credibility, ability to hire talent and a whole host of other important things.

While this focus is certainly well placed, in the process of focusing on revenue generating activities, many B2B SaaS companies forget to periodically assess their accounts from a profitability perspective and ensure that their sales coverage models are appropriately positioned given the varying profitability of the underlying accounts being served. The result is that many of these B2B companies end up relying too heavily on large direct sales forces when:

  • The profitability of the underlying customer accounts doesn’t justify the use of direct sales reps in all situations
  • There are often lower cost to serve models that are not only cheaper but also more effective with many types of customers.

As such, if you rely heavily on a large direct sales team, it can often be helpful to do a quick coverage alignment exercise to determine whether it is worth re-assessing your account coverage model. While this is primarily a cost-saving activity (e.g. moving some accounts from a higher cost direct model to a lower cost indirect model) there can many times also be revenue-generating opportunities as well because many customers actually prefer non-direct sales channels.

The first step to reassessing your account coverage model is to plot out the full distribution of all your customer accounts from a gross profit (not revenue) perspective. It is important to look at profitability and not revenue here as revenue alone will not help identify accounts that could be moved to different coverage models. Breaking out the distribution into deciles is often helpful in terms of bucketing accounts into different categories. Many B2B tech companies have a distribution that looks something like this:

Long Tail

As seen in the chart above, a minority of customer accounts are driving the vast majority of the gross profit. In this example, 30% of accounts are driving ~90% of the total GP. In most cases, it makes sense to continue to serve these high-value customers through a direct model. Losing these customers or serving them in a different way could be devastating.

That being said, there is also a long tail of low or even unprofitable accounts. For the most unprofitable accounts (e.g. very negative accounts), in some cases it may be better to simply stop serving these customers altogether because you are losing money on each one with no way to profitably serve the account.

For the “long tail” of customer accounts that are low profit or even slightly negative, this is where it is worth re-assessing your coverage model and thinking through other lower cost to serve options with the end goal of improving GP. There are at least 3 viable coverage options that could be used to improve the profitability of these accounts (and maybe even grow them).

(1) Inside sales: Inside sales reps come with less of the overhead and expenses that direct sales rep comes with. They have much lower (if any) travel, lodging and entertainments costs. Inside sales reps also tend to be cheaper and can have more flexible roles (i.e. as hunters, farmers, sales support, customer care, etc.)

(2) Channel: Indirect reps or channel partners can also be used to lower the cost to serve and improve account profitability. This go-to-market strategy can be more appealing to customers who are used to making purchases from a trusted channel partner. In addition to saving money on expensive direct sales reps, many of the same levers used to compensate direct sales reps (tiered commissions, accelerated payout above 100% quota attainment, etc.) can be used with indirect sales reps. If managed correctly, these compensation levers combined with the right channel partner could actually drive further top-line growth.

(3) Off-shoring sales support: Reducing the cost of sales support is another lever lowering the cost to serve. Sales support (e.g. admin functions, sales operations, product support, etc.) can often be handled in near-shore or far shore locations via virtual support at a fraction of the cost of on-shore resources. While this approach should be balanced with a need to serve customers well, many sales support activities (including some that are customer facing) can be handled in lower cost locations.

Bookings vs. Revenue in Early / Growth stage SaaS Companies

There is an important difference between revenue and bookings that comes into play for early stage SaaS businesses that are growing rapidly. This difference has implications on both the revenue and cost side of the equation and can also affect important decisions such as how much to raise when speaking with the investor community.

Before we get to growth SaaS businesses, however, let’s first talk about legacy software pricing. In the traditional software world, software was typically sold as a perpetual license. Customers would make a one-time payment for perpetual use of software and pay for annual support and upgrades each year. In this world, bookings = revenue and revenue was generally recognized at the time the contract was signed and the software provided to the customer. The benefit of this model was immediate revenue recognition; the downside was lumpy and unpredictable revenue.

In the SaaS world, software is provided on a recurring (often monthly) basis. The software, support and upgrades are all included in the subscription. This allows for stable and predictable revenues that are smoothed out over the life cycle of the contract. The down side, however, is that there is often a discrepancy between bookings and revenue that must be understood and accounted for appropriately.

This issue is best understood with an example. Assume for a moment that a SaaS business is selling software in 3 year contracts and that churn is 0% (for simplicity.) In a flat revenue business, where growth is 0% YoY, there are no major accounting issues or business implications because revenues = bookings. For example, if bookings are flat at $100M, the business would recognize $100M of revenue each year. $33M from 2 years ago, $33M from last year and $33M from this year.

The challenge that comes into play is when a SaaS business is in growth mode. For example, a SaaS business that books $25M in Y1, $50M in Y2 and $100M in Y3, would have the following revenue numbers:

Untitled.png

As seen in the table above, for a growth stage company that is doubling in bookings YoY, revenues do not equal bookings. There is a lag effect between bookings and revenue and it becomes necessary to take a haircut on bookings to get to revenue. This haircut will of course decrease over time as growth slows, but it does create an important accounting dynamic that has real business implications

One of the largest implications for early stage companies is in the amount of money a SaaS business must raise when going out to the VC market for financing. The underlying cost structure (COGS, sales expense, marketing expense, R&D, etc.) must be looked at as derived from bookings not revenue. This is a result of the fact that the SaaS business is incurring these expenses as a proportion of bookings not the revenue actually being recognized. Because expenses are incurred in line with bookings (and bookings > revenue), the result is that EBITDA is low (and generally negative for most early / growth stage SaaS companies). EBITDA ultimately ties to cash flow, burn rate and the required investment. Thus, it is key to make sure the raise is in line with what the business needs from a cash flow, and ultimately booking / expenses, perspective.