Bottom-up SaaS: A Model for Capital-Efficient Growth

It may be many months before we can fully assess the devastating human and economic impact of Covid-19. Against this uncertainty, most startups have reacted predictably by cutting expenses, shelving expansion plans, and reducing headcount; doing everything in their powers to conserve cash and extend their runway. As businesses across the board recast their operating plans to reflect a somber, uncertain future one thing is clear: we’ve transitioned to a new phase where the metrics for success will include not just growth but also capital efficiency and profitability. Economic downturns are, it seems, when we collectively rediscover the forgotten virtues of capital-efficient business models. Never mind that capital efficiency is not just desirable in tough times; it is desirable at all times. Capital efficiency gives a startup superpowers — the ability to do more with less; a way to bridge two seemingly conflicting objectives: maximizing growth while minimizing cash burn. The more capital efficient a startup’s business model, the better it is at satisfying both objectives. A traditional enterprise startup, however, starts off capital-inefficient:

• The minimum viable product (MVP) requires significant upfront investment; complex enterprise-class features require large teams and a multi-year R&D effort.
• The top-down go-to-market, built around direct sales and marketing, is expensive; and sales cycles are long.
• Serving the needs of demanding enterprises requires investing early in 24×7 customer support and professional services.

Only after the product is built out (R&D costs come down), and organizational and go-to-market scale efficiencies are realized, does the startup become capital-efficient. This can take years; until then the startup is burning cash and heavily reliant on outside capital to fund growth.

Some of the most exciting enterprise companies today are showing us a better way with business models that are capital efficient from Day One. They are doing this by approaching the market bottom-up: selling directly to users and bypassing the economic buyer (C-level execs) inside an enterprise.

A bottom-up enterprise startup is built around capital efficiency:

• The product emphasizes simplicity, ease-of-use and self-service; not feature complexity. Moreover, by prioritizing the needs of an individual user, the company postpones work on time consuming, heavyweight enterprise features. All this results in a quick MVP, developed by a small team on a shoestring budget.
• The go-to-market is powered by a low-cost customer acquisition funnel: a combination of in-product viral loops, content marketing, and word-of-mouth referrals.
• The company iterates simultaneously on both the product and business model — pulling in monetization concurrent with product usage.

For all these reasons, bottom-up SaaS businesses quickly get to an efficient frontier of high growth and low cash burn; some even get to profitability and become self-sustaining businesses.

Top-down SaaS businesses can also hit this efficient frontier but it takes them longer; the few that get there quickly have a rare combination of low R&D costs and very high price points to support their expensive go-to-market.

So far we have focused on the early days of a startup. How do these contrasting go-to-market strategies compare for businesses at scale? Do bottom-up companies retain their early advantage? Or do top-down businesses catch up as they unlock scale efficiencies?

To answer these questions, we curated a set of 38 leading VC-backed enterprise SaaS IPOs going back to 2012^[1]. We divided this set into two groups based on go-to-market: Top-down SaaS (N=21) and Bottom-up SaaS (N=17). (To avoid data snooping, we labeled each company’s go-to-market purely on qualitative criteria: product messaging, go-to-market and acquisition funnel characteristics from public S-1 filings; and no quantitative metrics: customer concentration, average deal size, price points, etc.)

First, a few high-level observations from the dataset:

1. Bottom-up SaaS companies have broad representation across verticals and sectors: Cybersecurity (Cloudflare), Closed-source infrastructure (Datadog), Finance (Smartsheet), Customer service (Zendesk), etc.
   • This challenges the conventional wisdom that bottom-up go-to-market strategies only work for products with intrinsic virality (e.g. collaboration) or community momentum (e.g. open-source).
2. A bottom-up go-to-market is by no means the only way to build a best-in-class enterprise software company
   • The best outliers in the Top-down group (Veeva Systems, Service Now) are comparable to the best outliers in the Bottom-up group (Atlassian, Zoom, Datadog).
3. The average bottom-up company is vastly more capital efficient than the average top-down company.
4. Bottom-up companies dominate the top-performance brackets across all efficiency metrics.

Now let’s take a closer look at the data:

• The median bottom-up company raised $70M (or 36%) less than the median top-down company ($122M vs $192M).
• The Bottom-up group outperformed the Top-down group across quartiles.
• The top performers in both groups had negative capital balances (i.e. the cash on their balance sheet was higher than the total capital they raised).
  • The best performing company in the dataset by far was Atlassian with a Net Raised of -$164M. Atlassian pioneered bottom-up selling and famously bootstrapped itself to an IPO; it continues to be an inspiration to all companies executing a bottom-up go-to-market.
  • The best performing top-down company was Veeva Systems with a Net Raised of -$45M. Veeva Systems is a remarkable story: the company went public with revenues of $136M having raised only $7M in venture capital; it is today a $29B market cap company.

• The median bottom-up company trades at a market cap of ~$7B, over 2x the median market cap of a top-down company ($3.2B).

We define capital efficiency as follows:

This metric is a rough proxy for how much a company spent for its revenue. It has the nice property of capturing the aggregate capital efficiency of a business from inception to IPO.

Some prefer to subtract cash from the denominator to adjust for companies that have significant cash balances but are otherwise capital efficient. We chose not to do this for two reasons:

1. Subtracting cash can lead to a negative denominator, making comparisons across the dataset impossible.
2. A large cash balance can point to sub-optimal capital allocation: the cash on the balance sheet is not being put to productive use. Our metric can be thought of as a blended measure of both capital efficiency and capital allocation.

• Bottom-up companies dominate the top-decile: Zoom, Datadog and Atlassian.
  • Veeva Systems was the only top-down company to make the cut.
• The median bottom-up company was 23% more capital efficient than the median top-down company – good enough to be in the upper quartile of the top-down group.
• There is high variance in the top-quartile band for both groups; the top-decile (Veeva, Zoom, Datadog and Atlassian) really separate themselves from the rest of the top-quartile, making over $2 for every dollar in capital raised.

The payback period measures how long it takes for a company to recoup its sales and marketing costs. It is a measure of sales efficiency but also capital efficiency: a company with a shorter payback period has higher operating cash flows with which to fund growth.

Bottom-up companies recoup their sales and marketing costs in a much shorter time.

• The median bottom-up company had a payback period of 17 months, 28% better than the median top-down company.
• Five companies (Veeva, Atlassian, Twilio, Zoom, Datadog) had payback periods less than 12 months; the only top-down company in the list was Veeva Systems.

Looking at Net Dollar Retention (NDR), the spread between the Top-down and Bottom-up groups is even more stark.  NDR is a measure of how much expansion from existing customers drives business; it is the closest thing a business gets to free revenue.

• The median bottom-up company had an incredible NDR of 135%; if it shuts off all sales and marketing, the business will still grow at 35%.
• The median top-down company had a NDR of 119% which puts it in the lowest quartile of bottom-up companies.

The bottom-up playbook relies on building grassroots momentum from small pockets of usage inside the enterprise. When this works well, expansion is a natural – almost automatic – consequence of more usage, more users and product upsells. The compounding effect of expansion is one of the reasons bottom-up companies can grow very fast with very little spend in sales and marketing – and are ultimately so capital-efficient. This chart from Atlassian’s S-1 makes the point powerfully:

“…our customers have spent over seven times their initial purchase over the subsequent five years through renewals, more products and teams.“

Overall, it is clear from the data: bottom-up SaaS companies are significantly more capital efficient than top-down SaaS companies; we see bottom-up outperform top-down across all metrics of capital efficiency:

Looking forward, we can expect two trends:

First, bottom-up SaaS will become the default way of building an enterprise startup. There is enough evidence that the model has full-stack applicability from apps to infrastructure. It is not only a more capital efficient path but also a better path for strong, product-centric founders.

Second, we will see more startups, powered by incredibly capital-efficient growth engines, skip traditional venture rounds and go straight from seed to growth (or even IPO). This won’t be a path for all but will for an increasing few. What was considered an impossibility before Atlassian is today just an outlier (1Password, Notion, Webflow, Zapier, etc.); soon it will be in the realm of hard but doable. It will be interesting to watch how this changes the venture capital and seed landscape, especially if these ‘few’ companies are the ticket to outsized returns.

[1] Dataset and related code can be found at: https://github.com/ra2w/B2B_SaaS_IPO_Analysis. Excluded from our dataset were non-subscription businesses; SMB and PE backed companies