Optimizing the SaaS LTV/CAC ratio

While many SaaS startups grope for the right size of customer to target, others find a sweet spot in the customer size spectrum where the LTV/CAC ratio is optimized. I’ve discussed in the past that LTV and CAC can be deceiving and muddling, however they can also offer a handy framework for optimizing go-to-market – to whom and how you sell.

After the boom (and bust?) of freemium models in the early 2010s, there is a growing recognition that the long-tail small business market is hard to serve profitably. Low CAC simply doesn’t make up for low Annual/Average Contract Value (ACV) coupled with high customer churn. Average customer churn is extraordinarily high among small employee count customers:

Pic 1

*Derived from underlying data of median $ churn as a function of ACV size from 2014 Pacific Crest SaaS Survey. Customer employee number estimated based on ACV size. Churn values adjusted based on average-to-median ratio and $-to-unit churn ratio for churn distribution in survey. Email me if you want the details.

Many startups are realizing they need to fire these long tail customers and go upstream. As they shift up-market from online sales/self-serve to inside sales to field sales, however, they face a new headwind – selling costs (CAC). CAC goes up with more complicated sales processes:

Pic 2

Source: 2014 Pacific Crest SaaS Survey. Data derived from CAC distributions.

Do the churn sea anchor and CAC headwinds balance at an optimal customer size? With a bit of math and Pacific Crest data, we can see that they do… at least on average:

 Pic 3

Chart based on the 2014 Pacific Crest data and a few simple formulae and assumptions. Namely: (1) LTV = (ACV*80% margin)/Churn (2) CAC for online, inside, and field sales are as shown in the bar chart and (3) employee counts correlate to ACV as in the chart above.

We see one intuitive result: selling to tiny companies tends to minimize profitability. We also a see a slight optimal peak selling software via inside sales to companies with a few hundred employees and ACVs in the $10Ks. Pretty cool.  This curve is an average for the companies in the Pacific Crest Survey. You’ll notice that the derived LTV/CAC ratios in the 4 to 10 range are high relative to the 3x “golden rule”, likely because of survivorship bias in the survey pool. For any specific startup, the shape of this curve is a function of whom it sells into (sales, finance, etc), structure of market, complexity of product, etc. How can you bend the curve?

Well, what if your product is so well marketed, easy to use and easy to implement that companies in the 100s of people can self-serve and buy online? If we change inside sales costs to online sales costs for 50 and 100 person customers, we get:

Pic 4

Wow. Now we know why Slack is “killing it”. From the outside, Slack appears to be a very recent example of a startup with a small and mid-size customer self-serve sweet spot. Unfortunately, self-serve is much harder with more complicated (or more poorly packaged and marketed) software, requiring many startups to use inside sales even for small customers and precluding a sweet spot. Ideally in the early stages of a startup, you can test different customer sizes and get some systematic (or at least anecdotal) data on how your startup’s curve looks and if there is an LTV/CAC sweet spot.

If there is a sweet spot, should you go after it and ignore bigger or smaller customers? Not necessarily:

Reasons to go larger: Many startups choose to pursue enterprise customers even if their LTV/CAC ratio is lower than when selling to small or mid-size companies. This is because enterprise customers are more valuable to acquirers. Acquirers greatly value enterprise customers because acquirers may be able to sell large contracts of other product lines to them. Such “strategic” customer value could mean a 10x revenue exit multiple instead of a 5x multiple – a difference that can easily offset a lower LTV/CAC ratio and having to take more funding and dilution while scaling a startup.

Reasons to go smaller: At the early stages of a startup, surviving means raising money or revenue, and either requires getting traction quickly. Small customers are faster and easier to onboard and may be the fastest way to show product/market fit and revenue, even if those customers aren’t optimally cash efficient. It is quite common to see startups start with small customers and go upstream later for this and other reasons.

5 thoughts on “Optimizing the SaaS LTV/CAC ratio

  1. Thanks for the deeper dive into two of the key SaaS business metrics. It’s important to understand how various metrics serve as “levers” for your performance. A while back I came up with a way of visualizing the interactions between the 4 mostly commonly tracked SaaS metrics (CAC, payback, LTV, churn). You can see it here: http://wp.me/p2EfeJ-jG

  2. Interesting post. In my last business we found that onboarding larger customers could take 2-3x as long as small/mid sized ones (10-100ish employees) and also that the relationship duration was shorter. The larger customers had purchasing functions that were often sensitive to price as well as vendor size (we were small). Since we had a strict margin discipline, the relationship created by collaborating with an internal engineer could carry the day in a smaller company but didn’t mean much to a larger company with a purchasing function that was being rated on its ability (in part) to get cost out.

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