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CPaaS earnings season kicks off in a week, and even temperamental optimists like myself are pensive. This marks the first de-risked year—the first year-over-year comparisons free from the pandemic-fueled growth and subsequent post-pandemic cleanup. Everyone will be on the lookout for early (really early) signals that the strategic bets these companies have made will pay off.
Everyone Loves a Salesforce Comparison
During its pre-IPO roadshow, Rubrik CEO Bipul Sinha compared his company to Salesforce. The comparison was to demonstrate ambition as much to display Rubrik’s transformative power in the cybersecurity industry. It is also a testament to the near-mythic status Salesforce occupies.
But what if we put a different twist on the comparison? What if we compared Salesforce’s pre and post IPO years (circa 2004) to the pre and post IPO years of the companies we follow? Would that be a fair comparison?
It turns out that after adjusting for inflation (all revenues adjusted to March 2024 CPI) and controlling for currency (USD), the cohort performs well when compared to Salesforce during the same period in their lifecycle.
(1) All revenue figures are in USD. (2) Numbers are inflation adjusted to March 2024
Some might point out the differences between historical time and chronological time, arguing that there is no way to control for market variables in 2004 versus 2024. This is true. When Salesforce IPO’d the SaaS market was still in its infancy. Software that didn’t come in packs of CDs was novel and untested. Today CDs are relics.
However, even though the CPaaS industry may have their TAM/SAM/SOM better defined than Salesforce did at its inception, this doesn’t mean the growth challenges facing this cohort are any less daunting. They still had to find a prospect, convert them into a customer, and do so faster and better than their competitors. Moreover, they had to publicly report their progress. That’s hard work in any market.
Sidebar: How Subscriptions Almost Killed SaaS
A poorly implemented subscription model almost killed SaaS. In typical tech fashion, first it was new, then everyone fell in love with the idea, and then everyone en-masse copied the idea without doing the underlying work, and then that indiscriminate adoption almost killed the business model.
Here’s how it happened:
- Investors, customers, and companies all loved the predictability of a subscription.
- The companies pushed customers onto a subscription.
- Thanks to autopay/autocharge, Terms of Service (ToS), and accounting rules, payment and revenue recognition became automatic.
- Over time, a drift developed between what the customer wanted and the subscription offering.
- The customer was unaware and unbothered by decreasing usage. The subscription simply appeared as a line item on a credit card bill. Unlike in the physical world, where an unused product sits like an eyesore in a corner of the house until you address it, a SaaS subscription was out of sight and out of mind.
- The business remained blissfully unaware because it didn’t track underlying product usage and was lulled into a false sense of security by the inflated margins resulting from spoilage.
- When customers eventually churned, it came as a surprise to the business, often when it was already too late to retain them.
The best way to prevent this is to track product usage.
I wrote at length about this in my Openview contribution on how to use Gross Margin to check product use, and also how NRR/GRR are a good way to keep track of healthy revenue.
However, these are all revenue-based metrics. Every SaaS platform should also track a non-financial leading indicator of usage that, when it falls below or exceeds a certain threshold, should trigger a call to action. In the CPaaS industry, this is typically measured by the number of texts or calls sent or received. It doesn’t matter whether customers are on a monthly, quarterly, or annual plan. If they’re not sending, they’re likely not renewing.
Finally
Temperamentally optimistic people have an optimism bias. If stranded in the middle of a lake and with only doggie-paddle swimming skills, they believe they can still make it even if the better option would be to wait for help. They need strong data, adversarial collaborators, and good process to stay grounded.
In the coming weeks, I will therefore be looking for data that may dilute my optimism, running my conclusions by a few of you (thank you!) and patiently reviewing my thesis to minimize intellectual shortcuts. Sign up for my newsletter for a play-by-play delivered in your inbox.