2020 is the decade of data. Look no further than the massive companies pushing the public & the private market forward: Snowflake, Databricks, Amazon, Azure, Google Cloud. It’s quite possible that data products have created more market cap than any other subsegment of SaaS in the last five years.
On October 25th, I’ll share my 10 predictions for data in 2023 at The Impact Data Summit.
Imagine a startup with 4 customers in the pipeline. The average sales cycle is 28 days. Two of those customers should close this quarter. Two of them, who entered the funnel later in the month, will take longer than 30 days to close.
If the ACV of the company is $25k, then the business should project $50k in bookings this period & $50k next period (assuming no additional pipeline materializes).
The number of vendors selling to sales and marketing has exploded from 500 to more than 3000 over the last three years. Are we reaching the end of an expansionary cycle? The software pendulum tends to swing between software suites, offering a collection of different tools, and best-of-breed point solutions.
But, have we reached the point where the best-of-breed, fragmented ecosystem is a permanent fixture? Okta’s Businesses at Work 2016 report calculates most enterprises pay for somewhere between 10-15 corporate applications. Those are the ones sanctioned by IT. How about the others?
At the inaugural DuneCon, Dune’s first conference, I shared a presentation on the state of crypto in 2022.
I aimed to characterize the health of the crypto ecosystem at the most basic level. For example, quantifying the number of active wallets, the population of active developers, & other dynamics within the ecosystem.
The slides are embedded here with summary commentary below and linked here.
Overall, the crypto ecosystem finds itself in a winter. I see it as the coiling of a spring. So much innovation has been unleashed in the last few years, most of us are still absorbing the implications & working to identify the best applications of the 4 fundamental innovations of web3.
Today, Adobe announced its intention to acquire Figma for $20b, valuing the business at 50x current ARR, the highest multiple paid for any software company of scale. Congratulations to team Figma on building their impressive business.
Doesn’t this acquisition reset the market price despite this year’s 70% correction?
After the correction earlier this year, public valuation multiples had reset to those of 2017. That year, Cisco acquired AppDynamics for 17x trailing revenue. If we assume a company recognizes about 2/3 of its ARR as revenue, then I estimate the Adobe/Figma deal at roughly 75x trailing revenue.
Ten years ago today, I wrote a post titled “Choosing market segments on customer profitability.” How healthy are the business’s underling customers?
Today, that question matters more because of the recession than at any point in the last decade.
Selling to very profitable customers benefits a startup. Account executives can charge a highly profitable company more than one without much money. I compared the profit margins of selling to a grocery store, a restaurant & a software company to illustrate the point.
This chart holds the secret to successful hiring of go-to-market teams.
What is it? A stack rank of attributes within HubSpot account executive candidates that correlate to quota attainment. Preparation, adaptability, domain experience, & intelligence topped the list. Closing ability correlated negatively. The pushier the account executive, the worse the result. That result alone should encourage rethinking of AE interviewing.
By my rough estimation, there are approximately 30m monthly active web3 users (getting closer to that 100m user mark!) The activity rates of those users mirror web2 finance apps.
MixPanel recorded the activity rate of web2 finance apps at 10.7% DAU/MAU. In other words, 11% of users who use the app every month, also use it daily.
Dune Analytics provides insight into the DAU / MAU of Ethereum users for August.
During Bill Binch’s Office Hours, he predicted many more software startups would achieve 200% net dollar retention. In other words, the average customer’s spend would double each year. I’ve only seen a handful of companies achieve that mark. The most notable public company is Snowflake which routinely reports NDR of 171%.
Large NDR figures are hugely beneficial to startups. They imply strong product market fit. They provide predictable revenue growth. They result in better capital efficiency, an important metric in this environment.
This chart shows the relationship between the top 100 public projects’ revenue and their trailing revenue multiple. There’s none. The correlation asymptotes to zero. At least when looking at the ecosystem as a whole.
But let’s break the data down by category into the top 5 by revenue: L1s (blockchains), DEXs (decentralized exchanges), Credit (lenders), NFT Marketplaces (buy & sell Bored Apes), & Yield Aggregators (systems to maximize interest rates on deposits).