Some confusion after hours in Snowflake. For some extra commentary on the Snowflake RPO figure (lots of talk about it this quarter), I'd point people to the pre-earnings research note Karl Keirstead at UBS published a few days ago. Screenshot below: $SNOW
To try and explain this further - RPO measures the aggregate total unrecognized contract value from all customers. Let's say a company had one customer that signed a $100k one year deal. After 6 months, the RPO would be $50k. this represents the remainder of the deal, or 6 months
Let's say instead that the same customer signed a $1M ten year TCV deal. The ACV is the same ($100k / year). However, after 6 months, this one customer would have a RPO of $950k (95% left on the 10 year deal, or 114 months of a 120 month deal)
The point here is that same customer who will pay $100k / year has a very different RPO based on the length of the contract. And this is an important factor of RPO - it doesn't take into account contract length.
Sometimes companies disclose a one year RPO figure (RPO looking only 1 year out). I think this is a better metric to track as it normalizes RPO for contract lengths more than RPO in a vacuum. Especially for companies going through a mix shift in contract lengths
So why is this relevant for Snowflake? As the research note points out in Q2 last year Snowflake started offering SPIFS (think of these as extra incentives for sales reps) to close multi year deals instead of single year deals - mini "bonuses" for closing multi year deals
As you can imagine, if reps get paid more for closing multi-year deals vs one year deals, the percentage of multi-year deals will go up!
This means that starting in Q2 last year the RPO started to go WAY up. The historical RPO from prior quarters primarily consisted of 1 year deals, while the RPO going forward would include a higher percentage of multi year deals.
As I mentioned earlier, multi year deals will greatly increase the RPO. So the growth in RPO was kind of comparing apples and oranges. One period had many multi year deals, while the historical periods didn't. So the RPO growth looked incredibly high for the last 4 quarters
However, this quarter was the first quarter where Snowflake "lapped" the quarter from last year where the sales reps were incentivized heavily for selling multi year deals. The RPO this quarter was more of an apples to apples comparison
So the deceleration in RPO is really driven by the following:
Q1 (last quarter) RPO compared a quarter with heavier percentage of multi year deals to a period without
Q2 (this quarter) RPO compared two quarters with a similar mix shift of multi year deals
This is all a bit of an oversimplification, however I think it's important to lay out. There was lots of confusion in the after-hours price movement of Snowflake.
I think it's easy to look at RPO decelerating from 206% last Q to 122% and get worried.
However, this really isn't a fundamental business issue. It's really just a quark in how RPO is calculated, driven by a mix shift in multi-year deals due to sales rep compensation
A bit of an unorganized ramble, but hopefully this make sense!
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Asana and Monday are two fascinating businesses. I remember looking at private rounds many years ago and thinking "this is a commodity space, there won't be any big outcomes with a long tail of small / medium outcomes."
Both are ~$15B companies today. Software markets are huge!
Both have executed extremely well and this shouldn't be understated. What's been incredibly impressive is the growth in the enterprise segments. Both define enterprise as customers with >$50k ACV. Asana grew that segment 92%. Monday grew it 226%. Way more than just a SMB business
Both businesses have net dollar retention >110%. This might not sound good in a vacuum, but it's incredibly impressive for businesses with a large base of SMB customers. Asana ACVs are ~$3k. Monday ACVs are ~$2k.
The acceleration across cloud software companies so far in Q2 has been very impressive. As of now, most companies with a June quarter end have reported Q2. Most accelerated. Graph below shows (Q2 YoY growth - Q1 YoY growth). Positive numbers represent acceleration (faster growth)
I removed 3 companies - Shopify, Olo and BigCommerce. All were major Covid beneficiaries. Q1 this year lapped a 2020 Q1 that did not see much Covid benefit, while Q2 lapped a 2020 Q2 which did see a Covid benefit. So the YoY compare between Q2'21 to Q2'21 isn't as relevant
If your curious where each would show up on the graph:
Shopify: (43%): 110% growth in Q1, 67% growth in Q2
Olo: (96%): 144% growth in Q1, 48% growth in Q2
BigCommerce: (6%): 41% growth in Q1, 35% growth in Q2
Some great slides from the Crowdstrike investor briefing last week. A few call outs on what makes them such a great business, that others should aspire to:
1. TAM Expansion: The best businesses increase the size of the pie, not just their piece
2. Product Expansion Velocity: At IPO (in 2019) Crowdstrike had 10 modules. They now have 19. Amazing product development velocity
3. Upsell / Cross-Sell: Customers are using more and more Crowdstrike products. Very powerful platform flywheel
I always enjoy reading Bessemer's annual State of the Cloud report. One of my favorite slides below. The takeaway? Leading cloud companies don't decelerate growth nearly as quickly as they're expected to. Why? Cloud markets are almost always much bigger than anticipated
Analysts constantly underestimate leading cloud companies ability to sustain higher growth rates for longer periods of time
I also liked the metric of Growth Endurance they discussed. This is defined as current year growth / last year growth. Nearly 1/3 of cloud companies accelerate growth! (growth endurance > 100%). Data points include every BVP Nasdaq Cloud company over last decade
A trend I'm excited for this year: DataOps & the Analytical Engineer
~10 years ago DevOps was born. The role of system admins and developers merged. Infrastructure became self-serve
Today the role of data engineers and business analysts are merging. Data is becoming self-serve
Data infrastructure is becoming so powerful that the tools today allow non-technical folks to carry out the once complicated / custom code/ huge backlog jobs of data engineers.
Before getting into what this means, let's first discuss how we got here
Before 2012 the data world was dominated by transactional (OLTP) databases like PostgreSQL, MySQL, etc and analytical (OLAP) databases like Oracle, Netezza
Tools like Informatica / Talend were used to batch load (ETL) data into these databases, Tableau used to visualize
My biggest takeaway from Q3 cloud earnings? We REALLY saw cloud businesses ACCELEERATE. Since Covid began we heard anecdotal data of "digital transformations accelerating." But the data was never there. It is now. Data below shows the absolute change in rev growth % from Q3 to Q2
For further clarification - the graph shows the delta between Q3 YoY growth rate and Q2 YoY growth rate (I tried to normalize for acquisitions where I could, sure I missed some). As an example - Zoom grew 367% in Q3 and 355% in Q2, so the delta, 12%, is graphed.
I'm defining "accelerating" as YoY rev growth that is increasing on an absolute basis. And as you can see, there are plenty of businesses who accelerated this quarter