A few sections from the SNOW earnings call transcript that caught my attention.
For me, the investing thesis for SNOW is:
- massive total addressable market and associated potential for durable growth,
- driven by the growth of data, analytics and data science, and
- the rise of data-driven decision-making, including automation
SNOW Q2 2023 Earnings Call Highlights
Frank Slootman - CEO
Data science, machine learning, and AI use cases on Snowflake are growing every day. In Q1, more than 1,500 customers leverage Snowflake for one of these workloads, up 91% year over year. A large U.S. financial institution uses Snowflake for model training, facing memory constraints with their prior solution.
Interpretation: Machine learning can require a lot of data that needs to be stored somewhere like a database. Snowflake provides this storage.
Mike Scarpelli - CFO
We saw in-line performance globally with the exception of our SMB and APJ segments.
…
From a booking standpoint, we saw headwinds globally, with the exception of our North American large enterprise segment.
Interpretation: As funding costs have risen, small company and startup business continues to be slow. These would be cloud-native customers and a percentage of these would be natural Snowflake customers for their data storage. In contrast, there remains a large number of larger, older companies running legacy workloads that are in the process of huge efforts to migrate to cloud solutions, including storage on Snowflake.
Mike Scarpelli - CFO
From a booking standpoint, we saw headwinds globally,… This is not due to competitive pressures, but because customers remain hesitant to sign large multi-year deals.
Frank Slootman - CEO
On the sales productivity side, I do think that’s very much a macro thing…This is definitely a situation where I feel like we have applied tremendous amounts of resources. We’ve been very, very successful at it…I definitely think it’s a macro thing.
[Tight IT budgets right now because of]…general anxiety that exists in the economy. …
These things will run their course… There’s plenty of demand out there, absolutely.
Interpretation: Macro conditions are causing customers to be cautious, but there is no lack of competency in the sales organization.
Mike Scarpelli - CFO
…we have seen slower-than-expected revenue growth since Easter. Contrary to last quarter, the majority of this underperformance is driven by older customers.
We are still focused on investing in efficient growth with a concentration on continuing to sign new customers…
Interpretation: Older customers are optimizing, and Snowflake is focusing their sales organization more on landing new customers versus expanding existing customers.
Mike Scarpelli - CFO
So, in terms of what customers are doing, actually, the number of jobs – the number of queries actually grew 57% year over year in the quarter. It’s outpacing our revenue. The queries are just running more efficiently and that is because of some of the optimizations, both – you reduce the amount of storage you’re running queries on, they run faster. It’s also the impact you’re getting right now of the full Graviton2 this year versus the core from last year. …the number of jobs is actually – growth is actually outpacing revenue and just we’re becoming so much more efficient for our customers.
Interpretation: This is an interesting metric if it truly correlates with overall customer activity on Snowflake. If they report this going forward, then it can be compared with revenue growth to identify if it is a meaningful measure of customer activity.
Frank Slootman - CEO
[To run machine learning and AI]… But the thing is you need to have highly curated, highly optimized data…You cannot just indiscriminately let these things loose on data that is – that people don’t understand in terms of [the data’s] quality, and its definition, its lineage, and all these kinds of things.
Interpretation: Raw data needs to be processed in several ways before it can be used by a machine learning model. Data needs to be cleaned, formatted, standardized and transformed in a variety of ways, this includes methods described as preprocessing and feature engineering. Data that is transformed at these different stages needs to be stored and Snowflake can store these data, in addition to the raw data and model predictions.
Frank Slootman - CEO
…Microsoft relationship has been growing faster than the other two cloud platforms… I think Azure will continue to grow and grow faster than the other platforms.
Interpretation: Many, many enterprises are Microsoft customers and these enterprises are or will be eventually moving to the cloud, and many will choose Microsoft’s Azure as their cloud provider and some of these customers will choose Snowflake for data storage.
Mike Scarpelli - CFO
…we have a lot of customers. We have only moved a fraction of their data that we know they have multiyear plans to go on Snowflake…
Interpretation: This is a big part of the durability thesis. There is a ton of data in legacy databases that needs to move to the cloud and Snowflake will capture some of this business. In many, many cases, it is not a simple or fast task to migrate data from one storage solution to another. After a customer onboards to Snowflake, it can be a long journey to transfer all workloads from legacy systems to Snowflake.
This is my overall interpretation of the Snowflake slowdown.
It’s macro, and the sales org is as functional as can be under this CEO. More specifically, new workloads from smaller growth companies have cooled way down as the funding environment has tightened over the last 12 months. The large pool of more established companies looking to migrate data from legacy systems to the cloud hasn’t gone anywhere, they’ve just grown cautious with the uncertain macro environment. Finally, Snowflake revenue grows with a considerable lag from when a customer signs up because migrating data from one system to another can be a complex and time-consuming task and revenue is consumption based (I think this has been mentioned on prior earnings calls).
I’m long msft, snow, net, ttd