Looking at the share price reaction for SaaS businesses the day after reporting Q2 earnings makes it appear like most stocks fell (the median stock fell 2.5%). However if you look at how the share price after earnings compared to 2 weeks prior it paints a different picture! Data:
This is particularly true for businesses with July Q ends who saw big run ups in share price in the couple days leading up to earnings, then big drops right after. Fastly (June Q end) was the epitome of this. Down 19% the day after earnings, but up 17% compared to 2 weeks prior!
Here's the data for the day after share price reaction for comparison:
The percentages shown are market adjusted. So if the market was up 1% after earnings, and the stock was up 3%, the market adjusted reaction would be 2%
h/t @TheRealCarlChi1 for the motivation to pull this data with a tweet from earlier this week
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After Q3 '22 Snowflake guided to 47% growth in 2023. Yesterday they dropped that guide to 40%. What was the cause? 3 things primarily (in ascending order of impact)
1. Change in existing customer purchasing behavior 2. Weaker new logo bookings 3. Slower ramp from newer customers
1. Change in existing customer purchasing behavior
Snowflake sells annual / multi year deals that come with minimum consumption spend. Once customers reach those committed spend levels, they have the choice to re-up with another multi-year commitment. Usually, they do...
...however, this quarter Snowflake saw more of these customers buy just enough capacity to get them through the next few months, nothing more.
Important to remember - the bigger impact of this is on bookings (more on this later), not revenue. Customers are still consuming...
Positives
- Strongest new logo quarter
- Gross churn very low
- Healthy pipeline generation
Negatives
- More optimizations in Q4 than Q2 / Q3
- Things got worse in December
- Weak 2023 guide
$DDOG
The best summary quote from the earnings call was "Overall, we observed slower usage growth with existing customers while continuing to scale our new logo acquisition and new product cross-sells."
The tricky balance is the commentary around the record new logo quarter + strong pipeline generation with weaker near term optimization headwinds
Some quotes:
"we had our strongest new logo quarter to date, with a record level of new logo ARR bookings"
Investing in infrastructure businesses, particularly data infrastructure, has been incredibly exciting over the last few years (and will continue to be!). A couple data infra stacks Im excited about
1) Analytics stack 2) Real time stack 3) Data Lake stack 4) AI stack
More below
1. The Analytics Stack (or Modern Data Stack). Typically consists of: 1) Data Source (transactional database, SaaS app, etc) 2) ELT tool (Fivetran, Airbyte, etc) 3) Data Warehouse (Snowflake, Redshift, etc) 4) Data Transformation tool (dbt) 5) BI (Looker, Sigma, etc)
We hear a lot about this stack. Budgets are massive for it, and many companies have hit escape velocity building different layers of the stack. How each vendor evolves will be interesting to watch. There are also newer parts of this stack emerging (Reverse ETL, Data quality, etc
Consumption models under fire this year given easier to optimize (reduce) spend quickly. This pain happens quick.
However, everything I'm hearing pointing to new customer signups posing the biggest challenges currently, and expansion / churn seeing relatively less pressure...
...Models more reliant on new customer signups for growth feeling more pain now, vs consumption models felt it earlier in the year.
What do I think of this? 2023 might be the year consumption models actually show more resiliency (less growth decay)...
...If more of your growth is coming from scaling existing customers vs signing up new ones, it'll be easier next year when the burden of getting through procurement / legal for a new customer signup gets tougher.
Of course, it's never purely a business model question...
Lots of interesting announcements from AWS at re:Invent today. Some new products, and some product enhancements. Data and Security are clearly top of mind for them. Some of my favorites below:
AWS Security Lake: A central data lake purpose built to store and analyze security data (primarily log data)