And the losing streak continues!! I may as well become a Cubs fan. The losses ARE getting smaller.
Another month of BIG swings!! The first half or so of the month were very painful on my portfolio but it came storming back the second half of the month. I was GREEN for the month until the last two days. I still ended the month red for the 5th consecutive month now. However, my companies are looking better than ever!!
One perspective that I really like is that while valuations in my positions have been hammered, the BUSINESS STRENGTH of each of them has gone WAY up. Consider this… Using Snowflake for this example because it is easier for me to do this math in my head… Snowflake continuing to grow triple digits while its stock price gets hammered. What if it takes 6 more months to get back to its all-time high? The BUSINESS has DOUBLED since it started getting hammered. What does this mean? This means to get back to its ALL TIME HIGH, the P/S ratio only needs to be HALF what it was at beginning of November. This is the magic of HYPERGROWTH investing. What appears expensive becomes CHEAP very quickly. Follow the long-term BUSINESS and enjoy the ride (or in the case of the last few months, tears, tears and more tears).
March February January Data Dog 20% 25% 24% Snowflake 20% 22% 22% ZScaler 20% 19% 19% MongoDB 12% 5% NEW SentinelOne 12% 12% 15% Cloudflare 12% 11% 3% Monday 5% 4% 14%
I did make one big portfolio modification other than my monthly contributions in March. It was at the very beginning of the month. In February, I had taken a 5% position in MDB. This month, I trimmed some of my biggest holdings (mostly DDOG) that got them more inline with a 20%-ish max and put all that into MDB. MDB is now what I consider a full position (>10% in FF terms) but I still consider it a bit low in terms of my confidence in it. I am a bit uneasy putting more confidence into it since I just entered but I have been in and out of it before. Also, I’ve been really chomping at the bit to add it to my portfolio for a while!!
Why did I want to add MDB? Well, besides continuing to accelerate revenue, it is dominant in its field (non-SQL transactional databases). The hypergrowers have lessened emphasis on competing products, like SNOW, and are now HELPING them out!! As discussed this month, it is not ideal for many worksets; however, being that it is CLOSER to the edge than other DBs, this gives them a HUUUUUUUGE advantage for what they do well, which is capture lots of data and scale horizontally (across datacenters). It is still a pin to REPORT on this data, especially analytical reporting, but with the rise of SNOWFLAKE and other easier to use reporting infrastructures, it is getting easier and easier to mitigate this issue. One thing I didn’t mention before is that while there is a HUUUUUUGE set of data its not ideal for, a very large subset of these applications CAN work just fine in MongoDB, even if it isn’t the ideal platform. In my opinion, it is very likely that we will see more and more traditional types of applications move to Mongo simply due to developer preference and not really suffer many ill effects from not being the ideal solution.
One more thing about them I really liked and hadn’t considered until ExponentialDave’s video overview of Mongo is that DBNRR is likely being HELD BACK by the non-Atlas portion of the business. I wonder what the Atlas DNBRR is. Total DBNRR is 129. Is it possible Atlas DBNRR is over 150? Is it possible this is the SECOND BEST DBNRR of companies we track, only behind Snowflake’s 181 or whatever video game number they have. With their continued acceleration, I’m excited for the future as Atlas continues to be bigger and bigger.
In any case, the numbers very much support Mongo going forward.
Not going to cover all of them due to how great the coverage is by so many others, just gonna add my 2 cents on a couple.
My MongoDB is basically in the previous section. Snowflake & Sentinel One are the other two of my positions that reported this quarter.
All the future growth numbers are still clicking at a doubling YoY rate. Snowflake improvements will make it look a bit less than that for the next Q or two but it should re-accelerate (or stabilize) after that. Snowflake and MongoDB are particularly well positioned to take advantage of the DATA EXPLOSION that is happening and will continue to happen. I think this data trend is the biggest tailwind in tech right now. Security coming in a close second. If Snowflake even comes close to mimic-ing the hyperscaler growth without other business affecting them (AWS, Azure), then it will make its owners very wealthy. Its very exciting to see AWS come around with Snowflake, Mongo & Datadog. All in prime spots.
SNOWFLAKE has won the cloud data warehouse wars. They are the UNDISPUTED leader. Amazon’s recent dealings with them are the proof. Tuesday’s release that Amazon retail customers can get their data thru Snowflake (using the retail cloud) is further evidence. I mean… SNOWFLAKE RETAIL CLOUD HAS AMAZON, THE BIGGEST RETAILER, IN THEIR DATA SHARING!! This is HUUUUUUGE news.
One thing I’d like to see thru their partnerships is something similar to what Azure and MongoDB are doing internally. Azure has Synapse Link & Mongo has something similar that basically automatically creates a copy of transactional database data in Azure Synapse (Snowflake competitor) for reporting purposes. Of course, this data will need to be massaged for blending and bigger analysis purposes but already having it in Synapse is a big benefit. Mongo does the same but just within another Mongo space. I’d like to see Snowflake partner with Mongo, Azure, AWS to be an option for this link stuff. IMO this would greatly reduce one of the biggest hurdles of using proper data analysis tools, the ETL process.
Security being another HUGE tailwind, and Sentinel One showing as a smaller, faster growing version of another favorite but no longer a holding of mine, Crowdstrike. The quarter came in a tad weaker than I had hoped but they gave very strong guidance that indicates that its going to continue growing at triple digits for several more quarters.
I want to underscore a post I wrote on March 9 to help people understand the differences between the consumption model differences between Snowflake, MongoDB, Datadog & Bill.com. I think it is important to understand the differences so we know why different things affect the performance on them. When I was listening to the MongoDB call, I just couldn’t believe that someone who is PAID to understand how companies work, would ask questions along the lines of assuming the consumption models are the same. Maybe I need to listen to more and lower my expectations of what analysts are paid to do…
For the most part, the Datadog consumption model is based on number of devices (computers, switches) monitored as well as the number of metrics monitored on those devices. For example, a company has many servers running supporting various applications and such. For each server, some of the metrics they are monitoring are things like uptime (is the device running), CPU usage, Memory usage, Disk usage, connectivity issues…
While this is technically a consumption based model, these things do NOT fluctuate very much. Think of it like a company’s Office 365 subscriptions. A company rents Office for each of its employees. When they add/lose employees, it’ll fluctuate but that’s really it. Its very stable, a lot closer to a strict SaaS model.
MongoDB charges based on consumption as well but it is based upon how much hardware is provisioned & how many hours it runs (it might be down to the minute or second). This too is very stable, however, if a month has 28 days in it instead of 31, then revenue for that month will be less by around 10%. One way companies like to save money on this model is to take applications they only need during business hours and shut them down when not needed. This cuts from 24 hrs/day to 8 or 10.
MongoDB is working on a Serverless option. They really have no choice as the hyperscalers already offer this in their competing platforms (both NOSQL & SQL). This will add more variability to the revenue but not as drastic as Snowflake. In general, serverless automatically scales up & down based on the workload being presented. I use an Azure SQL Database Serverless for managing my portfolio & Earnings Reports. It automatically shuts down after an hour of no activity, which saves me a lot of money.
Snowflake bills on the consumption of CPU resources all the way down to the individual query basis. Snowflake said on the call that 70% of queries run on their system are automated & 30% are human generated. They also said the automated are very steady but the human generated ones are not. This means things like extra holidays or a pandemic where more people are sick during a quarter or something can have an impact on revenue because those workloads are not running as they usually do.
I don’t follow this one as closely and I’m a data professional so the previous three are things I understand very well. However, this means my understanding of bill.com’s consumption model might be inaccurate so feel free to correct me.
bill.com generates money from two methods. The first is the straight SaaS model. This is very steady but a smaller set of overall revenue. The real money comes in via per transaction. The more companies using bill.com (or thru partners that whitelabel them) the better but its likely better to have companies with say, a lot of travel and other expenses. It is a real flywheel because they can add vendors & customers and make it easier and easier…
Anyway, the variability in this one seems the least stead in my opinion. If travel shuts down or we hit a recession, companies may cut some of their spending and therefore the revenue bill.com gets might not grow as well.