from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante earnings season has shown a conflicting mix of signals for software companies well virtually all firms are expressing caution over so-called macro headwinds we're talking about ukraine inflation interest rates europe fx headwinds supply chain just overall i.t spend mongodb along with a few other names appeared more sanguine thanks to a beat in the recent quarter and a cautious but upbeat outlook for the near term hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis ahead of mongodb world 2022 we drill into mongo's business and what etr survey data tells us in the context of overall demand and the patterns that we're seeing from other software companies and we're seeing some distinctly different results from major firms these days we'll talk more about [ __ ] in this session which beat eps by 30 cents in revenue by more than 18 million dollars salesforce had a great quarter and its diversified portfolio is paying off as seen by the stocks noticeable uptick post earnings uipath which had been really beaten down prior to this quarter it's brought in a new co-ceo and it's business is showing a nice rebound with a small three cent eps beat and a nearly 20 million dollar top line beat crowdstrike is showing strength as well meanwhile managements at microsoft workday and snowflake expressed greater caution about the macroeconomic climate and especially on investors minds his concern about consumption pricing models snowflake in particular which had a small top-line beat cited softness and effects from reduced consumption especially from certain consumer-facing customers which has analysts digging more deeply into the predictability of their models in fact barclays analyst ramo lenchow published an especially thoughtful piece on this topic concluding that [ __ ] was less susceptible to consumption headwinds than for example snowflake essentially for a few reasons one because atlas mongo's cloud managed service which is the consumption model comprises only about 60 percent of mongo's revenue second is the premise that [ __ ] is supporting core operational applications that can't be easily dialed down or turned off and three that snowflake customers it sounds like has a more concentrated customer base and due to that fact there's a preponderance of its revenue is consumption driven and would be more sensitive to swings in these consumption patterns now i'll say this first consumption pricing models are here to stay and the much preferred model for customers is consumption the appeal of consumption is i can actually dial down turn off if i need to and stop spending for a while which happened or at least happened to a certain extent this quarter for certain companies but to the point about [ __ ] supporting core applications i do believe that over time you're going to see the increased emergence of data products that will become core monetization drivers in snowflake along with other data platforms is going to feed those data products and services and become over time maybe less susceptible and less sensitive to these consumption patterns it'll always be there but i think increasingly it's going to be tied to operational revenue last two points here in this slide software evaluations have reverted to their historical mean which is a good thing in our view we've taken some air out of the bubble and returned to more normalized valuations was really predicted and looked forward to look we're still in a lousy market for stocks it's really a bear market for tech the market tends to be at least six months ahead of the economy and often not always but often is a good predictor we've had some tough compares relative to the pandemic days in tech and we'll be watching next quarter very closely because the macro headwinds have now been firmly inserted into the guidance of software companies okay let's have a look at how certain names have performed relative to a software index benchmark so far this year here's a year-to-date chart comparing microsoft salesforce [ __ ] and snowflake to the igv software heavy etf which is shown in the darker blue line which by the way it does not own the ctf does not own snowflake or [ __ ] you can see that these big super caps have fared pretty well whereas [ __ ] and especially snowflake those higher growth companies have been much more negatively impacted year to date from a stock price standpoint now let's move on let's take a financial snapshot of [ __ ] and put it next to snowflake so we can compare these two higher growth names what we've done here in this chart has taken the most recent quarters revenue and multiplied it by 4x to get a revenue run rate and we've parenthetically added a projection for the full year revenue [ __ ] as you see will do north of a billion dollars in revenue while snowflake will begin to approach three billion dollars 2.7 and run right through that that four quarter run rate that they just had last quarter and you can see snowflake is growing faster than [ __ ] at 85 percent this past quarter and we took now these most of these profit of these next profitability ratios off the current quarter with one exception both companies have high gross margins of course you'd expect that but as we've discussed not as high as some traditional software companies in part because of their cloud costs but also you know their maturity or lack thereof both [ __ ] and snowflake because they are in high growth mode have thin operating margins they spend nearly half or more than half of their revenue on growth that's the sg a line mostly the s the sales and marketing is really where they're spending money uh and and they're specialists so they spend a fair amount of their revenue on r d but maybe not as high as you might think but a pretty hefty percentage the free cash flow as a percentage of revenue line we calculated off the full year projections because there was a kind of an anomaly this quarter in the in the snowflake numbers and you can see snowflakes free cash flow uh which again was abnormally high this quarter is going to settle in around 16 this year versus mongo's six percent so strong focus by snowflake on free cash flow and its management snowflake is about four billion dollars in cash and marketable securities on its balance sheet with little or no debt whereas [ __ ] has about two billion dollars on its balance sheet with a little bit of longer term debt and you can see snowflakes market cap is about double that of mongos so you're paying for higher growth with snowflake you're paying for the slootman scarpelli execution engine the expectation there a stronger balance sheet etc but snowflake is well off its roughly 100 billion evaluation which it touched during the peak days of tech during the pandemic and just that as an aside [ __ ] has around 33 000 customers about five times the number of customers snowflake has so a bit of a different customer mix and concentration but both companies in our view have no lack of market in terms of tam okay now let's dig a little deeper into mongo's business and bring in some etr data this colorful chart shows the breakdown of mongo's net score net score is etr's proprietary methodology that measures the percent of customers in the etr survey that are adding the platform new that's the lime green at nine percent existing customers that are spending six percent or more on the platform that's the forest green at 37 spending flat that's the gray at 46 percent decreasing spend that's the pinkish at around 5 and churning that's only 3 that's the bright red for [ __ ] subtract the red from the greens and you net out to a 38 which is a very solid net score figure note this is a survey of 1500 or so organizations and it includes 150 mongodb customers which includes by the way 68 global 2000 customers and they show a spending velocity or a net score of 44 so notably higher among the larger clients and while it's a smaller sample only 27 emea's net score for [ __ ] is 33 now that's down from 60 last quarter note that [ __ ] cited softness in its european business on its earning calls so that aligns to the gtr data okay now let's plot [ __ ] relative to some other data platforms these don't all necessarily compete head to head with [ __ ] but they are in data and database platforms in the etr data set and that's what this chart shows it's an xy graph with net score or as we say spending momentum on the vertical axis and overlap or presence or pervasiveness in the data set on the horizontal axis see that red dotted line there at 40 that indicates an elevated level of spending anything above that is highly elevated we've highlighted [ __ ] in that red box which is very close to that 40 percent line it has a pretty strong presence on the x-axis right there with gcp snowflake as we've reported has come down to earth but still well elevated again that aligns with the earnings releases uh aws and microsoft they have many data platforms especially aws so their plot position reflects their broad portfolio massive size on the x-axis um that's the presence and and very impressive on the vertical axis so despite that size they have strong spending momentum and you can see the pack of others including cockroach small on the verdict on the horizontal but elevated on the vertical couch base is creeping up since its ipo redis maria db which was launched the day that oracle bought sun and and got my sequel and some legacy platforms including the leader in database oracle as well as ibm and teradata's both cloud and on-prem platforms now one interesting side note here is on mongo's earning call it clearly cited the advantages of its increasingly all-in-one approach relative to others that offer a portfolio of bespoke or what we some sometimes call horses for courses databases [ __ ] cited the advantages of its simplicity and lower costs as it adds more and more functionality this is an argument often made by oracle and they often target aws as the company with too many databases and of course [ __ ] makes that argument uh as well but they also make the argument that oracle they don't necessarily call them out but they talk about traditional relational databases of course they're talking about oracle and others they say that's more complex less flexible and less appealing to developers than is [ __ ] now oracle of course would retur we retort saying hey we now support a mongodb api so why go anywhere else we're the most robust and the best for mission critical but this gives credence to the fact that if oracle is trying to capture business by offering a [ __ ] api for example that [ __ ] must be doing something right okay let's look at why they buy [ __ ] here's an etr chart that addresses that question it's it's mongo's feature breadth is the number one reason lower cost or better roi is number two integrations and stack alignment is third and mongo's technology lead is fourth those four kind of stand out with notice on the right hand side security and vision much lower there in the right that doesn't necessarily mean that [ __ ] doesn't have good security and and good vision although it has been cited uh security concerns um and and so we keep an eye on that but look [ __ ] has a document database it's become a viable alternative to traditional relational databases meaning you have much more flexibility over your schema um and in fact you know it's kind of schema-less you can pretty much put anything into a document database uh developers seem to love it generally it's fair to say mongo's architecture would favor consistency over availability because it uses a single master architecture as a primary and you can create secondary nodes in the event of a primary failure but you got to think about that and how to architect availability into the platform and got to consider recovery more carefully now now no schema means it's not a tables and rows structure and you can again shove anything you want into the database but you got to think about how to optimize performance um on queries now [ __ ] has been hard at work evolving the platform from the early days when you go back and look at its roadmap it's been you know started as a document database purely it added graph processing time series it's made search you know much much easier and more fundamental it's added atlas that fully managed cloud database uh service which we said now comprises 60 of its revenue it's you know kubernetes integrations and kind of the modern microservices stack and dozens and dozens and dozens of other features mongo's done a really fine job we think of creating a leading database platform today that is loved by customers loved by developers and is highly functional and next week the cube will be at mongodb world and we'll be looking for some of these items that we're showing here and this this chart this always going to be main focus on developers [ __ ] prides itself on being a developer friendly platform we're going to look for new features especially around security and governance and simplification of configurations and cluster management [ __ ] is likely going to continue to advance its all-in-one appeal and add more capabilities that reduce the need to to spin up bespoke platforms and we would expect enhance enhancements to atlas further enhancements there is atlas really is the future you know maybe adding you know more cloud native features and integrations and perhaps simplified ways to migrate to the cloud to atlas and improve access to data sources generally making the lives of developers and data analysts easier that's going to be we think a big theme at the event so these are the main things that we'll be scoping out at the event so please stop by if you're in new york city new york city at mongodb world or tune in to thecube.net okay that's it for today thanks to my colleagues stephanie chan who helps research breaking analysis from time to time alex meyerson is on production as today is as is andrew frick sarah kenney steve conte conte anderson hill and the entire team in palo alto thank you kristen martin and cheryl knight helped get the word out and rob hof is our editor-in-chief over there at siliconangle remember all these episodes are available as podcasts wherever you listen just search breaking analysis podcast we do publish each week on wikibon.com and siliconangle.com want to reach me email me david.velante siliconangle.com or dm me at divalante or a comment on my linkedin post and please do check out etr.ai for the best survey data in the enterprise tech business this is dave vellante for the cube insights powered by etr thanks for watching see you next time [Music] you