Datadog: Top dog in IT observation

Datadog is one of the larger positions in my portfolio and I was happy to add more when the share price fell despite revenue growing 61% y/y in the recent quarter.

What does Datadog do?

It is 3am and your phone starts ringing. You are the CTO of one of the world’s largest banks and your CEO is on the line asking why the bank’s mobile banking application broke down and when you can get the app up again.

Enter Datadog!

There are 13,000 companies such as HSBC, MercadoLibre, Expedia and Starbucks which use Datadog’s platform to monitor and analyze their IT infrastructure and applications. Using Datadog’s monitoring platform allow IT teams to spend less time fixing problems and more time building useful features for customers.

Datadog was founded in 2010 by Olivier Pomel (CEO) and Alexis Le-Quoc (CTO) who had a vision of breaking down silos between software development, operations and security teams. Unlike other SaaS (Software as a service) companies, Datadog’s services are so easy to use that they do not requires professional services to implement. As a result, 100% of their revenue is subscription-based with most of the company’s revenue derived from annual subscriptions.  

Here are the top 3 reasons why I bought Datadog:

(1) Customer-focused culture  

Datadog’s founders were based in New York and had huge difficulties raising funds which forced them to spend six months listening to customers before writing their first line of code. The company was laser-focused for 5 years (2012-16) on the infrastructure segment with the company designing an infrastructure monitoring service specifically for the cloud. Focusing on infrastructure monitoring ensured that its first product was widely used by developer, operation and business teams before Datadog launched its second product – application performance monitoring in 2017.

During the early days, Datadog subscriptions was sold on a monthly instead of an annual basis to allow faster feedback if the client cancelled their subscription. This client-focused culture persists in Datadog today where all developers and engineers are required to spend at least 1 week on customer support rotation and trade show demonstrations every year. Meanwhile, Datadog’s CEO is still personally reading every customer complaint email.

A SaaStr interview with Datadog’s CEO is well worth reading. Here is a brief abstract.

Olivier: Right. So the one thing we haven’t done so far, we actually haven’t written down these other seven values or the 14 things you do. We haven’t done that so far. It’s something we’ll do in the future because I think, as we scale, we will need that so we can better train. But culture at the end of the day it’s really about who you hire, who you fire, who you promote. That’s what shows the culture. And for us the culture we want to have is a culture where we are completely customer-centric. So for example, we train people, we train the managers to care about the details. So one thing we do, I actually do still as a CEO, is I see every single customer complaint and support email that comes to us. I don’t read most of them. I delete most of them right away. I just scan them very quickly.

Olivier Pomel, CEO of Datadog: How to Build and Sell a Product that Customers Love, SaaStr, October 2018

Datadog’s customer-first culture, the widespread use of free trials for marketing and a bottom-up focus on developers rather than chief technology officers has resulted in marketing efficiency that is best-in-class. Take a look at the median marketing payback period of Datadog and their competitors over the last 10 quarters:

Datadog: 11 months

Elastic: 18 months

Dynatrace: 28 months

Sumo Logic: 36 months

New Relic: 37 months

Marketing payback was calculated by multiplying the change in revenue by gross margin divided by the sales and marketing expense from the previous quarter.

With a median 11 months marketing payback period compared to 18-37 months for competitors, Datadog’s customer-first culture is clearly delivering results. 

Datadog’s customer-first culture should allow the company to capture the huge growth opportunities available in the IT observation market. Only 5% of all IT applications are monitored according to Gartner and Datadog estimates its total market opportunity to be USD35 billion. With Datadog’s last 12 months revenue amounting to USD539 million, the company has plenty of room to grow. Look at the company’s rapidly expanding customer count and cross-selling possibilities. As of end-September 2020, Datadog had 1,015 customers with annual recurring revenue of USD100,000 with this metric growing 70% year on year. Datadog customers are also choosing more products from the company’s portfolio. As of end-September 2020, 71% of Datadog’s customers are using two or more products, which is up from 50% a year ago while over 20% of Datadog’s customers are now using four or more products compared to 7% in the same 2019 period.

(2) High switching costs

Datadog was the first company to combine the three pillars of observability in 2018 by combining infrastructure monitoring services with application performance monitoring and log management. Combining more observability services makes these Datadog services stronger together because it allows customers to assess and resolve their IT issues faster without shifting between products and translating multiple data sources. The company’s R&D speed is stunning. In the third quarter of 2020 alone, the company added 8 more products and features to its observation platform with these new services ranging from the Datadog app store (marketplace for third party developers), Continuous Profiler to measure code performance, Mobile Real User Monitoring for both Android and iOS; and Compliance Monitoring to notify on misconfigurations. It’s clearly still Day 1 for Datadog but these new products allows Datadog to provide a full end-to-end range of observation services which will be difficult for competitors to match.

Datadog’s wide range of services also increases switching costs because of their usage by different departments and users within a client. Compared to legacy systems that are often used only by a few users in an organization’s IT operations team, Datadog is used by developers, operations engineers and business leaders. One of Datadog’s clients has almost 800 Datadog users, about half of the client’s total employee count and greater than the client’s engineer headcount.

Datadog S1 filing describes how Starbucks uses Datadog products.

As Starbucks deploys new features and changes on the app, Datadog provides engineers immediate feedback on performance. With many ways for customers to pay (plastic cards, mobile payments, the Starbucks app, and third-party apps) Datadog collects crucial transaction data, allowing Starbucks to better understand the customer and ensure purchases are recognized through the rewards program. In all, there are currently over 100 Starbucks teams using Datadog to support the business.

Datadog S1

The company’s dollar based net retention rate (DBNR) is among the best when compared to other software companies. DBNR has stayed above 130% for 13 consecutive quarters – anything above 100% indicates increased spend by existing clients. Datadog’s gross customer retention numbers in the 2020 to date was also impressive: 95%.   That’s another clear sign of high switching costs.

(3) Network effects from third party integrations

Datadog’s service is also integrated with 400 other third party apps and software services which includes all the cloud providers such as Amazon Web Services, Microsoft Azure, Google Cloud and Alibaba Cloud. Datadog thus has an advantage over the large cloud providers as they only monitor their own platform. More third party integrations results in Datadog becoming more useful to clients while a growing client count attracts other software companies to integrate with Datadog’s monitoring platform.  As Datadog integrates with more software companies, the company is able to provide increasingly valuable insights while enabling them to predict customer needs which drives new Datadog product features. This trend creates a small moat where the company’s growing eco-system becomes increasingly valuable to users and suppliers. I’m most excited about Datadog’s app marketplace because it has the potential to transform Datadog into a platform which benefits from network effects. As developers add new applications to Datadog’s marketplace, it makes Datadog increasingly attractive to clients which in turn attracts more developers.

Datadog was only listed in 2019 but the company’s limited financial data look promising.

USDmRevenueGross profitNet profitOperating cash flowFree cash flowNet cashDiluted shares (MM)
201710177-2.513.8660N/A
2018198151-10.710.81.853N/A
2019363274-16.724.211713327
LTM539423-7102.794.9862.5332

Revenue and free cash flow has compounded at 90% and 35% respectively from 2017 to 2019.

Revenue growth has slowed to 70% for the first nine months of 2020 and 61% in the third quarter of 2020 with companies cutting back on usage of Datadog services to save costs during the pandemic but growth should pick up again once the economy normalizes.  Datadog’s subscription revenue is usage-based with usage measured primarily by the number of hosts (servers) or by the volume of data indexed.

I’m also not worried about the recent revenue growth slowdown because the company has been successfully signing large customers (USD100,000+ annual revenue) during 2020.

Conclusion

Datadog is a customer-focused company at the right place at the right time with the pandemic stressing the need for cloud-native solutions and high uptime for a company’s technology stack.

Given the company’s large addressable market, revenue should compound at least 40% over the next 10 years which should lead to 30% free cash flow margins and a 100% upside from today’s USD86 share price. I used a 2% terminal growth rate and 8% cost of capital.

For perspective, Salesforce.com has compounded revenue at 41% per year over the last 16 years while mature software companies such as Veeva Systems and Adobe enjoy 30-40% free cash flow margins. Datadog was GAAP profitable in the first half of 2020 while free cash flow margin was 14% in the last 12 months so 30% free cash flow margins should be achievable with scale.

Datadog has sky-high valuations with its enterprise value trading at 49x trailing revenue. With their customer-first culture and high switching costs, I think Datadog should continue growing so I’m happy to add to my position at current levels if the share price continues falling.   

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