A Rising Company of Snowflake to Run to a Giant in Cloud Data Warehousing

Snowflake is a cloud-based data-warehousing startup that was founded in 2012 by a team of data warehousing experts including two Oracle engineers – Benoit Dageville, Thierry Cruanes – and a co-founder of the Dutch start-up Vectorwise – Marcin Zukowski. The cofounders picked a whimsical company name that reflected their shared love of skiing.

Snowflake offers a cloud-based data storage and analytics service, generally termed “data warehouse-as-a-service”. It allows corporate users to store and analyze data using cloud-based hardware and software.

From the puny start, Snowflake has raised more than $900 million in venture money, while achieving a valuation that tops $4 billion. Snowflake’s engineers have redefined data storage and computation to take full advantage of cloud computing’s flexibility. The result: A data-warehousing system that is easier to use and stunningly faster than older alternatives. Look at Snowflake’s customer list, and you will see businesses ranging from scooter maker Lime to banking giant CapitalOne.

Snowflake came out of stealth mode in October 2014 shortly after appointing former Microsoft executive Bob Muglia -former Microsoft executive – as CEO that June. He said the sweet spot for Snowflake is in the storing of a mixture of structured and unstructured data.

Muglia said the focus of Snowflake is its ability to store data in the cloud while also being able to run analytics across that data. “We’re now seeing a world where more data is being born in the cloud and created in the cloud than on premises. We’re seeing a great deal of interest for building data analysis for data warehousing,” Muglia articulated.

The cloud data warehouse became generally available in June 2015 and had 80 organizations using it at that time. Snowflake runs on Amazon S3 since 2014, and on Microsoft Azure since 2018. It is being rolled out on Google Cloud Platform in 2019. Its Snowflake Data Exchange allows customers to discover, exchange and securely share data.

With huge new $450M funding round in 2018, Snowflake Computing has now raised almost $1 billion.

#1. The Uniqueness of Cloud Data Architecture Designed by Snowflake Inc.

Snowflake’s unique cloud-built architecture enables organizations of any size to make data-driven decisions not previously possible. With Snowflake, customers get deeper insights from more data thanks to the power, simplicity and instant and near-infinite scalability of modern data warehousing and data analytics. In addition, customers can easily and securely share live data with revocable access rights across business units and with their partners and customers. These ongoing innovations put Snowflake far ahead of traditional, on-premises and “cloud-washed” solutions.

Most of the traditional databases, as well as RedShift and many noSQL systems, use shared-nothing architecture, which distributes subsets of data across all the processing nodes in a system, eliminating the communications bottleneck suffered by shared-disk systems. The problem with these systems is that compute cannot be scaled independently of storage, and many systems become overprovisioned, Snowflake notes. Also, no matter how many nodes are added, the RAM in the machines used in these systems limits the number of concurrent queries they can handle, Muglia says.

“Today’s organizations want the broadest access to the deepest insights from all their data,” Bob Muglia said. “Snowflake continues to deliver on this vision with the data warehouse that enables the data-driven organization. They have advanced on that uniqueness by connecting more organizations to the Data Economy via Snowflake, so they can easily and securely share any part of their data across their business units and with their business partners and customers. That will enable them to uncover new insights and enable new opportunities and revenue streams. Only Snowflake can provide these capabilities by virtue of our unique cloud-built architecture.

Snowflake is designed to solve this problem by using a three-tier architecture:

• A data storage layer that uses Amazon S3 to store table data and query results.
• A virtual warehouse layer that handles query execution within elastic clusters of virtual machines that Snowflake calls virtual warehouses.
• A cloud services layer that manages transactions, queries, virtual warehouses, metadata such as database schemas, and access control.

Snowflake have capitalized on the demand for cloud-based analytics by optimizing its technology for the public cloud, making it extremely easy to use, and requiring zero maintenance. It allows organizations to manage massive quantities and diverse types of data across all three major public clouds in one easy-to-use, secure platform. It appears that its founders foresaw the coming shift to the cloud, which explains why they were the first to build a data warehouse platform optimized for cloud computing.

Due to the network effects that apply in the enterprise software space, in which leading solutions often become even more widely adopted because people have trained on them and are used to them, Snowflake appears to have a fair amount of stickiness as the leading purely cloud-based data warehouse. And even though its three key cloud partners all offer similar types of services, Snowflake’s “cloud-neutral” posture seems to be resonating. It is similar to the way that MongoDB’s cloud database has taken off, even though all the major cloud providers provide their own static databases.

This architecture lets multiple virtual warehouses work on the same data at the same time, allowing Snowflake to scale concurrency far beyond what its shared-nothing rivals can do, Muglia says.

One potential problem is that the three-tier architecture might led to latency issues, but Muglia says that one way the system maintains performance is by having the query compiler in the services layer use the predicates in a SQL query together with the metadata to determine what data needs to be scanned.

But make no mistake: Snowflake is not an OLTP database and it is only going to rival Oracle or SQL Server for work that is analytical in nature.

Meanwhile, though, it is setting its sights on new horizons. “In terms of running and operating a global enterprise having a global database a very good thing and that’s where we’re going,” Muglia says.

#2. An Elite Group of Savvy Leaders and Technology Experts

Another advantage for Snowflake may be its management. They usually have the expertise and technology chops to make it happen. The firm is led by its CEO, Frank Slootman, a famous tech veteran. He is known for his exceptional skills in managing fast-growing tech companies. Before joining the start-up, he held the CEO position at Data Domain, which he later sold to EMC for $2.4B.

Interestingly, Snowflake’s co-founders have always hired outside CEOs with significant administrative experience, which could be another reason why the company has accomplished just as much on the sales and marketing execution front as it has with its technology. Current CEO Frank Slootman was just hired out of retirement in April 2019. He has extensive executive experience and was formerly CEO of ServiceNow, one of the leading cloud-based, back-end software vendors for large enterprises. Slootman is highly regarded, and during just six years at ServiceNow, he grew the company’s revenues from $75M to $1.5B. The fact that Snowflake was able to lure him out of retirement also says something about the company’s prospects.

#3. A Phenomenally Strategic Alliances with Competitive Business Partners

Snowflake’s most outstanding features include:

• Diverse data type capabilities and data volume scalability.
• Dynamic availability of compute resources.
• Secure and governed access to all data.
• Near-zero maintenance as a service.

The company has developed a robust partner programme, which helps the business expand its product’s reach and capabilities. Some of the firm’s strategic alliances include those with Amazon Web Services, Google Cloud, Microsoft Azure, Salesforce, Accenture, Deloitte and Dataiku.

Snowflake provides integrated data management capabilities to enterprises around the world. While it pursues organizations of all sizes, its main focus remains on medium and large firms. Capital One is currently its single biggest customer, representing 11% of Snowflake’s fiscal 2020 revenue.

Other notable names on the list of the firm’s customers include businesses from all industries and sectors: Adobe, Doordash, Dropbox, Electronic Arts, Hulu, Lionsgate, Logitech, Lululemon, McKesson, Netflix, Office Depot and Yamaha.

By July 31, 2020, Snowflake has already had more than 3,100 clients, including 146 of the Fortune 500.

#4. Three Components in Snowflake’s Marketing and Sale Foundation

With steady growth to-date and marquee customers such as Netflix, Office Depot, DoorDash, Netgear, Ebates and Yamaha, it is no wonder why the business was ripe for additional funding.

However, it was Snowflake’s ability to scale Marketing and Sales to meet aggressive growth goals that gave them the leverage they needed to secure funding for additional growth and globalization.

There were three key components of their Sales and Marketing foundation:

Component #1: Key Account

In business-to-business (B2B) deals, companies sell to accounts, not single buyers. Gartner research found on average, 12 to 14 individuals participate in larger technology purchases, like that of Snowflake’s cloud data warehousing services.

As many high-performing B2B organizations have done, the company adopted an Account Based Marketing (ABM) strategy in order to focus time and resources on high-value, good-fit target accounts. This allowed Snowflake’s Marketing team to drive larger deals (and better economies of scale) by focusing on the same prioritized list of accounts that Sales was focused on.

To build that target list, the company’s Marketing team worked with Sales to define their Ideal Customer Profile (ICP), a set of criteria that helps to refine a universe of possible targets down to only those that are most likely to buy, and drive strong lifetime value. This collaboration was important, as it ensured buy-in from both teams.

Many teams wonder how to build a list of target accounts. Building an ICP as Snowflake did comes down to answering a few critical questions:

What sector(s) are you winning in?

What “lookalike” segments are similar to those you’re having success with now?

What new markets are most important for your company to develop? Are they growing?

What accounts will deliver the most value in the form of strategic value, advocacy, referrals, or geographic presence?

Component #2: Intelligence and Management at the Account Levels

With the target account list identified, a key factor in the success of this initiative was the unique way in which Snowflake approached measurement to align with this account focus.

The company first needed to understand its account coverage, or how many viable contacts for these target accounts existed in their marketable database. Lead records are historically owned by marketing, and do not roll up to accounts in most CRM systems. This divide is also reflected in the current marketing automation systems. The risk of un-matched contacts manifests itself in incorrect routing, scoring, a disruption to the customer’s experience, and poor reporting.

To bridge this gap, Snowflake deployed lead-to-account matching technology within their CRM database to understand which individual contacts belonged to which target accounts.

The company also implemented measurement that focused on account engagement tracking – in other words, how all potential buyers at target accounts interacted with both Marketing and Sales touches. Engagement is a key for teams like Snowflake’s to measure, as it indicates that prospects and customers are more committed to the relationship. It also shows increasing likelihood to purchase, renew, or advocate.

Snowflake shares this intelligence across the entire revenue team for a common perspective on how an account is performing. This has helped the team warm up new territories when the revenue team identifies trending engagement, as they continued to hire Sales representatives against their aggressive growth targets. By accelerating the ramp-up of these new sales hires and new territories, Snowflake successfully expanded their global sales team by 4X with fast and efficient ramp periods.

Today, with visibility on current engagement levels and account progression, Snowflake’s target accounts are demonstrating more than 15 engaged people across the business.

Component #3: Tiering Accounts and Prioritizing Accordingly

The final piece to Snowflake’s success was the understanding that all target accounts are not created equal and prioritizing accordingly.

The firm supports 3,000 target accounts with a single ABM program manager who sits on the Marketing team, and 30 sales reps.

Their key to this type of scale and efficiently is tiering. Rather than try to treat all 3,000 accounts equally with the same messaging, campaigns, or resource allocation, Snowflake identified 300 “tier 1” accounts, with the rest assigned to “tier 2.”

Tier 1 accounts get bespoke attention, fully customized plans, and deep research – after all, these accounts represent the highest potential value to the business. The ABM program manager works closely with Sales in ABM standups to tailor key messages, and deliver highly customized 1:1 programs (one person on Snowflake’s team engaging with one person on the target account’s team) for each of the rep’s top 10 accounts. These efforts could span channels such as ads, direct mail, live events, and more.

The team uses orchestration to automate interactions across these multiple channels.

Tier 2 accounts do not get completely customized marketing plays and content, but they still get highly relevant touches based on their industry and persona. Instead of 1:1 program, these accounts get 1: few campaigns, such as Snowflake’s “Data Warehouse Modernization” campaign designed to shift companies from legacy to cloud-based infrastructure.

This creative content-based sales play provides 1-pagers that directly speak to the challenge’s buyers face in this scenario. This type of highly defined, yet programmatic, play has allowed Snowflake to be successful at operationalizing and delivering ABM at scale and achieve rocket ship growth.

Thanks to the combination of these efforts, Snowflake realized 300% growth in 15 months, far exceeding any original goals they set. They were able to implement a high-functioning ABM program that scaled pipeline in a repeatable and predictable motion, helping them to raise $450 million to fund additional growth across the globe.