Today I'm going to talk about Snowflake and how it became the largest IPO in software history.
To give you an idea of the magnitude of this IPO, Berkshire Hathaway — led by Warren Buffett, hasn’t participated in an IPO in over 50 years. But they threw their hat into the ring and purchased 250 million worth of stock in a private placement as part of the IPO.
That’s how exciting this IPO was.
But let’s bring it back to the beginning.
How did Snowflake achieve such incredible growth, attracting some of the largest investors in the world?
There were 4 very prominent things that Snowflake did to achieve this incredible milestone.
- Perfected the cloud-based data platform.
- Changed the traditional SaaS pricing model from per-seat-based pricing to a utilization model.
- Perfected their account-based marketing strategy.
- Made strategic management changes pre-IPO.
Now for the purposes of this article, we’re going to focus on #3, and break down their ABM (account-based marketing strategy).
Why am I not talking about 1, 2, or 4?
Well.
1. You are most likely not building a new cloud-based data platform that will compete with the largest IPO in software history to date (forgive me if you are).
2. You may not be selling SaaS, and although you may be — I don’t want angry CEO’s coming after me saying Scott influenced their VP Sales/Marketing to suggest they redo their pricing strategy and uproot their entire pricing model.
4. You don’t know Frank Slootman.
So let’s get into the ABM part of it.
In 2018, Snowflake grew its customer base by 300%.
How did the company do this?
Sales and marketing alignment.
It may seem like a simple concept, but many organizations have disjointed sales and marketing conversations.
An ABM approach creates alignment between the sales and marketing teams.
As opposed to having marketing messaging going out to customers and sales teams dialing for dollars across a wide variety of accounts, a true ABM approach identified an ideal customer profile, and then marketing and sales will work together to ensure that every conversation a potential prospect has with a sales rep, aligns with the marketing message that the prospect has seen on your website, or your social.
So how did Snowflake execute this strategy so successfully?
They threw out the rule book.
“Instead of using our preconceived ideas around industry categories, we let the data speak for itself.” — Daniel Day, former Director of ABM, Snowflake
Create An Ideal Customer Profile
The strategy was to capture market share but also retain customers. They wanted to make sure that churn was at an all-time low and that the customers they captured were actually retained.
To do this, they needed to have a strong ‘Ideal Customer Profile’ that would help drive sales and marketing efforts, but they understood that their Ideal customer profile may not be static, so they needed to constantly test the model to make sure that every customer they brought on, was a customer, that would last.
Snowflake leveraged machine learning (using the business analytics platform Everstring) to analyze its 50 fastest deals and its 50 biggest deals to find accounts that would make up their customer profile.
They repeated this regularly to make sure that the model was 100% accurate.
Personalize The Outreach
In the early days, Snowflake only had 30 sales reps, so they needed to prioritize and optimize the sales/outreach they did.
They gave each sales rep 100 accounts, with automated outreach going to 90 and 10 receiving custom, personalized messaging.
They broke their outreach into a 1:many, 1:few, strategy, leveraging automation to reach people at scale while hyper-focusing on a select subset of customers.
Now, ofcourse, this may not seem like 10 accounts is a lot of work to personalize an ABM & sales strategy. Still, these are enterprise accounts, so the reps were reaching out to several individuals (multi-threading) at each organization with messaging that fell in line with the marketing strategy.
After they closed these accounts, they added these accounts into their model and continued to improving and optimizing their ideal customer profile.
Rinse and repeat.
Tech Stack
After a bit of research, I found the exact process and tech stack that Snowflake uses, courtesy of Douglas Karr.
This breaks it down quite simply.
The ABM Process that Snowflake deploys:
Target — utilizing Everstring and Bombora, Snowflake isn’t hand-selecting target companies… it’s discovering businesses that match their best clients and have displayed an intent to purchase.
Reach — utilizing Terminus, Sigstr, and LinkedIn, Snowflake is assembling personalized content experiences that touch prospective buyers before they may even be aware of their solution. In fact, one customer had 450 touches before the client ever submitted a request!
Engage — utilizing Uberflip, Snowflake has content experiences that are owned by the sales account manager but produced by the ABM team to provide highly targeted content to drive the buyer into the customer journey.
Measure — utilizing Engagio, Tableau, and Looker. Snowflake developed a proprietary means of scoring the leads and providing the sales intelligence needed to the sales account managers to assist them in closing the deal.
Results?
Click-through rates increased 149x on 1:1 ABM ads. Half of ALL of the content that Snowflake produces is being consumed by ABM targeted organizations.
Throwing out any preconceived notions or category standards of who they were “supposed” to target, ensuring alignment across sales and marketing, trusting data, and leveraging tools and tech, all merged in a beautifully executed ABM (sales & marketing) campaign which not only won a massive amount of customers but won a massive amount of the right customers, many of which are still loyal to Snowflake, to this day.
When you really look into how meticulously and strategically Snowflake chose to operate its sales and marketing strategy, it’s not hard to see why they achieved such significant growth.
Also published on https://newsletter.roioverload.com/publish/post/37255245.