Behind HockeyStack’s New Round and What It Means For Our Category
Last week, we announced our $2.7M round with participation from the best investors in the world. This is likely the most transparent write-up you’ll ever find on how such a round came together.
Last week, we announced our $2.7M round with participation from the best investors in the world - General Catalyst, Madrona, Y Combinator, Soma Capital, Austen Allred, Jude Gomila and other incredible partners. This is likely the most transparent write-up you’ll ever find on how such a round came together.
The Context
Between June and August, we went through the YC S23 batch. Prior to joining YC, we were already raising and had a couple of great offers that we had to turn down.
As the batch starts, most YC companies are pre-revenue, pre-product, and even pre-idea. They sprint towards PMF for 2-2.5 months, make a spectacular amount of progress, and then go out to talk to investors.
Imagine you are an investor, talking to 20 YC companies a week, most of them having a couple of LOIs or pilots, and some having <$100k revenue, which is still incredibly impressive. But then you come across us, already doing good amount of revenue, has a strong scalable GTM, a fully built out product, case studies. We stood out, and it made things easier.
The Plan
The goal for the fundraise was to raise the least amount of money possible to get us to Series B. That means low dilution. But that also means a forcing function on efficiency. A balance of efficiency and growth will be the key to us doing a great Series B, so having a forcing function is actually a good thing.
But it was a challenge to balance resources and define priorities internally, because as a startup you always have more things you need to do than you can actually do. My thinking is that you always have to prioritize one thing:
Solving bottlenecks towards your progress, with progress being defined as the learnings you get that helps you answer the question: “Will this company ever do $1B in revenue?”
Once you are able to confidently say Yes, you reach product-market fit.
Notice a few things:
I’m saying “$1B in revenue” instead of “$10B company”, because company valuations are fake. The only real metric is profit. The best proxy for profit at this stage is revenue.
I’m saying “learnings” instead of “growth”. That means, given the choice between $1M in revenue from 100 customers and $500k in revenue from 5 customers, you take the second option, because it gives you more learnings.
I’m saying “Solving bottlenecks” instead of, again, “increasing growth”. That means, if you have an incredible engineering team, and a beautiful product, you don’t invest in making the product better; you invest in selling. Conversely, if you have a huge customer pipeline, you don’t invest in doubling your pipeline; you invest in building better product or retaining more customers.
So if you have that sentence as your north star, you are able to make the hard decisions easier.
The Raise
I started raising on August 7th and got my first check the same day. Overall, we had a super low dilution, with great partners on the cap table, and on a short enough timeline.
Here’s, by far, the biggest challenge we had to overcome throughout the entire process: MarTech and SalesTech are two of the most crowded categories of software out there.
I’m a follower of the Peter Thiel doctrine of “become so differentiated that nobody can compete with you and you now have to prove to people that you have competition.”
So, my narrative was, “there is nothing like what we’re building”, and I said nothing about the thousands of marketing and sales tech companies out there.
This objection disappeared after I started saying, head-on, “Yes, I know there are thousands of marketing and sales tech companies out there, and that’s why we’ll win.”
The HockeyStack Chapter
10 years ago, the tech stack was a lot more centralized than today, with Salesforce or a data warehouse sitting in the middle of everything. Over the past 10 years, with the internet gaining steam for B2B sales, traditional sales methods losing their significance, B2B markets getting saturated, and go-to-market machines getting more complex, the B2B buyer journey completely changed shape. For companies to keep up with the change, new point solutions popped up for each part of the go-to-market process. The average number of SaaS tools a B2B company uses increased from 5 in 2015 to 130 in 2023.
So, yes, the B2B go-to-market tech stack has grown uncontrollably large. You now have a separate tool for intent, audience building, attribution, dashboarding, forecasting, goal setting, sequencing, and more. And most of the data doesn’t seamlessly connect into your Salesforce, because Salesforce’s data structure is just too rigid to handle it all. Small companies have no shot at managing a data warehouse. Large companies have a data warehouse, but it has to be tuned manually for each new use case, and is completely inaccessible to the smart non-technical marketer or salesperson.
We first encountered this problem in the form of marketing attribution. Companies don’t know what channels are driving revenue, because all of their go-to-market data is spread out across tens of tools and who knows how someone can sort through everything to find out what influenced a single deal, let alone every deal that was won.
That’s why, companies, for years, settled for incomplete attribution by creating properties like “first_touch_source” and “last_touch_utm_campaign” in their CRMs. Everybody went along with this, disregarding the fact that just looking at the first touch and last touch for a B2B deal with a 6 month sales cycle, multiple stakeholders, often over $50k in value, is absolutely insane. Some solutions started popping up where you can do multi-touch attribution across a defined set of touchpoints. You have pre-defined rulesets like “U-shaped”, where you give X amount of credit for a conversion to the first and last touchpoint, and then distribute the remaining credit across all touchpoints. That is a lot like watching a 4k-recorded YouTube video at 144p. B2B buyers are complex human beings that are capable of being influenced by a marketing or sales touchpoint regardless of whether it was the first or the second touchpoint in their journey. And what about the touchpoints that aren’t recorded? It’s almost 100% that you haven’t recorded all touchpoints in a given buyer’s journey.
Here’s how you solve this:
You acknowledge that you will never know the entirety of the buyer psyche that led to a conversion. You let go of the idea of a perfect attribution model. You gain the capability of dissecting the known parts of the data from several angles to form an idea of what’s working. You test the idea out with experiments to see if what worked in the past is actually driving incremental revenue.
All of this requires:
for all your customer data to be recorded in the same place
in a flexible event-based format, rather than Salesforce’s rigid object-based format
and for a non-technical team member to be able to analyze data in any way, shape, form they want,
and take action on the dataset to turn data into revenue without complex workflows
If you put the entire GTM tech market on a graph based on their ability to achieve these three things, you see that there is a consistent increase in valuation and revenue of the companies from left to right. That proves that we’re on the right track.
Now, how do we actually build and grow this?
Step 1 - We create the most flexible system of record of customer data out there, with the ability to prescriptively and predictively analyze any sort of customer data. While everyone else is focused on building point solutions and modules for specific use cases, we play the long game and build a full-fledged data tool.
Step 2 - We find pockets of GTM tooling where competition is the weakest, and start selling there. First one being attribution, where we already became an undoubted choice.
Step 3 - As we grow our brand and customers trust us more, we increase the number of use cases we sell to, growing in each account along the way and selling larger and larger deals that deliver more and more value.
Today, we’re at this step, launching marketing mix modeling, marketing forecasts, goal setting, budget optimization, and ABM. All of them are continuations of the same exact product, rather than separate modules.
Step 4 - We end up becoming the market leader in most data use cases. Companies implement HockeyStack as their primary source of truth. Customer data is still inputed and managed across several different tools, but analyzed and actioned in HockeyStack.
Step 5 - We become the system of record where everyone inputs, reads, and actions on customer data.
From Step 3 to Step 5 will be a long journey, but this round is a testament to how fast we’ve come where we are and how much more we can do.