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Announcing Eppo's $28M Series B, and Why We Raised
Why we raised our Series B, and why experimentation is more important than ever in an era of AI and efficient growth
Today, Eppo is announcing our $28M series B financing, led by Davis Treybig at Innovation Endeavors with participation from Preeti Rathi at Icon Ventures.
Since our last funding, Eppo has become synonymous with large experimentation ambitions. Category-leading companies like Twitch, DraftKings, and Coinbase use Eppo to supercharge their experimentation. So do generative AI pioneers like Descript and Perplexity. Eppo customers are running experiments across their businesses with use cases spanning product, marketing, and AI.
When looking who to partner with on this round, Davis and the IE team were ultimately a no-brainer among our offers. Davis is the author of the best-researched piece on experimentation from anyone in venture capital, "The Experimentation Gap", outlining its clear connections to AI and use cases beyond digital products. We’ve been partnering with Davis and IE since our seed funding, and have been continuously impressed by their ability to build relationships with researchers on the frontiers of technology, and companies who see where this technology fits in their stacks.
Our Series B comes at an interesting inflection point for the tech industry, a new era of change where experimentation is the clear differentiator between which companies will thrive, and which will struggle.
Here’s why experimentation is more important than ever, and why we raised this Series B:
The two conversations happening in every boardroom
If there are two things every company is discussing right now, they are efficient growth and AI.
Rising interest rates and scarce capital have squeezed companies to the point that even a $500M ARR company with 30% growth trades at only 15x multiples. Teams were stripped down via layoffs, SaaS spend was consolidated, and growth venture investments have cratered. Growth is still an imperative, but must be done with scalable economics instead of growth at all costs. Put another way, it’s never been a greater existential risk to not know which company initiatives are growth levers vs. low-ROI money pits.
The second conversation is around AI. Even as companies clamp down on spend, AI budgets live outside of financial discipline. It’s for good reason: CEOs have seen astronomical increases in efficiency for CoPilot-augmented software development, creative asset development, and knowledge management. CEOs have all taken the time to imagine an AI-native competitor, and how they’d fare against them. GenAI technology presents a clear opportunity to leapfrog competition - or be leapfrogged.
Both of these factors have led to a blossoming in our experimentation space. Running experiments is the simplest path to high conviction on which products, campaigns, and AI strategies are successful, and which need to pivot or wind down. This market landscape is Darwinian: those who experiment, adapt, and swiftly refine their strategies are the ones who succeed. They accelerate their winning initiatives while quickly abandoning the ones that don't work.
Companies now expect more from experimentation
The point of experimentation is to drive velocity, growth, and innovation. Most companies aren’t there yet. They have low horsepower; bottlenecked experimentation stacks that don’t actually power velocity, growth, or innovation.
The biggest change since I started Eppo is companies demanding more from their experimentation investments.
Legacy experimentation vendors can’t deliver the vision. If a company buys a marketing-focused tool like Optimizely, they quickly figure out that the only supported tests are simple website changes. If they buy a feature flagging-centric tool like LaunchDarkly, they realize that the “experimentation” is a shallow coat of paint on a narrow DevOps tool. Teams using these tools end up stunted, spending more time and money on tedious manual efforts and expensive supplementary tools to fill the gaps.
The result is an inadequate trickle of experiments that are never quite trusted either.
In contrast, the tech giants with modern tooling are winning the era of efficient growth and AI. Instead of slowly spinning up button color tests, companies like Microsoft, Netflix, and Eppo customers are running experiments that can generate revenue and change strategic paradigms:
- An AI team saved $5M+ of spend by proving that open source LLM models could match the performance of an expensive GPT model they previously used. Now, all GenAI models are tested for ROI instead of implicitly trusted on brand name.
- At Netflix, simple UX changes are completely automated. Thousands of tests on show artwork are designed, set up, and adjudicated by algorithm.
- At Airbnb, we experimented on a sales team, holding out a random set of markets from their work and seeing if the sales-worked markets grew faster than the holdouts. The team was ultimately disbanded and reassigned, saving headcount cost and increasing strategic focus.
- A company spending tens of millions of dollars a year on YouTube ad campaigns tested whether the spend was doing anything by zeroing out the spend in a select group of geographies and comparing their performance.
In short, these companies are able to experiment pervasively, quickly, and with leadership trusting the results. They’ve built the accessibility and governance required to make any test possible and make experimentation like water: easy, continuous, expected.
Legacy tools like Optimizely or LaunchDarkly look nothing like the workflows that enable market leaders to evaluate large, expensive campaigns, or run all product development through test and learn iterations. With programs and software spend under tight budget scrutiny, the bar is now set much higher.
The age of AI will be an age of experimentation
We didn’t predict the explosion in AI capabilities at the end of 2022, but it created a massive appetite for experiments. There’s a short-term need to evaluate AI model ROI, and a long-term need to evaluate more ideas in general. AB testing is the primary solution for both.
Companies now have a firehose of new GenAI model generations at their fingertips, each reaching new heights and new, higher price tags. A GPT model release gets quickly followed by a new Claude, new Llama, and a host of open source models. As the New York Times aptly put it, AI has a measurement problem: companies have no idea which models are most accurate and provide the best user experience.
With AI capabilities in cloud APIs, the switching cost of these models is near zero. A simple feature flag can be repurposed to a routing system for AI model vendors — frictionless swaps of which API to use. This means that companies with good experiment infrastructure can get results that are more powerful and far cheaper with little effort.
Companies with better infrastructure can go further, multiplexing across an ensemble of gen AI models. Maybe pay up for a premium Claude 3.5 model for high-value users, and save money with open source models on Free tier users. There are wide disparities in price across the LLM clouds, and experimentation gives companies the edge to discern between real performance gains and spending that should be cut.
But there’s an even more interesting long-term trend. GenAI is about to exponentially increase the number of ideas generated and implemented. All of the necessary pieces to produce a new product or a new campaign concept are already levered by AI:
- AI models are great for brainstorming ideas, crowdsourced from our collective intelligence
- AI models can whip up creative assets easily, even strikingly realistic photography
- AI models can implement ideas in code, not just to multiply the output of engineers but even to enable less technical users to implement changes themselves
With just these existing capabilities there will be 10x more product implementations and 10x more marketing campaign concepts, which will all need to be evaluated before they are rolled out.
As the cost of ideas goes to zero, the cost of evaluating these ideas becomes the new bottleneck. Companies hoping to leverage this AI explosion will need experimentation infrastructure that can handle 10x more volume and use cases.
We raised $28M to create experimental companies
Winning companies thrive in an era of change with innovative experimentation. There’s a reason why Jeff Bezos talks about experimentation in every speech, why Netflix ran experiments as early as their DVD mailing days, why Mark Zuckerberg established AB testing on their growth team while still only operating at a handful of colleges. The companies that outcompeted and won their markets are highly experimental.
Our ambition is to change corporate culture everywhere, unleashing their best ideas with a broad experimental mindset. We’re excited to bring more partners and fresh funds to our mission.
Run an experiment in Eppo!
We’d love to show you what we’ve built. Request access to Eppo and we’ll help you get a few experiments set up.
Join the team
Our team is made up of veteran product builders from Airbnb, Snowflake, Slack, Amazon, and Stitch Fix. We’re on a mission to change corporate culture everywhere. Have a look at our open jobs. We’d love to meet you.
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