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Experimentation budgets: Cost considerations
Here's how a single feature test at Bing generated $100 million in revenue - and they almost didn't budget for it. That header layout experiment mentioned in the original Harvard Business Review study? It drove a 12% increase in annual ad revenue, which translates to roughly nine figures for a company of Bing's scale.
Yet most companies still treat experimentation budgets as an afterthought. They'll meticulously plan every dollar for product development, marketing campaigns, and infrastructure, but when it comes to testing and learning? That gets whatever's left over. It's backwards thinking that leaves money on the table.
The importance of budgeting for experimentation
Experimentation isn't just nice to have - it's how modern companies compete. The data backs this up consistently. Companies running regular experiments see improvements across the board: higher conversion rates, better user experiences, and yes, more revenue.
But here's where it gets tricky. You need to fund these exploratory initiatives without tanking your core business metrics. Lab managers on Reddit swap horror stories about this balance - one day you're testing a promising new approach, the next you're explaining why the quarterly numbers dipped. Clinical trial coordinators face the same challenge: how do you allocate resources to find the next breakthrough while keeping existing trials on track?
The sweet spot? Marketing professionals typically recommend setting aside 5-20% of your budget specifically for experimentation. That might sound like a lot, but think of it as insurance against stagnation. Academic researchers take this even further - they're upfront about budget constraints from day one because it shapes what's actually possible to test.
The key is treating experimentation as a line item, not leftovers. Build flexibility into your allocations. Track expenses religiously. Set up contingency funds for when experiments take unexpected turns (and they will). Companies that get this right create a sustainable engine for innovation - not just random acts of testing.
Key considerations in developing an experimentation budget
Let's get practical about what actually goes into an experimentation budget. First up: you need to separate direct costs from indirect costs, and yes, there's a difference that matters.
Direct costs are the obvious ones - the specific tools, people, and resources dedicated to your experiment. Indirect costs are trickier: that's your shared infrastructure, the fraction of your data scientist's time, the overhead that supports multiple projects. Cornell's research services team has seen enough budget proposals to know this distinction trips people up constantly.
The NIH's grant guidelines spell out four criteria every cost should meet: allowable, allocable, reasonable, and necessary. Sounds bureaucratic? Maybe, but it's actually a solid framework for any experimentation budget. Can you defend why each dollar directly supports learning something valuable?
Here's what experienced practitioners actually do:
Start with historical data on similar experiments to predict resource needs
Build in 10-15% buffer for the inevitable scope creep
Track performance metrics obsessively to adjust on the fly
Communicate constraints clearly (no one benefits from surprise budget overruns)
The biotech folks managing clinical trials have this down to a science - they know exactly how much buffer to build in because they've seen what happens when you don't. Marketing teams running PPC experiments learned the same lesson: successful experiments often need immediate scaling, and you better have budget ready when lightning strikes.
Strategies for managing and optimizing experimentation costs
So you've carved out budget for experimentation - now what? The approach varies by industry, but some patterns hold true across the board.
Start small and scale what works. Marketing teams testing new channels often begin with just 5% of their total budget allocated to experiments. Once they find a winner? That percentage can jump to 20% fast. The key is having clear success metrics before you start - not figuring them out after you've burned through half your budget.
Clinical trial managers have perfected the art of detailed expense tracking. They itemize everything:
Personnel costs broken down by role and hours
Equipment and supplies with contingency buffers
Data management and analysis resources
The always-fun "unexpected complications" fund
Lab managers take a slightly different approach. They focus on forecasting future needs based on current burn rates. One lab manager on Reddit described their system: "I review our spending weekly, not monthly. By the time monthly reports come in, you've already overspent."
The communication piece matters more than most people realize. Research companies now ask about budgets upfront - not to squeeze every dollar, but to avoid wasting time on proposals that will never fly. It's refreshingly honest: here's what we can do with X budget, here's what we'd need for Y result.
Build versus buy: Cost implications for experimentation platforms
This is where things get interesting - and where many companies make expensive mistakes. The build-versus-buy decision for experimentation platforms isn't really about upfront costs.
Sure, building your own platform means no licensing fees. But let's talk about what you're actually signing up for:
6-12 months of engineering time before you can run your first real test
Ongoing maintenance that grows more complex over time
Features that third-party platforms consider table stakes (like proper statistical engines)
The opportunity cost of your best engineers not working on your actual product
The total cost of ownership analysis from Statsig breaks this down in painful detail. When you factor in delayed experiments, lost insights, and engineering hours, that "free" internal platform starts looking expensive.
Third-party platforms benefit from massive economies of scale. They're spreading development costs across hundreds of companies, iterating on features based on collective learnings, and - crucially - they're done on day one. You can run your first experiment this week, not next year.
The experimentation gap identified by data science teams shows what happens when companies underinvest in proper tooling. They want to run experiments but lack the infrastructure, so they either don't test at all or run flawed tests that lead to bad decisions.
Netflix, Spotify, and other experimentation leaders initially built their own platforms - back when good alternatives didn't exist. Today? Even companies with massive engineering resources are evaluating whether to buy instead. The math has changed: professional platforms now offer more features, better statistics, and faster iteration than most internal teams can match.
Closing thoughts
Budgeting for experimentation isn't about finding spare change in the couch cushions. It's about recognizing that systematic testing is how modern companies stay competitive - and funding it accordingly.
The companies getting this right share a few traits. They allocate specific percentages to experimentation (usually 5-20%). They track costs obsessively but aren't afraid to scale winners quickly. And increasingly, they're buying platforms rather than building them, because they'd rather run experiments than maintain infrastructure.
Want to dive deeper? Check out:
The Harvard Business Review's case studies on online experiments
Your industry's subreddit (seriously - practitioners share budget strategies more openly than you'd expect)
The best time to start budgeting for experimentation was yesterday. The second best time? Right after you finish reading this. Hope you find this useful!