Core Concept: Randomized Experiments (RCTs) & Design-Based Analysis
- Maria Alice Maia
- Sep 2, 2024
- 3 min read
Does that 20% discount coupon actually make you money, or is it just an expensive gift to customers who would have bought from you anyway?
Answering this question is fundamental to your marketing ROI. And most companies get it dangerously wrong.
This is a classic "Doing Data Wrong" scenario I see in Marketing Departments all the time.
The Wrong Way (Observational Illusion): A team analyzes historical data. They see that customers who used a promotional coupon have a 30% higher lifetime value (LTV). They declare the coupon program a massive success and scale it up.
They have just fallen for the oldest trap in analytics: selection bias. Who is most likely to seek out and use a coupon? Your most engaged, price-sensitive, and loyal customers. These customers were likely to have a higher LTV to begin with. You haven’t measured the effect of the coupon; you’ve just confirmed that your best customers like a good deal. You are rewarding correlation, not creating causation.
The Right Way (The Power of Experimentation): To find the truth, you must stop observing the past and start designing the future. The gold standard for this is the Randomized Controlled Trial (RCT), and its logic is brutally effective.
You take a group of customers and randomly assign them into two groups. One gets the coupon (treatment), the other doesn’t (control).
Randomization is the "magic" that makes these two groups statistically identical, on average, in every conceivable way—both things you can measure (past purchases, demographics) and things you can't (brand loyalty, intent to purchase). With randomization, selection bias disappears because you, the researcher, are controlling the assignment.
Now, any difference in LTV between the two groups is the true, causal effect of your coupon. Maybe it’s only a 5% lift, not 30%. That number might be less exciting, but it’s real. It’s a number you can build a strategy on.
This brings us to a more powerful way of thinking: design-based analysis. When you run an RCT, the uncertainty in your results doesn't come from some abstract statistical theory about populations; it comes directly from the "coin flip" of your random assignment process. Your analysis is based on the integrity of your design. This is especially powerful when you're not dealing with a small sample but with your entire customer base or a large segment of it.
My experience has taught me this lesson over and over: from my academic research at Berkeley to my hands-on work driving growth at places like Stone and Ambev, the most valuable insights don't come from mining messy historical data. They come from designing clean experiments that generate causal data.
Let's make this practical.
For Managers: Challenge your teams. When they show you a correlation from past data, ask, "How can we design an experiment to prove this is causal?" Allocate a budget for real A/B tests and RCTs. The cost of a well-designed experiment is a tiny fraction of the cost of a bad decision based on confounded data.
For Data Professionals: Your most valuable skill is not just analyzing data, but designing the process that creates clean data. Be the person in the room who pushes for randomization. Your job is to replace "we think" with "we know."
This is the scientific method applied to business strategy. It’s my mission to pull this rigorous thinking out of academic papers and put it to work inside every company that’s serious about data. This knowledge isn't mine to keep.
If you believe in building strategy on a foundation of truth, not correlation, join my movement. Subscribe to my email list for more no-nonsense, research-backed insights to build a culture of real impact.
