10 experiments with NDA for a gifting brand. Clear impact.
Published 60+ long-form articles (2K words avg.)
After running 45 experiments
(with the support of 200 people)
Skipping the most critical step: understanding user behaviour. Without qualitative and quantitative research, tests become random guesses dressed up as strategy. You end up optimizing the wrong problems, chasing insignificant gains, or worse, hurting conversion. Real experimentation starts with profound insight into user intent, friction points, and decision triggers, not assumptions.
Running tests without a clear hypothesis turns experimentation into noise. A button color change or layout tweak without a defined “if this, then that, because” framework leads to inconclusive or misleading results. A strong hypothesis connects user psychology, business goals, and expected outcomes. Without it, even a “winning” test teaches you nothing and cannot be scaled or repeated.
Declaring winners too early or relying on gut over data is one of the fastest ways to destroy trust in experimentation. Without statistical rigor, sample size, significance thresholds, and controlled variables, results are unreliable and often false positives. This leads to poor decisions being rolled out at scale. True experimentation requires patience, discipline, and respect for data integrity.
Not all experiments are equal, but most teams treat them that way. Without a prioritization system, high-impact opportunities get buried under low-value tests. Teams waste time on cosmetic changes instead of solving core business problems. A structured prioritization model ensures every experiment is aligned with impact, confidence, and effort so you focus on what actually drives revenue.
We don’t run random tests.
We build a structured experimentation system that identifies where your website is losing revenue and fixes it.
1. Identify where you’re losing growth
We start by understanding your key metrics and user behavior using heatmaps, session recordings, and funnel analysis.
2. Turn insights into high-impact experiments
We analyze the data to uncover what’s happening, why it’s happening, and what to do next — forming clear, testable hypotheses.
3. Run statistically valid A/B tests
Your team implements the experiments, and we ensure they run long enough with sufficient data to produce reliable results.
4. Scale what works
Winning experiments are implemented, learnings are captured, and your KPIs evolve — creating a system of continuous growth.
