Benjamin Degueldre

EXPERIMENTATION RED FLAGS 🚩: # 17 → Experimentation Velocity "We need to run more experiments!" How many times have you heard this in your marketing meetings? It's like a siren song that's hard to resist. After all, more experiments should mean more insights, more wins, and faster growth, right? Right??? Focusing solely on experimentation velocity can be a dangerous trap. (as I posted yesterday) Why: 1. Quantity over quality 2. Analysis paralysis 3. Implementation bottlenecks 4. Increased error rate (Remember my red flag about QA from a couple months back?) 5. Loss of focus: Real-World Example: A B2B SaaS client came to us last year, proud of their "high-velocity" experimentation program. They were running 20 experiments per month. But... not all was well! The problem? - 83% of their experiments showed no significant results - Their North Star metric hasn’t really moved in 6 months - The team was burnt out and losing faith in experimentation After digging deeper, we found most experiments were superficial UI changes, there was no proper prioritization framework, analysis was rushed, and the team was spread too thin to conduct proper user research. Here's how we turned things around: 1. Implemented a rigorous prioritization framework: We introduced our own scoring to ensure only high-potential experiments were run. 2. Focused on high-impact areas: Instead of tweaking button colors, we shifted focus to paid landing pages, core user flows, pricing strategy, and value proposition testing. 3. Invested in deep analysis: We reduced experiment volume by over 60% but increased analysis time per experiment by more than 200%. 4. Integrated user research: Before running experiments, we conducted user interviews and surveys to inform our hypotheses. This wasn’t really done in any systematic way before. 5. Implemented learning cycles: After each experiment, we dedicated time for team reflection and documentation of insights. The Results (after 6 months): - Experiment volume decreased from 20 to ~8/month. (now slowly picking up pace again) - "Win" rate increased from ~14% to ~36% - Overall conversion rate improved by 22% - Team morale and confidence in the program skyrocketed (this one is subjective but that’s my feeling anyway) Key Takeaways: 1. Quality trumps quantity 2. Velocity can be a vanity metric 3. Invest in analysis 4. Balance is key 5. Align with strategy Every experiment should tie back to your core business objectives. Remember, experimentation is not about how fast you can run tests. It's about how quickly you can learn and adapt. Sometimes, slowing down is the fastest way to accelerate your growth. Next time someone pushes for "more experiments," ask them: "Are we running the right experiments, or just more experiments?" Just a touch of common sense.

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