In marketing, we love a good winner. The subject line with the highest open rate, the Facebook ad with the lowest cost per click, the CRM email that tripled conversions. We celebrate these wins, hold them up in pitch decks, and tell ourselves they’re proof of what works. Success becomes the story we tell.
But here’s the thing: if you only focus on the winners, you’re falling into a classic cognitive trap: survivorship bias.
This bias sneaks in when we base our decisions only on the campaigns that performed well, ignoring the ones that didn’t. It’s like judging a cooking contest by only tasting the dishes that made it to the finals. You’re missing out on everything that went wrong in the kitchen. And that’s where the real lessons are.
Understanding survivorship bias
Survivorship bias is the mistake of looking only at outcomes that “survived” a process. It creates a false picture of reality by excluding failures. Abraham Wald famously corrected this during WWII by showing the military that the real vulnerabilities of planes were in the parts where returning aircraft had no bullet holes. Because those hit in critical spots didn’t make it back.
In business and marketing, this kind of thinking is just as dangerous. It leads to copying what appears to work while ignoring the full context and complexity underneath. You end up optimizing for outcomes that may not be reproducible. Or worse, misleading.
Where It Shows Up in Marketing
- Social Media Ads: You might think your top-performing ad creative is your golden ticket. But did you look at why the other 9 versions underperformed? Maybe one was too wordy for mobile. Another might have hit the wrong mood during a global news event. Sometimes, your ad failed not because it was bad, but because your audience was tired, distracted, or simply not in the mood to buy. The real insight is in what didn’t work and why. Dissect the losers with the same intensity you dissect the winners.
- Paid Search: Pausing underperforming keywords too early could cost you long-term value. A low-CTR keyword might be a sleeper hit, bringing in fewer but better-qualified leads. Or it could perform better on weekends, in different geographies, or with a different landing page. Before killing a keyword, ask: have I tested enough variables around it? Always tie performance to the full customer journey, not just the first click.
- CRM Campaigns: High open rate? Nice. But what was the actual revenue per send? Maybe the “winner” got opens but no conversions, while a less flashy subject line quietly drove sales. And consider the long tail. Did a particular campaign increase unsubscribes? Did it impact deliverability on future sends? Timing, frequency, and audience fatigue all play roles. Don’t let a vanity metric call the shots. Optimize for value, not applause.
- Ecommerce: Testing product pages? Your A/B test winner might’ve coincided with payday, a flash sale, or a viral TikTok. Did you isolate the variable you were testing, or were multiple factors at play? If you don’t repeat the test in controlled conditions, your data’s wearing a costume. Also, look beyond the winner. What changes might improve the losing variation? Treat all tests as temporary insights until proven otherwise.
- Content Marketing & Landing Pages: Survivorship bias creeps in when we assume the blog post that got shared most is also the most effective. But was it shared by your ideal customers, or just the general internet? Did those shares drive leads, time-on-page, or email signups? A popular piece isn’t necessarily a profitable one. Look deeper.
Five Ways to Keep Yourself Honest
- Study What Failed: Every flop contains data. Maybe it was the CTA, the image, or the time of day. Catalog what didn’t land. Look for patterns across failed variants. You’ll learn faster by studying what people ignored than what they clicked.
- Track Full-Funnel Results: That killer ad might get clicks, but does it convert? Always follow performance down the funnel: clicks, add-to-carts, purchases, and repeat buys. Build dashboards that show the journey, not just the starting line.
- Segment By Behavior, Not Just Demographics: Testing only by age or location is surface-level. Group by behaviors like number of sessions, previous purchases, scroll depth, or dwell time. That’s where you’ll uncover how motivation, familiarity, or loyalty shape performance.
- Let Tests Run Long Enough: Resist the urge to crown a winner after 48 hours. Wait for statistical significance and understand your sample size. Early data is often misleading. Peak hours, anomalies, or outliers can distort.
- Document Context: Was your test affected by a holiday? A PR mention? A server outage? Keep a journal of external conditions so you can interpret spikes and slumps accurately. Context helps you separate noise from signal.
Final Thoughts
A/B testing isn’t magic. It’s messy, iterative, and if you’re not careful, deceptively biased. Survivorship bias tricks you into thinking you’ve found the answer when really, you’ve found an answer. One slice of a bigger pie.
Marketing is full of ghosts. The tests we forgot. The campaigns we buried. The emails we never sent. Ignoring them doesn’t make you smarter. It just blinds you. You can’t optimize what you pretend didn’t happen.
Don’t build your strategy on half the story. Pay attention to what flopped, flailed, and fell flat. That’s where the truth lives. That’s where your edge is.
Because when you see everything, you make decisions that actually work. Durable decisions. Smart, repeatable decisions.
And in a world full of noise, that’s how you really get ahead. With your eyes wide open, your ego turned down, and your learning turned way up.
