Survivorship Bias: Lessons from Business Failures

1. What is this in one sentence?

Survivorship bias is the mistake of focusing only on successful examples while ignoring the many failures that never made it.

2. What it means to businesses

If you only study the companies, products, or campaigns that “worked,” you risk missing the bigger picture and making decisions based on incomplete data.

3. Customer opportunity

Customers are telling us just as much with the products they don’t buy as with the ones they do. Retailers who listen to the silent signals—cart abandonment, trial without repeat, returns—gain a truer picture of demand.

4. Business threat

By copying only “winners,” businesses risk repeating flawed strategies, over-investing in the wrong areas, and failing to spot unmet customer needs hidden in the “failures.”

5. Business examples of this effect

• Fashion retail: A retailer stocked more neon products after seeing high sell-through of one bright pink jacket—ignoring that 90% of other neon items failed.
• E-commerce: A platform highlighted top 5-star reviews in ads, overlooking the large number of neutral 3-star reviews that pointed to product quality issues.
• Food & beverage: A chain expanded a “bestselling” seasonal drink, but missed that most customers tried it once and never reordered—success was an illusion created by first-time trial.

6. How can we use data to maximise this effect?

• Track both successes and failures—log why items are discontinued, why customers churn, and why campaigns underperform.
• Build denominator awareness—don’t just ask “what worked?” but also “out of how many tries?”
• Use customer-level data (repeat purchase, basket analysis, browsing drop-offs) to reveal whether success was sustainable or just surface-level.
• Test new ideas on smaller segments, and record both positive and negative outcomes to refine future bets.
  
  

For retailers, survivorship bias is most useful when flipped: instead of celebrating only the visible winners, use it as a lens to uncover blind spots in customer behaviour. The missed signals in what didn’t survive often hold the richest insight.