Product Case Study:
Car Wash Soap Manufacturer
Takeaway: Sometimes, the most critical insights are the ones you weren’t even looking for. Sturdy Statistics uncovered a hidden consumer perception issue that was limiting repeat sales — with this knowledge, a simple product adjustment created more loyal customers.
The Problem: Why Weren’t Consumers Repurchasing?
A car wash soap manufacturer had built a strong reputation among professional detailers and car show enthusiasts — a clear endorsement of product quality. However, their consumer market was struggling. Advertising campaigns generated first-time buyers, but few repurchases. Reviews suggested consumers found the product “less effective” than competitors, but even after manually reviewing feedback, the company couldn’t explain the difference in perception. If professionals loved the product, why didn’t everyday consumers?
How Sturdy Statistics Uncovered the Hidden Consumer Bias
We analyzed all of the manufacturer’s product reviews alongside competitor reviews, structuring each into key themes. A simple quantitative analysis revealed a striking insight: The issue wasn’t what consumers were saying — it was what they weren’t saying.
In highly rated competitor reviews, a dominant theme was “suds and foam.” Consumers loved the visual feedback of foam and equated thickness with cleaning power. However, this topic was entirely absent in our client’s reviews.
The Fix: Introducing a Foaming Formula for Consumers
The company’s soap didn’t foam by design — their chemists saw foam as an unnecessary quality that didn’t affect cleaning and required an extra rinse. Professionals didn’t mind, but consumers saw the lack of foam as a sign of poor performance.
Armed with quantitative proof, the company launched a foaming product line. The new formula matched consumer expectations, leading to higher retention. The company no longer lost repeat customers over a simple perception issue.
The Impact: Turning an “Unknown Unknown” Into a Competitive Advantage
Before using Sturdy Statistics, the manufacturer had no way of knowing that a misalignment between consumer expectations and product design was the root cause of their retention issue. The data was always there — but only Sturdy Statistics surfaced it.
What will Sturdy Statistics help you discover in your data?