Premium Children's Swimwear Built on Quality and Consistency
Founded nearly a decade ago, Minnow carved out a distinctive position in the children's swimwear market by offering timeless, solid-colored swimwear that combined quality construction with classic design. What started as a solution to a gap in the market grew into a full lifestyle brand, expanding from their flagship children's swim collection into men's and women's swimwear, knits, coverups, and everyday essentials.
Based in Charleston, South Carolina, with a flagship store on King Street, Minnow built their reputation on premium positioning and brand consistency. They only went on sale twice a year, a deliberate strategy that protected their brand equity and maintained customer perception of value. Their customer base, primarily mothers in their 30s through 50s and grandmothers purchasing for grandchildren, proved remarkably loyal, often returning year after year as children grew and families expanded.
But in early 2025, rising tariff costs created an opportunity to rethink their pricing strategy.
When Tariffs Prompted a Closer Look at Pricing
The new tariff regulations meant Minnow's upcoming collections would cost more to produce and receive. For Sophie Pilkington, Senior E-commerce Manager, and her COO, this made them consider whether they were pricing their products optimally, or was there room to adjust?
For Minnow, the question wasn't whether to cut corners. The materials, craftsmanship, and attention to detail that defined their brand were non-negotiable. The real question was whether their pricing reflected the value they delivered, and whether there was room to protect the quality their customers expected without absorbing costs that would force unacceptable tradeoffs down the line.
"One of the things that we realized was how far we could go to really push the needle on certain prices for products that are very popular until we see erosion in our conversion rate," Sophie explained.
The team had seen some encouraging signals. Earlier in the year, a collaboration with Liberty had featured higher price points than their typical collection, and customers converted just fine. Sophie and her COO noticed that their customer base seemed less price-sensitive than they might have assumed. But observing a pattern and having data to support pricing decisions were two different things.
They wanted to understand, with some confidence, where their customers' price threshold actually was. Testing would let them find the balance between offsetting tariff costs and maintaining healthy conversion rates.
Minnow was already using Shoplift for A/B testing their website, and the platform had recently launched a price testing feature. It seemed like a natural fit.
A Methodical Approach to Finding the Right Price Points
Rather than implementing changes across their entire catalog, Sophie and her team decided to test systematically, starting with products they knew well.
First on the docket were their top-performing categories, the bestsellers that often served as the first items customers purchased when discovering the brand. Testing these products first made sense. If there was price sensitivity in their catalog, it would likely show up here. And if these items could handle a price increase without affecting conversion, it would inform their approach to other categories.
The test was simple: a 10% price increase on these core items, with close monitoring of conversion rates. If customers continued converting normally, they'd know there was room to optimize. If conversion started dropping, they'd catch it before expanding to other products.
Sophie found the setup process to be straightforward. The preview feature was helpful during that first test. She noticed an issue where the new price displayed correctly on the product page but showed the original price when items were added to cart. "I was able to reach out to your team on Slack and someone was able to just get on a huddle with me and screen share and adjust the code so that it reflected across multiple areas," she explained. After that initial code adjustment with help from her developer, Sophie could set up subsequent tests on her own, using the preview feature to check that pricing displayed correctly throughout the purchase flow.
The process was quick. After the initial developer setup, Sophie could launch new price tests herself and monitor them like any other A/B test they ran through Shoplift.
The Data Validated Their Hypothesis
The results came in clearly: customers converted at their normal rates with the 10% price increase.
This confirmed what the team had suspected about their customer base and brand positioning. The customers shopping at Minnow valued quality and timelessness. A 10% increase on products they already considered premium didn't change that value equation.
For Sophie and her COO, this data removed the guesswork. They had concrete evidence that they could adjust pricing to protect margins while maintaining their conversion rates.
"We're going to continue working through every category to find the right balance on price point," Sophie explained. The plan was systematic: test 10% increases across all categories first, evaluate results by category since different products might respond differently, then return to test whether some categories could support additional increases.
What started as a response to tariff increases became a more comprehensive approach to pricing optimization, one that uses data-driven signals to adjust prices in either direction.
The successful tests gave Minnow's team a framework for thinking about pricing differently. Rather than treating prices as relatively fixed except for their twice-yearly sales, they now had a way to evaluate whether their pricing fully reflected the value customers placed on their products.
Making Decisions Based on Data
When tariff costs increased, the team could have either accepted narrower margins or made pricing changes based on assumptions. Price testing gave them a third option. They could now make decisions based on data about their specific customers and products.
"I don't think tariffs are going away anytime soon," Sophie acknowledged. But now Minnow had a systematic way to respond, grounded in evidence rather than guesswork.
The approach fit well with what Minnow had built over nearly a decade. Their premium positioning, their selective discounting strategy, their focus on sustainable profitability, all of it benefited from understanding the actual value customers placed on their products. Price testing gave them that understanding in concrete terms.
Sophie put it simply: "We just wanted to see where we could adjust prices to reflect the value Minnow delivered and maintain our level of quality in the new tariff environment."
