Premium Travel Equipment Brand Shopify CRO Case Study: Results After 6 Months

The Insight

The brand was preparing for a major evolution of its product range. Its commercial ambition extended beyond increasing order volume: the store needed to support more purchases of premium travel bags, bundles and connected product ecosystems.

This mattered because the brand had limited scope to use public discounting as a conversion lever. Sustainable improvement needed to come from making the range easier to understand, strengthening purchase confidence and helping customers find the right route through the store.

Blend’s CRO Audit & Roadmap showed that the store was already attracting meaningful intent. More than 55% of sessions began on product pages, but those pages converted at only 1.25%. The largest loss occurred before customers added a product to their cart, while shoppers who entered checkout converted comparatively efficiently.

Mobile made the opportunity more commercially significant. It accounted for 76% of traffic, yet desktop customers converted 51% better and recorded a 20% higher Add to Cart Rate. Search users converted 120% above the site average, but only 1% of visitors used search. Returning visitors generated 52.7% of revenue from just 22% of traffic.

These signals pointed to a connected journey problem. Customers had intent, but mobile discovery, product understanding and decision support were not consistently helping them act on it.

From Audit Findings to a Prioritised CRO Programme

Blend’s hypothesis was that Revenue per Session could be improved by reducing friction at the moments where customers selected a category, assessed a product or decided whether to proceed.

The recommendations were ranked using Blend’s PECTI prioritisation framework. This allowed the team to weigh commercial impact alongside supporting evidence, implementation effort and the reach of each change.

The resulting roadmap combined direct implementation with controlled experiments. Sticky product-page actions, persistent mobile navigation, collection-page usability and product presentation could be improved directly. Changes to category navigation, value communication, payment options, search and recommendation logic were tested so decisions could be based on observed customer behaviour.

Making Mobile Discovery Easier

Mobile visitors needed to navigate a technically detailed product range without repeatedly backtracking or losing access to important controls. Blend implemented sticky collection filters and sorting controls so customers could refine the range while browsing. Menu, search and cart actions were also kept accessible during scroll.

A redesigned mobile category menu was tested to make the product range easier to scan visually. During the test period, the variant increased Average Order Value by 3%, Checkout Begin Rate by 4% and Revenue per Visitor by 1%, although Conversion Rate declined by 2%.

The experiment did not reach statistical significance, so Blend treated the result as directional. The movement across the funnel nevertheless suggested that clearer category access was helping some customers build higher-value journeys and progress further towards checkout.

Supporting Product Decisions

Product pages carried unusual commercial responsibility because more than half of visitors entered the store through a PDP. Blend implemented a sticky Add to Cart experience so the primary purchase action remained available while customers reviewed specifications, imagery and longer-form product information.

Important delivery, returns and guarantee messages were also moved closer to Add to Cart. In the resulting test, Revenue per Visitor, Average Order Value and Checkout Begin Rate each increased by approximately 2%, while Conversion Rate remained unchanged.

The movement did not reach weekly statistical significance, so it was not presented as a conclusive causal win. Its value came from the relationship between the metrics: the additional reassurance did not create more Add to Cart actions, but it appeared to support higher-value purchase decisions among customers who continued.

The complete experiment and its limitations are documented in the related PDP value-proposition A/B test case study.

Using Segment Data to Protect Performance

Blend also tested how express checkout and standard payment options were presented on product pages. The aggregate results initially suggested that the alternatives should be rejected, but segment analysis revealed a more useful finding.

One variant increased mobile Revenue per Visitor by 2%, Conversion Rate by 1% and Average Order Value by 1%, while both desktop variants underperformed the control. The recommendation was therefore to retain the original desktop treatment and apply the alternative only on mobile.

This protected the stronger desktop journey while introducing the change where the evidence supported it. It also demonstrated why storewide averages can hide commercially important differences between customer groups.

Improving Confidence Across Collections, Homepage and Cart

The next stage of the programme extended the same strategy across the wider journey. A mobile collection-page test allowed customers to view multiple product images without opening each PDP. Revenue per Visitor increased by 4%, Average Order Value by 2% and Add to Cart Rate by 2%. The result remained directional rather than statistically conclusive, so rollout was paired with continued monitoring.

On selected product pages, bringing a featured customer review closer to the decision area produced a 16% increase in Conversion Rate and an 18% increase in Revenue per Visitor when analysis was limited to the products and landing pages where the experience was implemented. Blend kept the interpretation within that tested scope rather than applying it to the entire catalogue.

Homepage value propositions also improved progression. The tested treatment increased Revenue per Visitor by 7.76%, Conversion Rate by 5.82% and collection-page visits by 4.79%, suggesting that clearer early-stage messaging helped visitors understand the offer and continue browsing.

Cart work focused on both progression and basket relevance. A Rebuy cart-drawer test increased Conversion Rate by 6% and Checkout Begin Rate by 10%. Revenue per Visitor and Average Order Value were not compared directly because the two experiences used different product-recommendation logic. This prevented an unreliable attribution from being presented as a commercial conclusion.

Learning What Not to Roll Out

Not every promising idea improved the north-star metric. Exposing the mobile search bar increased Conversion Rate by 6.53%, but Revenue per Visitor fell by 3.77% and Average Order Value by 9.67%. The result did not reach significance, and the broad treatment was stopped.

Returning visitors had responded more positively, so Blend recommended a follow-up focused on that audience instead of applying the experience to every mobile visitor. The decision preserved the learning without accepting weaker overall commercial performance.

A colour-swatch experiment provided another nuanced result. Revenue per Visitor and Conversion Rate both increased by 9%, while swatch and Quick Buy interactions declined. Because the commercial movements had not reached statistical significance, Blend recommended a measured rollout with monitoring rather than presenting the result as a proven win.

Building Towards Higher-Value Purchases

The programme also developed product-bundle and recommendation strategies to support the brand’s wider move from accessory-led orders towards bags and connected product ecosystems.

This work formed part of an ongoing CRO implementation programme rather than a collection of unrelated site changes. Each recommendation was tied back to product discovery, purchase confidence, basket value or Revenue per Session.

Across the six months, the combination of audit insight, prioritised implementation and controlled testing gave the brand a clearer way to decide what should be rolled out, adapted for a specific audience or stopped.