Nate Montgomery had just invested in new manufacturing machinery for (code)word, and he was ready to scale. His plan was straightforward: optimize the site's conversion rate, then turn up paid advertising to drive growth.
Most merchants accept a trade-off when scaling paid ads. As you push into colder audiences, conversion rates typically decline. Nate wasn't interested in that trade-off. He wanted to maximize every advertising dollar by getting the site converting at its highest possible level before scaling spend.
He faced a complication that made this optimization push more complex than usual.
Every (code)word customer personalizes their product with custom text embroidery. An app overlay lets them see their chosen text on the product before purchase. This customizer touches nearly every transaction, making it business-critical infrastructure. But Nate needed to replace it entirely.
The existing app created operational bottlenecks. It couldn't integrate with Nate's production systems, forcing manual data entry and increasing error risk. As the business scaled, these manual processes would become unsustainable. The legacy customizer interface also likely hurt conversion rates by adding unnecessary friction to the purchase journey.
Replacing the customizer would solve the operational problems and potentially improve conversion rates. But it also introduced significant risk. Get the new version wrong, and conversion rates could crater just as ad spend ramped up. Nate needed to rebuild mission-critical infrastructure while simultaneously optimizing and scaling traffic—with no room for costly mistakes.
A Dual-Track Testing Strategy
Clean Commit, (code)word's agency partner, recommended a systematic testing approach using Shoplift. Rather than gambling on gut instinct or waiting until problems emerged, they would validate every significant change with data.
For Clean Commit, Shoplift solved a critical agency challenge: client transparency and collaboration. Nate, as a semi-technical founder, actively monitored experiment progress and performance directly in Shoplift. This eliminated the need for Clean Commit to recreate reports or schedule unnecessary status meetings. The dashboard became the single source of truth.
Clean Commit developed a testing strategy with two parallel tracks:
Track 1: Quick Conversion Wins
While the new customizer was being built, Clean Commit focused on improvements they could test and deploy immediately. The testing revealed that user-generated content wasn't prominent enough and needed to be more visible, boosting social proof. Additionally, a high percentage of visitors were scrolling to the bottom of product pages without making a selection so more product options were offered to each page.
Track 2: De-Risking the Customizer Rebuild
Clean Commit's hypothesis was straightforward: if we simplify the hat customizing process and eliminate operational friction, more customers will complete it.
Shoplift's gradual rollout capability was critical to slowly testing the new customizer before going all-in. This approach transformed a high-stakes launch into a measured, data-validated rollout.
At any given time, Clean Commit aimed to run 3-4 experiments across different areas of the store: homepage, collection pages, product detail pages, and supporting pages. Since most of (code)word's traffic volume came through PDPs (the target for paid ads), Clean Commit often stacked multiple tests on product pages to maximize learning velocity.
Testing at Scale
Over the course of the partnership, Clean Commit and (code)word ran 31 experiments. They saw 7 statistically significant winners, 17 inconclusive results, and 7 loss prevention experiments (tests that prevented bad changes). All three categories told them something useful about their ecommerce platform that helped drive conversion and inform future testing angles.
Homepage Redesign
The homepage receives almost 50% of (code)word's traffic. An experiment focused on giving visitors more entry points to start shopping resulted in a 3.4% increase in clickthrough rate. This might sound modest, but when half your traffic starts on the homepage, every additional customer taking a step down the conversion funnel represents another sales opportunity.
Suggested Text Labels
One of the simplest but most impactful tests involved providing suggested text customers could add to their hats (for example, "TEXAS"). This minor change addressed a hidden friction point: the indecision of choosing what to customize.
The results were dramatic. Page conversion rate jumped 15% overall, with mobile traffic seeing a 12.8% improvement. Since the vast majority of (code)word's visitors browse on mobile, this single test significantly impacted the bottom line.
Review-Based FAQs and Collection Page Optimization
Tests focused on collection pages introduced review-based FAQs and redesigned gallery layouts. These experiments targeted clickthrough and conversion rate, moving customers efficiently from browsing to product pages.
The Customizer Rebuild
Clean Commit's most critical test involved launching the entirely new custom app. After months of development, they used Shoplift to gradually roll out the new experience while monitoring real customer behavior.
The rebuild succeeded. The new customizer reduced friction, integrated seamlessly with manufacturing systems, and provided the flexibility (code)word needed for future growth. More importantly, testing proved it worked before committing 100% of traffic.
As Nate reflected after launch: "This will completely change our morale and backend in production and allow us to scale more efficiently."
What Made This Work: Shoplift's Role
From a technical perspective, Shoplift provided the infrastructure to test the release of an entirely new feature. As the Clean Commit team noted: "Building custom functionality and apps for clients is part of how we attack CRO. Having features to measure and control these experiments is mandatory."
The gradual rollout capability was particularly valuable for the customizer launch. Rather than a binary launch decision, they could validate the new experience incrementally, building confidence with real customer data before full deployment.
Why This Matters for Your Store
(code)word's story illustrates three critical principles for Shopify Plus merchants:
Test Before You Scale
Nate didn't just increase ad spend and hope for the best. He optimized the foundation first, ensuring every new visitor had the best possible experience. Sustainable growth without the typical conversion rate decline that comes with scaling was the result.
Validate High-Stakes Changes
Rebuilding a core product feature is risky. Testing de-risked the entire project. Instead of launching and hoping, Clean Commit proved the new customizer worked before committing all traffic. Gradual rollout transformed a potentially catastrophic launch into a measured, validated success.
Quick Wins Fund Big Bets
While the customizer was being rebuilt, smaller tests generated immediate conversion improvements. This dual-track approach meant (code)word didn't have to wait months for results. Quick wins provided immediate value while larger strategic initiatives developed.
If you're planning to scale, rebuilding critical features, or simply want to know your optimization efforts are working, systematic testing is the answer.
Glossary
A/B Testing: A method of comparing two versions of a webpage or element to determine which performs better. Visitors are randomly shown either version A (control) or version B (variant), and their behavior is measured to identify statistically meaningful differences.
Conversion Rate: The percentage of website visitors who complete a desired action, such as making a purchase. Calculated by dividing the number of conversions by total visitors and multiplying by 100.
CRO (Conversion Rate Optimization): The systematic process of increasing the percentage of visitors who take a desired action on a website through testing, analysis, and iterative improvements.
Cognitive Friction: Mental effort or hesitation a user experiences when navigating a website or completing a task. Reducing cognitive friction typically improves conversion rates by making decisions easier.
Clickthrough Rate (CTR): The percentage of users who click on a specific element (such as a button, link, or product) after viewing it.
Gradual Rollout: A testing methodology that slowly increases the percentage of traffic exposed to a new feature or experience, allowing teams to monitor performance and minimize risk during launches.
Loss Prevention Experiment: A test that reveals a proposed change would have negatively impacted performance. These experiments prevent bad changes from being implemented permanently.
PDP (Product Detail Page): The page displaying comprehensive information about a single product, including images, descriptions, pricing, and purchase options.
Statistical Significance: A mathematical threshold indicating that test results are unlikely to have occurred by chance, providing confidence that observed differences reflect real performance changes.
UGC (User-Generated Content): Content created by customers rather than the brand, including reviews, photos, and social media posts featuring products.
Testing Velocity: The speed at which an organization runs and completes experiments, directly impacting how quickly insights are gathered and optimizations implemented.
