with clearer product selection
DRIP combined customer research with experiments tracked in Apex to build a structured testing program. Clearer product selection increased purchases by 6.5%, while negative results informed which changes to stop.
A free 30-minute call with the team. That's it.
¹ The application-time matrix was classified as a winner through a manager decision. Each percentage shows the purchase uplift against its respective control. The uplifts should not be added together.
The short version
A growing DTC beauty-tech brand wanted a more systematic approach to shop optimization. Together, we connected customer research with an ongoing testing roadmap. Two practical decision aids showed positive results: a compact application-time overview and mobile product selection based on shoppers' priorities.
The team was already eager to test. They had tried changes themselves and seen some positive results. But experiments were sometimes stopped after just three or four days. Results were documented inconsistently, making earlier tests difficult to revisit.
The buying journey was also complex. A long product detail page, multiple variants and bundles, and a high share of mobile traffic created specific challenges for navigation and decision-making. Campaigns, influencers and seasonal demand added further fluctuations.
Which changes actually help shoppers buy? And which positive movements are only temporary? Answering those questions required clear hypotheses, traceable results and enough time for each experiment to run.
The content challenge was equally specific. Shoppers needed to understand the price, what results to expect and how the application would fit into their daily routine. Those questions became the basis for optimization.
Our process
DRIP's method in three steps, and what each step looked like at the beauty-tech brand.
Step 1
Understand what makes the buying decision difficult
Step 2
Turn insights into testable changes
Step 3
Multiple experiments with a clear process
Step 1
Understand what makes the buying decision difficult
We brought together customer reviews, post-purchase surveys, social media comments and shop analyses. Qualitative and quantitative funnel data, along with a GA4 analysis, added context.
The central pattern was a combination of price concerns, uncertainty about effectiveness and a lack of concrete supporting evidence. That gave the roadmap four priorities: explain the value, clarify expected results, reduce perceived purchase risk and make application easier to plan.
Input
Reviews, surveys, comments and funnel data showed which questions remained unanswered before purchase.
Insight
Price, expected results and application needed to be considered together.
Output
Customer questions shaped a roadmap for clearer information and better decision support.
Step 2
Turn insights into testable changes
Each experiment had a clear hypothesis and documented variants. The roadmap connected customer research, shop data and previous test results. That made it clear which uncertainty a change addressed and how its success would be measured.
Weekly reviews used the results recorded in Apex to decide whether to adopt a change, stop it or develop another variant. Looking at mobile and desktop separately helped reveal differences in buying behavior.
Hypothesis
Every test addressed a specific obstacle in the buying decision.
Evaluation
Clicks alone were not enough. Purchases, revenue and revenue per visitor informed the evaluation.
Iteration
Positive, negative and inconclusive results improved the next hypothesis.
Step 3
Multiple experiments with a clear process
Four experiments were running in parallel within the first month. A consistent process covering design, development and quality assurance brought the variants into the shop. Longer runtimes replaced decisions made after only a few days.
The focus was on specific changes in the buying journey: clearer application information, easier product selection and more focused trust messaging. Each variant was evaluated in an experiment before it could become a permanent change.
Scope
Tests addressed individual questions and obstacles in the purchase journey.
Build
Documented variants and quality assurance kept implementation traceable.
Decide
Evaluation considered the downstream funnel and differences between devices.
Four questions shaped the roadmap. They generated the test ideas, and experiments showed which answers helped shoppers in practice.
Make the benefits and differences between product options easy to understand.
Bring specific product information and credible supporting evidence into the buying decision.
Place trust information deliberately and test its effect.
Make the time commitment concrete and easier to plan.
Two positive results and one negative test show why practical decision support needs controlled experimentation. The first two show the results in Apex; the third visual is schematic.
Test 1 of 3
How long does application take? The variant replaced a general statement with a compact matrix showing application times for different areas. This helped shoppers judge the time commitment more precisely. The tested code was subsequently handed over for permanent implementation.
Test 2 of 3
The variant let mobile shoppers choose between application time, value for money and the overall package. It highlighted the matching product option while keeping the other options visible. This supported different purchase priorities.
Test 3 of 3
A more prominent money-back guarantee was intended to reduce perceived purchase risk. Instead, the experiment reduced the purchase rate. The variant was stopped and was not permanently implemented. The result informed a new direction for the roadmap: build confidence through more concise trust cues.
How Apex supported the decisions
Apex made the experiment pipeline and outcomes visible in one place. The team could distinguish running tests from decided results and see which experiments were marked as winners, losers or inconclusive. Manager decisions were labeled separately.
The mobile product-selection experiment was classified as a winner with 6.5% more purchases. The application-time matrix showed a 3.2% uplift and was classified as a winner through a manager decision. A more prominent guarantee reduced purchases by 9.2% and was not rolled out. Together, these results informed what to adopt, what to stop and what to test next.
The value extended beyond finding positive changes. Testing exposed a harmful variant before it became a permanent shop change. The result then informed a more focused approach to trust messaging.
Priority-based choice on mobile
Tested code handed over for permanent implementation
Stopped, not rolled out
Mobile product selection delivered a 6.5% purchase uplift. The compact application-time matrix showed 3.2% more purchases and was classified as a winner through a manager decision. Testing also prevented the negative guarantee variant from becoming a permanent shop change.
DRIP's research and testing process, with pipeline and outcome visibility in Apex, gave the team a clearer basis for follow-up decisions. Each result contributed to the next round of optimization.
For this beauty-tech brand, clearer application information and product selection based on personal priorities showed positive results. More prominent guarantee messaging reduced purchase performance.
Customer questions set the direction. Controlled experiments test the implementation. Apex keeps the pipeline and outcomes visible so the team can use each result to make the next decision.
Book a call with the team and tell us about your shop. We'll talk through where it stands, where you want it to go, and how we'd get you there. If working with us isn't the best return on your money, we'd tell you rather than take it.
30 minutes, and you'll know exactly where your shop stands.
