Most stores change things and hope.This one measured
A US stair components retailer
The Brief
A Shopify store selling stair components — steps, wooden posts, rails, and related products. The retailer wanted a structured optimization program rather than isolated changes: an audit to find what was holding conversions back, the fixes implemented, and clear evidence that those fixes moved the numbers.
That last requirement is the one that shapes everything. It is easy to ship a list of improvements and describe them afterward as a success. It is harder, and much more useful, to be able to point at what the store was doing before and what it did after.
The store also needed to understand where it sat against competitors, and to run promotional sales — all of it grounded in real data rather than assumption.
Measured, not assumed
What the program produces, and why each piece exists.
| Instead of | The program used |
|---|---|
| Shipping fixes and describing them as a win | A baseline captured before any change, compared after |
| Guessing where visitors give up | Clarity session recordings of real behavior |
| Optimizing against a best-practice checklist | A competitive analysis of the actual market |
| A single round of fixes | A second round drawn from the competitive report |
The Approach
We started with a CRO audit to identify the concrete friction points, then implemented what it surfaced.
Before touching anything, we captured a baseline. That sequencing is the whole point. Once fixes are live, the pre-change state is gone, and any claim about impact becomes an argument rather than a measurement. Recording it first is a small amount of work that decides whether the rest of the engagement can be evaluated at all.
From there we layered in Clarity session analysis to watch real user behavior, ran a competitive analysis to benchmark the store against its market, and used that report to drive a second round of fixes — alongside planning and launching a sale.
Before
After
What we Did
Ran a CRO audit and worked through the fixes. The friction points identified across the store, addressed rather than filed as recommendations.
Recorded a before-and-after baseline. Captured ahead of the fixes so their effect could be measured directly instead of estimated afterward. This is the piece most optimization work skips, and skipping it is why so much of it cannot be evaluated.
Ran Microsoft Clarity session analysis. Where visitors dropped off, and what they were doing immediately before they left. Those insights directed the work rather than sitting in a report.
Conducted a competitive analysis. How the store compared against its competitors, establishing market position instead of optimizing in isolation.
Before
After
Implemented a second round of fixes from that report. The analysis was not filed as a document. It produced work.
Planned and launched a sale on the store. Promotional activity run as part of the engagement rather than handed back as a suggestion.
Results in Detail
A before-and-after baseline captured, making the impact of the fixes measurable rather than assumed, and providing an evidence base for every claim that follows.
This is the result the retailer can still use after the engagement ends. A store with a recorded baseline can evaluate its next change, and the one after that. A store without one is permanently reliant on whoever did the work to tell it whether the work succeeded.
Before
After
The CRO audit is complete, with fixes implemented across the store, and a before-and-after baseline was captured and compared to measure the impact. Clarity session analysis identified drop-off points from real user sessions, while a competitive analysis benchmarked the store against its market. A second fix round followed, drawn from the competitive report, and a promotional sale has been planned and launched and is now live.
Tools & technologies
What the build runs on
verified