About
At Hero Bread, the eCommerce team was running storefront tests to improve conversion and customer quality. The problem was measurement: if every test requires a new spreadsheet and a new debate about which numbers are real, you can’t scale experimentation.
“We have grown rapidly in terms of run rate and where we stack up from a channel perspective compared to 18 months ago. We are the top seller in four of our five categories on Amazon.” — Christina Blaisdell Oyler, VP of eCommerce at Hero Bread
SourceMedium gave them a consistent baseline for storefront performance, so the team could compare tests apples-to-apples and make clear calls on what to scale and what to kill.
Hero Bread is a baked goods brand offering ultra-low net carb products for online orders and retail distribution.
The challenge
- Storefront tests were easy to launch, but hard to compare consistently across channels and landing pages
- Decisions on what to scale depended on downstream metrics (AOV, CAC, LTV), not just top-line conversion
- Manual extraction and spreadsheet workflows didn’t scale as the number of experiments grew
Storefront testing is easy. Measuring it consistently is not.
Hero embarked on its eCommerce journey in early 2022, marking its first introduction to customers across the United States. With a growing product line and rapidly growing customer base, Hero sought to enhance the consumer experience with optimized storefronts.
To make those storefront changes worth the effort, the team needed a consistent way to answer questions like:
- Are these new experiences actually improving conversion and customer quality, or just shifting mix?
- What is the performance baseline we trust across channels and landing pages?
- If we scale a test, which downstream metrics are we protecting (AOV, CAC, LTV)?
They used SourceMedium to unify measurement across repeat behavior, LTV, and cohort patterns so the team could evaluate tests on the same definitions, not competing dashboards.
“Our eCommerce growth was impressive, but manually extracting and analyzing data was time-consuming. We needed a solution to streamline data analysis and provide actionable insights to keep growing.”
The solution
- One consistent definition set and drill-down for storefront performance (so tests were comparable)
- A repeatable reporting workflow for test vs. non-test pages, without rebuilding in spreadsheets
- A faster operating cadence: measure the same way, then keep or kill quickly
A repeatable way to test, compare, and scale storefront experiences
Hero used Fermat to build and run storefront experiences, then used SourceMedium to measure those tests against a consistent performance baseline.
With SourceMedium, the team could look at storefront performance by the metrics they care about (CVR, CPA/CAC, ROAS, AOV) and compare test vs. non-test pages without rework.
As the number of experiments grew, having one source of truth made it easier to compare experiences, see what worked, and decide what to scale across channels.
Over time, this turned storefront testing into an operating loop: build a new experience, measure it the same way, then keep or kill it quickly.
“Once we had a consistent measurement baseline, we could look at storefront tests and actually learn something. It made decisions around merchandising and offers much more straightforward.”
What changed
- Decision enabled: The team could decide which storefront experiences to scale, and which to retire, based on performance instead of gut feel.
- How they validated it: Storefront-level measurement (CVR, CPA/CAC, ROAS, AOV) made tests comparable and reduced debate.
- What got faster: Faster iteration loops on messaging and merchandising while maintaining a consistent performance baseline.
The results
-
10-15% month-over-month growth
Hero sustained 10-15% month-over-month growth while expanding its eCommerce motion. -
Top seller in 4 of 5 Amazon categories
The brand became the top Amazon seller in four of its five categories. -
Higher AOV vs. general purchasers
Storefront experimentation helped improve average order value (AOV) compared to general purchasers.
With a consistent measurement baseline in SourceMedium, the Hero team could roll out storefront tests, understand what changed, and keep iterating without re-litigating the numbers every time.