SolutionseCommerce Growth

Topline CVR cannot tell you what to fix. The behavior underneath it can.

Most stacks track pageviews and orders, then argue about everything in between. We instrument the site down to the module and the position, stitch behavior across sessions, and read tests, promos and merchandising with the rigor a revenue decision deserves.

The foundation nobody sees

Instrumentation that can tell a hero from a carousel.

The homepage is where tracking usually gives up: modules with no events, positions nobody records, and a redesign debate with no data on either side. A real tracking plan gives every component its own event and properties, built once with engineering so new modules inherit the schema.

Tracking plan · excerptEvery event QA'd with engineering
Product Viewed
Product funnel
product_idvariant_idpricecolor_displayedconfiguration_displayedrating_countship_timebadge_text
Product Tile Viewed / Clicked
Anywhere a product appears
locationlist_positionsection_titlevisible_on_pageloaddisplayed_imagecolor_selectedprice
Content Module Viewed / Clicked
Homepage & landing modules
module_typemodule_positionmodule_nameelement_typeelement_positionvisible_on_pageload
Navigation Item Clicked
Top of funnel journey
titlelevelis_mobileis_collectionis_productbadge_text
This is what makes the rest of the page possible. "Does the hero earn its slot" and "what does the rec module actually add" become queries instead of debates, and every new module ships reporting itself.

Excerpt of the shape. The full plan covers the whole funnel, with definitions and QA status per event and property.

Journeys, stitched or not

The same buyers, two opposite conclusions.

A real read from this work: look only at the purchase session and customers seem to skip discovery entirely. Stitch the same people across ~2 weeks and discovery turns out to be doing the selling.

Session-level read
Most purchase sessions are short and focused: land near the product, buy.

Roughly a third of buying sessions never leave the product page, and most of the rest touch only a page or two. Browsing barely shows up.

Conclusion drawn: nobody browses. Cut the discovery content, double down on the product page.
Stitched across sessions, ~2 weeks
The same buyers explored the homepage, collections and several products before that final visit.

Across the full journey, the majority touched homepage, listing pages and multiple product pages. The final session is where the decision lands, not where it is made.

Conclusion drawn: discovery is doing the selling. Invest in it, and stop judging content by last-session behavior.

Same warehouse, same buyers, opposite roadmaps. The difference is whether the tracking can stitch a person across sessions. And once it can, personalization stops being a guess: one brand's hero engaged ~20% of visitors overall but ~3% of visitors arriving from its loungewear ads; a hero matched to the ad took that segment to ~60%. [Loungewear brand, client unnamed]

Merchandising, connected

Some products need exposure. Others need a better page. The matrix says which.

Product views against bookings per view. High take, low visibility means the product converts whoever finds it, and the fix is placement: navigation, homepage modules, email, ads. High visibility, low take means the traffic is there and the page, price or positioning is not. Each quadrant has a different owner, which is the point.

Views × bookings per view · illustrative
product viewsbookings per viewfix the page or priceprotect the placementquestion the slotgive it exposurethe sleeper
The filled dot converts better than anything on the site and almost nobody sees it. That is a placement decision, not a product problem, and it is invisible in a bestseller list.
The part the tools skip

The naive read fails quietly. These are built not to.

A/B tests & site changes
Overall CVR moves with traffic volume more than with anything you test, so "stat sig on CVR" alone is how discount-trained customers and phantom wins get shipped. The hypothesis gets filed before launch, the read isolates the segment the change actually touches, and behavior has to agree: a real offer effect shows up in email capture and add-to-cart patterns, not just a topline delta.
Pricing & promo changes
Revenue up or down after the change is not the answer. The read decomposes into what the change touches, AOV, shipping revenue, checkout abandonment, in the basket range around the change, netted to margin. A change can lose orders and still make money, or the reverse, and the components say which.
Merchandising
The bestseller list rewards whatever already gets traffic. Bookings per product view against views separates products that need exposure from products that need a better page or price, and routes the fix to whoever controls it: marketing, navigation, merch modules, recommendations.
New product launches
Launch week always looks good; that is the email blast and curiosity. The read compares the category's trend to its own year-over-year path, waits ~90 days for a verdict, and checks whether the buyers were new or just reshuffled, so a launch that moved demand around does not get planned as growth.

Every one of these reads is cheap once the instrumentation above exists, and impossible to trust without it.

The playbook

Instrument first. Then every read gets cheaper.

You do not need all of it, and almost nobody starts with more than two. Everything lives in your repo and your warehouse, so it is yours whether or not we are around.

01
Unified funnel tracking
The tracking plan above: every module, position and property, designed once with engineering and QA'd event by event. New components inherit the schema instead of needing new tags.
02
Content & module analytics
Homepage and landing modules read by view rate, click rate and position, so the page is laid out on evidence about what earns its slot.
03
Experimentation program
Hypotheses filed before launch, sample sizes set before the test, reads on the touched segment with behavioral evidence required. Inconclusive gets called inconclusive.
04
Merchandising analytics
Bookings per view against visibility, connected to what drives views: navigation, modules, marketing, recommendations. The fix gets routed to its owner.
05
Pricing & promo reads
Component decomposition in the basket range each change touches, netted to margin, so promos stop being judged on topline alone.
06
Journeys & personalization
Behavior stitched across sessions, then acted on: content and heroes matched to where the visitor came from and what they have already looked at.
07
Recommendations & search
What the rec modules actually add to units and AOV, and what people search for that the site does not surface. Both are merchandising inputs, not widgets.
08
Checkout & abandonment
Abandonment read by basket range and change history, so a shipping or threshold decision is made on its real components instead of a feeling about friction.
Proof

What happened when we did it.

Different businesses, same approach. The full studies say how each result was measured.

Loungewear brand · client unnamed
The LTV analysis that re-ranked which products acquisition leads with.

First-product cohorts showed which entry purchase predicts the customers worth buying, and the media plan followed.

Home & furnishings DTC brand
A first-party pixel closed a ~20% click-to-session gap, and rebuilt landing journeys followed.

Once clicks and sessions reconciled, landing experiences were rebuilt by journey, and the highest-LTV category grew. Part of the 2.8x growth study.

In the writing
The dedicated site & experimentation study.

Instrumentation, the experiment program, and what the module analytics changed, with the mechanism shown.

Read the full case studies

Get in touch.

Tell us what you are trying to figure out. If we can help, we will say how. If we are not the right fit, we will say that too, and point you somewhere better.

01
Free work first
We start by doing a real piece of work on your data, free, so the first thing you judge is output, not a pitch.
02
First build, fixed scope
A trusted core metric layer in weeks, not quarters, at a fixed price. Small enough to be safe, real enough to matter.
03
Scale when it earns it
Month to month from there, from a few thousand a month. You own every model, dashboard and definition.