The Mom Store
Owning the digital product surface of a growing direct to consumer brand, where product management, search across three engines, and an AI assisted operating layer meet the unforgiving reality of a checkout.
01Overview
The Mom Store is a direct to consumer brand serving parents and young families. My remit is the digital product itself, the storefront as a system rather than a set of pages. That means holding the roadmap, deciding what gets built and in what order, and being accountable for the metrics that sit between a first impression and a completed order.
Unusually for a single role, it spans the full arc: how customers discover the store, how they move through it, and how the team behind it understands what's happening. Product, growth, and analytics aren't separate functions here. They're one continuous surface, and this case study follows that surface end to end.
02Business Challenge
A growing D2C brand runs into the same tension repeatedly: demand and traffic climb faster than the operational machinery underneath them. Discovery, merchandising, checkout, and reporting each start to strain, and it becomes unclear which of them is actually costing the most growth.
- Discovery increasingly happening outside classic search, in answer boxes and AI assistants the brand had no strategy for.
- Friction between arriving on the site and completing checkout that manual review couldn't reliably locate.
- Reporting that described the past accurately but arrived too slowly to change the present.
- A roadmap competing for attention against day to day firefighting.
03Discovery
I began by mapping the store as a funnel rather than a catalogue, instrumenting each transition from impression through product view, cart, checkout, and purchase, and marking where the biggest dropoffs actually lived versus where the team assumed they lived. Those two maps rarely match, and the gap between them is usually where the roadmap should start.
Fix what the data points to, not what's loudest in the room. The discovery phase exists to make the invisible dropoffs impossible to ignore.
04Research
Research ran on two tracks. Quantitatively: behavioural analytics, search query data, and onsite paths to understand where intent formed and where it broke. Qualitatively: reading the actual language customers used, in queries, in support, in reviews, because that language is what both people and answer engines match against.
- Query and keyword landscape across the category and its long tail.
- Customer journey mapping from first touch to repeat purchase.
- Competitive teardown of how rival stores structured discovery and checkout.
05Strategy
The strategy was to treat three usually separate problems as one system: be found (search, everywhere it now happens), convert cleanly (a checkout with the friction engineered out), and see clearly (reporting fast enough to act on). Each reinforces the others: better discovery is wasted on a leaky checkout; a great checkout is wasted if reporting can't tell you it's working.
Everything downstream, the roadmap, the experiments, the automation, was sequenced against that three part thesis.
06Execution
Execution ran as a rolling roadmap of prioritised bets, each shipped, measured, and either kept or reversed. Cross functional by necessity, working across content, design, development, and operations to move an item from hypothesis to live change.
- Roadmapping and feature prioritisation against funnel impact, not novelty.
- Structured experimentation, one change, one hypothesis, one measurable outcome.
- Checkout and conversion rate work targeting the specific steps the data flagged.
- Continuous journey optimisation informed by live behavioural data.
07Product Decisions
The hardest part of the role isn't generating ideas. It's deciding which not to build. Prioritisation ran on a simple frame: expected impact on the funnel, weighed against effort and reversibility. Cheap, reversible experiments shipped fast; expensive, one way door changes earned deeper scrutiny first.
Impact × reversibility. A reversible bet with plausible upside beats a “safe” change that moves nothing, and both beat a slow, irreversible one made on a hunch.
08SEO · AEO · GEO
Discovery is no longer one channel. I treat it as three overlapping surfaces, each requiring its own thinking:
- SEO, the classic foundation: technical health, site structure, and content mapped to real search intent, so the store ranks where customers still type queries.
- AEO (Answer Engine Optimisation), structuring content so answer engines and featured surfaces can extract clean, correct answers about products and the brand.
- GEO (Generative Engine Optimisation), making the brand legible to AI assistants that increasingly summarise and recommend before a customer ever reaches a page.
Treating all three as one discipline is the point: the same clarity of structure and language that ranks well is what lets a model quote you accurately.
09Analytics & Dashboarding
Measurement is the nervous system of the whole operation. I built the reporting so the numbers that matter, funnel health, search performance, conversion, sit in one place and update fast enough to change a decision this week, not next quarter.
- GA4 and Search Console wired into a single view of discovery to purchase.
- Dashboards framed around decisions, not vanity metrics.
- Experiment results read consistently, so “did it work?” has an honest answer.
10AI Integration
AI earns its place on the repetitive middle of the work, the tasks that are necessary but don't need a human's judgement every time. Used well, it widens how much ground a small team can cover: drafting and structuring content at scale, accelerating research and analysis, and supporting the search work across all three engines.
AI as leverage, not autopilot. It handles volume and first drafts; the judgement about what ships stays human.
11Automation
Alongside AI, plain automation removes the manual glue between systems, the copy and paste, the weekly export, the report that someone used to assemble by hand. Every workflow automated is attention returned to the team for the decisions that actually need them.
12Business Results
The work is ongoing, and it moves the numbers that matter most between a first impression and a completed order. The direction of travel:
checkout completion
across SEO · AEO · GEO
time
13Lessons Learned
- Discovery and conversion are one problem, optimising either in isolation leaks value at the seam between them.
- The fastest wins came from measurement, not from building. You can't fix a dropoff you can't see.
- AI and automation compound: their real return is the roadmap time they free up, not the tasks they replace.
14Next Steps
Deepening the generative engine work as AI mediated discovery grows; extending experimentation into personalisation; and pushing more of the reporting toward being self updating, so the team spends even less time describing the business and more time changing it.