Case study — the business behind the systems
Running an Eight-Figure DTC Brand
Before I built software for businesses, I ran one. For six years I owned the ecommerce operation of a national DTC apparel brand: eight demand channels and their budgets, the Shopify storefront and its dev team, the CRM and retention program, in-house customer service, five agencies, and the annual growth model that set every one of their targets. This page maps that business the way the rest of this portfolio maps codebases, because that is what it was: an architected system, operated daily for years.
in practice
What running it actually meant
The year started with a model. Each annual cycle I projected the customer base forward: how many new customers each channel would bring at what cost, what the returning base would spend based on cohort behavior, and what blended profitability required. That rolled down into per-channel spend, revenue, and return targets, and those targets became the KPIs every agency partner was held to. When a paid media agency asked what their ROAS goal was, the answer came out of my spreadsheet, not theirs.
The day started with the revenue read: yesterday's revenue against plan, spend and return by channel, email performance, and anything the storefront or CS queue was signaling. The week ran on the marketing calendar: campaigns, promotions, content, and channel pushes sequenced against inventory position and season. This cadence, daily read, weekly budget-and-calendar review, annual model, ran for years without a gap.
The scope beneath it: I purchased, onboarded, and built the processes for every piece of the ecommerce technology and agency stack. Storefront, email platform, CS software, Loop returns, affiliate network, analytics. When a tool was in the stack, it was because I put it there and trained the team that used it.
architecture
The business as a system
The operating map, demand to doorstep
Demand
eight channels, one growth model
Every channel carried spend, revenue, and return targets from the annual growth model. Agencies ran four of them, each held to the model's numbers.
Storefront
Shopify, owned end to end
Full store ownership: catalog, pricing and promotion mechanics, conversion work, and a three-person dev agency shipping against a roadmap I set.
Order-to-cash
NetSuite ERP + 3PL fulfillment
The ownership boundary: NetSuite ops belonged to another business lead and the 3PL was a third party. I owned the integrations that connected them to the store, plus Loop returns wired into tax and inventory systems. Output: 5-day delivery anywhere in the US, every day, for years.
Customer layer
in-house CS + lifecycle CRM
Customer service run in house on software I purchased, configured, and trained the team on. Every contact channel integrated and tracked. The CRM feeds retention revenue back into the model.
Instrumentation
the daily revenue read and the annual model
Daily revenue, spend, and channel metrics against plan. Annually: a growth model projecting the customer base, new versus returning revenue, and per-channel targets that became each agency's KPIs.
Demand: eight channels, four agencies
Google Ads and Meta each carried six-figure monthly budgets at peak, run through a paid media agency held to channel ROAS targets. Around them: a tiered affiliate program, email and SMS through Klaviyo, an auto-triggered direct mail program, a national catalog campaign, influencers, and organic social. Budgets moved between channels based on blended CAC and the model's return requirements.
Storefront: full Shopify ownership
Merchandising, pricing and promotion mechanics, CRO, and site operations, with a three-person dev agency shipping changes against a roadmap I set. Consumer-style dynamics: the store was the product, and everything from the session up was my zone.
Retention: CRM as a P&L input
Klaviyo flows and campaigns for lifecycle revenue, with the customer base modeled by cohort: repeat rates, LTV curves, and base health treated as a forecastable revenue line, not a marketing afterthought. Returning-customer revenue had its own targets in the annual model.
Order-to-cash: integrations at the boundary
NetSuite owned inventory and the allocation between ecommerce and wholesale, run by another business lead; fulfillment was a third-party 3PL. My job was the connective tissue: maintaining the Shopify integrations to both, and owning Loop returns, wired deep into Avalara tax and the inventory stack. The output the customer saw: 5-day delivery, anywhere in the US.
Customer service: in-house, every channel
Zendesk, purchased and configured by me, processes built to match the business, one trained CS specialist. Customers could ticket, chat, email, or call, and every contact was integrated, tracked, and answered within 24 business hours, through a queue that ran anywhere from 10 to 200 contacts a day with the season. The CS queue doubled as a product and site feedback loop.
The team: five in-house, five agencies
My in-house team ran five deep: two on content, two on social, one on email, plus the CS specialist I trained. Around it, five agencies under my direct management: dev, paid media, affiliate, catalog, and direct mail. Each held to KPIs derived from the growth model, so agency accountability was arithmetic, not vibes.
operating highlights
The parts that show the operator
An affiliate channel opened from zero to 8% of revenue
Direct mail that landed two days after a site visit
The CAC ceiling, managed with the brakes on
The growth model was the management system
instruments
The systems this business trained me to build
The engineering in this portfolio comes from this operation. The ecommerce data warehouse reconciles this brand's five years of orders, spend, and sessions, and the DTC growth dashboard is the operating picture I ran the business from, rebuilt as software. First I ran the machine. Then I built the instruments I always wanted while running it.
stack
Operated with
Shopify (storefront, owned end to end) · Klaviyo (email + SMS) · Zendesk (in-house CS) · ShareASale (affiliate network) · Loop (returns, integrated with Avalara tax and inventory) · NetSuite + 3PL (via integrations I maintained) · Google Ads + Meta (agency-run, six-figure monthly budgets) · direct mail and catalog programs · BigQuery, later, for the analysis layer.
The brand is genericized here as in the rest of this portfolio, and scale figures are given as ranges.