Store launch and demand engine for a DTC brand
Manual order-taking had become the constraint on growth.
The brand had genuine demand and no infrastructure — orders arrived as direct messages, payment was a bank transfer screenshot, and stock was a shared spreadsheet that was wrong by lunchtime. The product was working. Everything around it was a bottleneck with a person inside it.
- Client
- Direct-to-consumer brand, first online store
- Engagement
- 6-week build, then growth retainer
- Market
- Pakistan, shipping regionally
- Delivered
- 2025
What we walked into
- Orders taken manually through social DMs, capped by how fast one person could reply.
- No reliable stock position, leading to oversells and refunds.
- Per-order margin unknown once payment fees and shipping were counted.
- Paid social spend rising with no attribution beyond platform-reported sales.
What we actually did
Model the margin before building
We modelled per-order economics first — payment fees, packaging, courier rates by zone, and return rate. That analysis set the free-shipping threshold and killed two SKUs that could not carry their own fulfilment cost.
Build the store properly
A Shopify build with the full catalogue structured for search, local and international gateways, courier-accurate shipping zones, and a checkout stripped of friction. Inventory connected to accounting so the stock position stopped being a guess.
Instrument before spending
Server-side conversion tracking and GA4 configured and verified against real test transactions before a single advertising rupee went out — so the first month of spend produced usable data rather than a plausible-looking report.
Layer the demand engine
Paid social run to a target cost per order, organic content on the two channels the audience actually used, and lifecycle email — welcome, abandoned cart, and post-purchase — carrying repeat revenue without anyone sending a campaign.
What changed
- 6 wks
- From first session to live store
- 2.9%
- Store conversion rate at month three
- 31%
- Of revenue from automated email flows
- 0
- Orders still entered by hand
- Order volume decoupled from how many DMs a person can answer in a day.
- Known margin per order, per SKU, after fees and shipping.
- Advertising spend decisions made against verified revenue data rather than platform claims.
- A third of revenue arriving from flows that run with nobody watching.
Client identity is withheld under confidentiality. This case study is representative of the engagements we run and the results they produce; figures illustrate typical outcomes rather than a specific audited account. We're happy to discuss specifics, and arrange references, on a call.
The engagements behind this work
E-Commerce Store Setup
A Shopify or WooCommerce store built to sell — listings, payments, shipping, and tracking done properly.
PPC & Paid Advertising
Google and Meta campaigns run to a cost per lead, with every click tracked to revenue.
Social Media Marketing
Content and paid social that build an audience worth something commercially — not a follower count.
Email Marketing & Automation
Lifecycle email and automation that earn revenue from the list you already own.
More on how we work with e-commerce businesses.
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