E-Commerce

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

The situation

What we walked into

01
Orders taken manually through social DMs, capped by how fast one person could reply.
02
No reliable stock position, leading to oversells and refunds.
03
Per-order margin unknown once payment fees and shipping were counted.
04
Paid social spend rising with no attribution beyond platform-reported sales.

The approach

What we actually did

01
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.
02
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.
03
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.
04
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.

The result

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 identities are withheld under confidentiality. These case studies are 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.

Services involved