GTM Systems for D2C & Ecommerce
Consumer commerce is the one place where the arithmetic is unforgiving and immediate. If second-purchase rate is low, no amount of acquisition efficiency saves the business, and most brands are still reporting on blended ROAS.
Key Facts
- Focus
- GTM for D2C and ecommerce
- Category
- GTM by Industry
- Defined outputs
- 5 deliverables
- Regions served
- India · United States · United Kingdom · UAE · Singapore
- Last reviewed
- 2026-09-10
You Are Measuring ROAS When the Business Runs on Contribution Margin.
Platform-reported return on ad spend counts revenue the platform believes it caused, before shipping, payment fees, returns, discounts and cost of goods. Brands optimise hard against that number for two years and discover the campaigns with the best reported ROAS were the least profitable ones. The fix is not a better attribution window. It is measuring the metric that determines whether the business survives.
Repeat purchase is treated as a marketing outcome rather than an operational one, so nothing in the post-purchase flow is engineered.
Creative testing is unstructured. Winners are declared on small samples and short windows, and the learning is never written down.
New and returning customer economics are blended, hiding the fact that acquisition is often subsidised entirely by a small repeat cohort.
Rebuilding the Motion Around Contribution Margin
Get to True Contribution Margin per Cohort
We pull cost of goods, shipping, payment fees, returns and discounts into a cohort-level margin model, split by acquisition channel and first product purchased. This routinely reorders the channel ranking and identifies products that acquire well but lose money over the customer's life.
Engineer the Second Purchase
The gap between first and second purchase is the highest-leverage interval in consumer commerce. We build replenishment timing from actual consumption cadence per category, cross-sell logic from purchase-sequence data, and post-delivery flows triggered by fulfilment events rather than by order date.
Make Creative Testing a System
A structured testing cadence with defined hypotheses, minimum sample thresholds, and a tagged creative library so learnings accumulate. Most brands generate a great deal of creative data and retain almost none of it, which is why the same failed concept reappears every eighteen months.
Measure Paid Honestly
Geo holdouts and incrementality tests on the largest channels, reconciled against post-purchase survey data. It is uncomfortable at first. Incrementality usually reveals that a meaningful share of branded and retargeting spend was harvesting demand that already existed.
Deliverables
- Cohort-level contribution margin by channel, product and acquisition month
- Replenishment and cross-sell flows triggered by fulfilment and consumption cadence
- A structured creative testing programme with a tagged, searchable learning library
- Incrementality testing on your highest-spend channels with post-purchase survey reconciliation
- A single operating dashboard tracking contribution margin, repeat rate and payback
Is This You?
Strong fit
- You are past roughly ₹5 crore or $600K in annual revenue with a repeatable product line.
- You spend meaningfully on paid and cannot separate incremental from harvested demand.
- You have order and fulfilment data accessible through Shopify, an ERP or a warehouse.
Not a fit yet
- You are pre-launch. Find product-market fit and a working creative angle first.
- You want media buying management. We build the measurement and retention systems around it.
What Is Your Second-Purchase Rate?
If you cannot answer that within thirty seconds, split by acquisition channel, that is the whole conversation. It is the number that decides whether your acquisition spend is an investment or a subsidy.
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Common Questions
Do you do media buying?
No. We build the measurement, retention and data infrastructure that makes media buying decisions correct. Most brands we work with have a competent buyer or agency who has simply never been given trustworthy contribution margin data to optimise against.
We are on Shopify with a stack of apps. Is that enough?
For contribution margin modelling you will need order, cost and fulfilment data somewhere they can be joined, often a lightweight warehouse rather than another app. The app layer is generally fine for execution; it is the measurement layer that tends to be missing.
Does this apply to Indian D2C brands specifically?
Yes, with the added complexity that cash on delivery, high return rates and marketplace channel conflict materially change the margin model. Any contribution analysis for an Indian brand that ignores return-to-origin rates is not describing the real business.
How long before we see the effect?
Margin visibility arrives within weeks and frequently changes spend allocation immediately, which is the fastest return. Repeat rate improvements follow the purchase cycle of your category, for a monthly consumable that is one quarter, for durables it can be a year before the cohort data is conclusive.
Related GTM Systems
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Onboarding, expansion and churn-prevention motions driven by product usage rather than calendar dates. Built on your data, measured on net revenue retention.
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GTM Systems for Marketplaces
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