Short answer
Campaign-level reporting tells you which ads, audiences and creatives drive orders and at what cost. Product-level analytics tells you which SKUs make money after returns, sizes and stock, and which you can afford to push. A D2C brand needs both: a campaign can look great while driving a product with high returns or low stock, and a hero product can look weak because its campaigns are poorly targeted.
Key takeaways
- Campaign view: where to spend. Product view: what to push, make and stock.
- Fashion brands especially need product-level return and size data. A winning campaign on a high-return SKU loses money.
- Join the two by SKU: spend and ROAS per product, alongside return rate and days of cover.
- Review both weekly in the same meeting so marketing and ops decisions don't contradict each other.
In most D2C brands, the marketing team lives in Ads Manager and the operations team lives in the OMS. One reports by campaign, the other by SKU. Both are right, and decisions go wrong when they don't meet.
The difference in one table
| Campaign-level reporting | Product-level analytics | |
|---|---|---|
| Unit of analysis | Campaign, ad set, creative, audience | SKU, product, collection, size |
| Main questions | Where should we spend? Which creative works? | What should we push, make, restock or drop? |
| Key metrics | Spend, CTR, CPC, ROAS, CAC | Units, revenue, return rate, margin, days of cover |
| Source | Meta Ads, Google Ads | Shopify, Unicommerce, Shiprocket |
| Blind spot | Returns, sizes, stock | Which spend drove the sales |
Why it matters: a fashion brand example
How to connect the two views
- 1
Tag campaigns by product
Use consistent naming or product sets so spend can be mapped to SKUs.
- 2
Pull product outcomes
Units, delivered revenue, return and exchange rate, RTO, margin by SKU.
- 3
Join by SKU
Calculate spend, ROAS and contribution per product, not just per campaign.
- 4
Add stock
Days of cover per SKU and size, so you don't scale ads into a stock-out.
- 5
Review together
One weekly meeting with marketing and ops looking at the same table.
The combined table to review weekly
| SKU | Ad spend | Attributed revenue | ROAS | Return rate | Contribution after ads | Days of cover | Action |
|---|---|---|---|---|---|---|---|
| Dress A | ₹80,000 | ₹2,40,000 | 3.0 | 12% | ₹46,000 | 40 | Scale |
| Dress B | ₹1,20,000 | ₹4,10,000 | 3.4 | 38% | −₹9,000 | 6 | Pause, fix sizing, restock |
| Kurta C | ₹30,000 | ₹60,000 | 2.0 | 8% | ₹4,000 | 25 | Test new creative |
Where product-level analytics matters most
- Fashion and footwear: size-driven returns and exchanges.
- Beauty and personal care: shade or skin-type mismatches; batch expiry.
- Food and beverage: quick commerce availability and shelf life.
- Any brand with heavy COD: RTO varies a lot by product and price point.
How MarQet BI helps
MarQet BI puts ad spend from Meta and Google next to Shopify, Unicommerce and Shiprocket data, so you can see sales by product, returns by state and ad spend next to orders in one place, and ask "which products drove last week's returns?" in plain language.
Frequently asked questions
What is the difference between campaign-level and product-level analytics?
Campaign-level reporting measures ad performance by campaign, ad set and creative. Product-level analytics measures performance by SKU, including returns, margin and stock. D2C brands need both to make good decisions.
Why does product-level analytics matter for a D2C fashion brand?
Fashion has high size-driven returns and limited stock per size. A campaign can show strong ROAS while driving a SKU with high returns or low cover, which loses money after returns.
How do I connect ad spend to products?
Tag campaigns or product sets by SKU, then join ad spend to product outcomes from your store and OMS. Blended analytics tools like MarQet BI bring both into one view.
Written by
Ayush Singhal, Founder, MarQetAyush founded MarQet to give D2C founders one system for customer conversations, daily performance and quick-commerce shelf availability.