Case study
Listing Velocity
A D2C brand went from 4 product listings a week to 40, with the same one person.
The case study
The brand
A ₹3Cr D2C brand selling on Amazon, Flipkart and Shopify. One person owned listings, and new SKUs sat in a spreadsheet for weeks.
The situation
Every listing was 2 to 3 hours by hand: title, description, bullets, keywords, marketplace variants and alt text. So 4 went live a week while dozens waited.
The catalog, the thing customers actually search, stayed thin. A competitor listed 40 a week and collected the long-tail searches this brand never appeared for.
What they did
- 1Fed the pipeline just SKU data and one product photo.
- 2AI drafted the title, description, bullets, keywords and Amazon/Flipkart variants in brand voice.
- 3The owner reviewed and approved each in about 12 minutes instead of 2 to 3 hours.
- 4Listing velocity went from 4 a week to 40, clearing the backlog in weeks.
The result, 6 months
₹2,40,000 / month in long-tail orders the catalog used to miss
Search traffic on queries the brand finally listed for. Zero new ad spend.
40 / wk
listings published, up from 4
12 min
per listing, down from 2 to 3 hours
₹0
extra acquisition cost for long-tail search
10×
faster catalog coverage than before
Before / after
Before
After
Listings / week
4
40
Time / listing
2-3 hrs
12 min
Catalog gap
widening
closed
More SKUs than listings?
Reply "LISTINGS" and I will outline what your pipeline would look like. Free.
Reply LISTINGS →