Assortment & Pricing
Building an assortment that earns the second order.
Assortment and pricing architecture built for repeat purchase behaviour.
"draft": false for this brand in src/data/case-studies.json to remove this notice.
Assortment architecture
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Quick Look
01
The Brand
EDIT — One or two lines on who Dharishah is and what they sell. Real information only.
02
The Opportunity
The Quick Commerce range mirrored general trade rather than the pack sizes and price points that convert on a ten-minute shelf.
03
What We Did
Rebuilt the assortment around trial, repeat and basket-building packs, then set a pricing architecture that held margin at the point of decision.
04
The Outcome
EDIT — The outcome in one line, once the client has confirmed it in writing.
Our Process
Find the real problem first.
Every engagement starts by identifying what is actually limiting growth. Executing against the wrong diagnosis is the most expensive thing a brand can do on Quick Commerce.
- The assortment on Quick Commerce mirrored general trade rather than the pack sizes and price points that convert on a 10-minute shelf.
- Pricing was inconsistent across platforms, which suppressed conversion on the platform where the shopper was actually deciding.
- Growth was coming from discounting rather than from repeat purchase.
Growth was real but rented. Almost all of it traced back to discount depth rather than to shoppers coming back.
- The range was built for a shelf a shopper browses, not one they scan in seconds with a basket already open.
- Price gaps between platforms meant the brand was competing against itself at the moment of decision.
- Repeat-purchase cohorts were small relative to first-order volume — the classic signature of discount-led growth.
Where the gaps wereSeverity
Highlighted levers were worked directly during this partnership.
Assortment first, because pricing and media decisions only make sense once the range is right.
- Rebuilt the Quick Commerce assortment around trial packs, repeat packs and basket-building combinations.
- Set a pricing and promotion architecture that held margin while staying competitive at the point of decision.
- Cleaned up platform operations — onboarding, content, inventory signals and availability — so the assortment actually stayed on shelf.
- Shifted media weight towards repeat-purchase cohorts instead of first-order volume.
- 01 Diagnose
- 02 Implement
- 03 Optimise
- 04 Scale
The Result
EDIT — The result in one line, written for this brand. No generic claims.
results.metrics for this brand in
src/data/case-studies.json and rebuild — for example
{"value":"30","suffix":"%","label":"Revenue growth"}.
Partnership-level averages are published on the case studies page.
The thinking behind the work
EDIT — Founder name
Founder, SerenSynth Labs
Quick Commerce isn't won by being everywhere. It's won by being operationally right where demand already exists.
EDIT — Two or three lines of real professional background. Where they worked, what they built, how long they have been operating in Quick Commerce. No invented credentials.
EDIT — Why SerenSynth exists. The gap the founder saw that the company was built to close.
What brands most often get wrong
EDIT — The thing brands most commonly get wrong on Quick Commerce, in the founder's own words.
FAQ
Frequently asked questions.
What was the biggest Quick Commerce challenge here?
Growth that depended on discounting. Volume was arriving, but very little of it was coming back for a second order.
What did SerenSynth change first?
The assortment. Pricing architecture and media weight are downstream decisions — they only work once the range itself suits a ten-minute shelf.
Which platforms were involved?
Blinkit, Swiggy Instamart and Zepto. This reflects platform expertise, not an official partnership claim.
What contributed most to the result?
EDIT — Answer once results are confirmed. Keep it specific to what actually moved the number.
How long did the engagement take?
EDIT — Real engagement length.
Can SerenSynth do this for another brand?
Yes. The approach transfers; the specific pack architecture and price ladders are rebuilt per category.
Next step
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A small number of partnerships at a time — each one read, diagnosed and operated the same way.
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