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Track 06The cause

Find out why products come back, and what to change.

The analyst reads every reason, note and QC photo, groups them by product, size and batch, and tells your team what is driving returns and which fix is most likely to work.

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AI Return Reason AnalystLast 60 days
Straight cotton kurtaStyle BT-KRT · 312 returns · batch 07 and 08
Tight in shoulders64%
Sleeve length short17%
Colour differs from photo11%
Other8%
Suggested fixShoulder width runs 0.5" small against the size chart. Update the chart and add a size up note on the product page.
AcceptSend to design team
Every reasonPicked reasons, free-text notes and QC results
By style and batchSpots a bad batch before it spreads
Suggested fixesSize chart, photo, description or vendor
Before and afterTracks return rate after a fix ships
AI Return Reason Analyst

From "too tight" to a size chart that fits

Return reasons are the most honest product feedback you get. The analyst turns them into a short list of fixes your team can decide on.

Reason clusters

"Tight in the shoulders", "shoulder seam pulls" and "kandhe pe tight" are the same problem. The analyst groups notes in English, Hindi and Hinglish into clear causes.

Batch and vendor signals

When returns for one batch or vendor jump, you see it within days, with QC photos as evidence.

Fixes with reasons

Update the size chart, reshoot the colour, add a fabric note or raise it with the vendor. Each suggestion shows the returns behind it.

Fix tracking

Mark a fix as shipped and the analyst compares return rates before and after, by style.

Pattern flags for review

Unusual return patterns, such as repeated wear-and-return, are flagged to your team with the evidence. The AI never blocks a customer.

Ask in plain words

Ask "why are maroon kurtas coming back this month" and get the answer with the returns, photos and numbers behind it.

Workflow

From reasons to a shipped fix

01

Collect

Reasons, notes, photos and QC grades from every return.

02

Group

Similar causes are grouped by product, size, colour and batch.

03

Suggest

The analyst proposes fixes, ranked by the returns they could prevent.

04

Decide and track

Your team accepts or rejects. Results are tracked the next month.

Settings

What the analyst looks at

Every setting is changed from the Vyatyasa dashboard by your team. No code and no ticket to us.

InputsReturn reasons, free-text notes, portal photos, QC grades and photos
Product dataStyle, size, colour, batch, vendor and listing content
LanguagesEnglish, Hindi and Hinglish notes
OutputsReason clusters, suggested fixes, pattern flags, weekly digest
ControlEvery suggestion needs a person to accept it

Responsible AI, by default

  • The AI flags unusual return patterns for your team to review. It never blocks a customer on its own.
  • Every suggestion shows the returns and photos behind it, so your team can check the reasoning.
  • Your return data is used to serve your brand only. It is not used to train models for other brands.
  • Customer data is encrypted and never sold.
FAQ

AI Return Reason Analyst, answered

Does the AI block customers?
No. It flags patterns, such as many returns of worn items from one account, for your team to review. Any action on a customer is taken by a person.
How much data does it need?
It starts grouping reasons from the first few hundred returns. Batch and vendor signals get sharper after a month of data.
Can it read Hindi notes?
Yes. Notes in English, Hindi and Hinglish are grouped together, so the same cause is counted once.
Track 05Refund DeskTrack 07Returns Reports
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