All proof

Kanjivaram and bridal silk · India

Snehalyaa Silks

T Nagar, Chennai · Coimbatore

From a ₹40–45L plateau to an ₹80L+ monthly run rate.

How five AI systems, phase-split planning, and a measurement approach built on Shopify actuals moved a heritage Kanjivaram brand's online store past its ceiling.

May – August 2026Shopify orders, reconciled against every ad platform · May – August 2026, against the May 2026 plateau of ₹40–45L monthly online revenue
  • Growth strategy
  • AI automation
  • Performance marketing
  • Revenue analytics
  • Shopify e-commerce

“We finally stopped losing customers to slow replies.”

  • Online revenue

    ~2x

    Monthly online revenue vs. the May plateau

  • Festival orders

    2x

    Orders in the festival sale after the discounting change

  • Chatbot

    ₹9L

    Sales made through the AI Chatbot

  • Carts

    ₹10L

    Revenue recovered by the abandoned cart flow

  • Recovery rate

    3 in 10

    Abandoned carts recovered

  • Meta ROAS

    10–14x

    Meta ROAS on normal days, reconciled to Shopify

At a glance

A brand with deep offline roots, an online store that had stopped growing, and a festive calendar that decides most of the year.

Category
Kanjivaram and bridal silk sarees
Channel
Shopify online store
Physical stores
T Nagar (Chennai) and Coimbatore
Engagement began
May 2026
Starting point
₹40–45L monthly, plateaued
Current trajectory
Tracking toward ₹80L+ monthly
Data sources
Shopify, GA4, Google Ads, Meta Ads, Merchant Center, Search Console, WhatsApp

What moved

After four months

Baseline period
May 2026
Method
Shopify orders — monthly online revenue
Locked on
May 2026

  • Monthly online revenue

    ₹40–45L

    ₹80L+tracking run rate, not a closed month

  • Orders in the festival sale

    Baseline

    2xafter the discounting change

  • Unlisted products in Merchant Center

    670+

    ~11July to August

  • Meta ROAS, reconciled to Shopify

    Unverified

    10–14xon normal, non-sale days

Over

May 2026, ongoing

A calendar that decides everything, and three problems the data was hiding.

Indian ethnic wear runs on a calendar. Aadi Sale, Varalakshmi Vratham, Varamahalakshmi, and Raksha Bandhan pull demand into short, intense windows, and a blended monthly target hides what's really happening inside them. By July, three further problems were visible in the data.

Over 670 products were unlisted in Google Merchant Center, so a large part of the catalogue couldn't appear in Shopping surfaces.

Search impression share was low next to competitors, who were winning top-of-page placement far more often.

A large share of GA4 sessions arrived as "Unassigned," and Meta's reported revenue didn't match what Shopify recorded.

Objectives we set

Break the ₹40–45L plateau with systems that keep working after a campaign ends.

Recover revenue already lost to abandoned carts and unanswered questions.

Plan around the festive calendar in phases, with a target for the sale and a separate one for what follows.

Treat Shopify as the source of truth and report every channel against it.

Five systems, one revenue engine.

Each system targets a specific leak or lever in the customer journey, from first question to repeat purchase.

AI Chatbot

· live from 1 July

Silk buyers ask about fabric, zari, occasion fit, delivery and care before they commit. The chatbot answers these at any hour, so a shopper's doubt is resolved in the moment instead of becoming a closed tab. The cumulative report puts sales made through the chatbot at ₹9L.

Abandoned Cart Recovery

· WhatsApp flow, live from 15 July

A WhatsApp flow reaches shoppers who left items in the cart or stopped at checkout. It recovers 3 out of every 10 carts, and has brought back ₹10L in revenue that would otherwise have been lost.

Discounting Intelligence

· added 1 July

Around festival periods, the call was to discount harder: clear stock and accept a lower AOV in exchange for volume. It worked. The store took twice the number of orders, and the sale period was planned separately from the post-sale period.

Revenue Intelligence

· reads every source

Shopify, GA4, Google Ads, Meta Ads, Merchant Center, Search Console and Meta Business Suite feed one monthly cumulative report. It reconciles channel claims against Shopify orders and shows where revenue really comes from, which is how the organic search pattern and the feed problems were found.

AI Image Generation

· built for ads

A festive calendar demands a steady flow of fresh creative. AI image generation produces about 10 videos and 20 images every day for ads. The result is better ads, and creative fatigue that's refreshed on time instead of after performance has already dropped.

Planning in phases, not in months.

August was planned as two periods instead of one blended month. The sale window and the post-sale festival period behave differently, so each was planned on its own. Aadi Sale, Varalakshmi Vratham, Varamahalakshmi and Raksha Bandhan each sit in that calendar.

The split gave the team and the client a shared, honest picture of each phase. The sale is built for volume, and the post-sale phase for higher-value orders.

Three lenses on the same revenue.

Meta, GA4 and Shopify each count revenue differently. Meta uses a 7-day click and 1-day view-through window, so it reports more than Shopify's last-click view credits to Paid Social. That gap appeared in consecutive months, and it shaped how we report.

Meta Ads

~19–20x

Self-reported ROAS. Generous windows count more orders as Meta's own.

GA4

A fraction

Last-click credit to Paid Social is far smaller, and many sessions sit in "Unassigned."

Shopify

Ground truth

Actual orders. Every forecast and target is anchored here.

  • Platform revenues are never added together to forecast store revenue.

  • Order-level reconciliation of Shopify against Meta shows ROAS on normal, non-sale days holding at 10–14x, so the strong efficiency is real.

  • The Unassigned channel is treated as a known blind spot, and closing it is part of the roadmap.

Three findings, none of them guesses.

Organic search punches above its weight

For several consecutive months, Organic Search has converted revenue at about 3.5 times its share of sessions. It's a pattern worth protecting and amplifying.

A cleaner product feed

Between July and August, unlisted products in Google Merchant Center fell from more than 670 to about 11, putting nearly the whole catalogue in front of Shopping shoppers.

Suspicious traffic

August data showed suspected bot or scraper traffic from certain geographies, flagged so it doesn't distort conversion and channel reads.

Five things worth carrying into the next account.

Anchor every plan on Shopify actuals, and use ad platforms to explain movement, never to size revenue.

Split the plan by phase whenever the calendar has a sale peak.

Reconcile at the order level before arguing about saturation or scale.

Protect the channels that convert above their traffic share, starting with Organic Search.

Scale spend in defensible steps. A 25–50% increase is easier to defend than a large jump, even when the data would allow more.

Four things on the roadmap.

Close the attribution gap between Meta, GA4 and Shopify, and shrink the Unassigned channel.

Raise Search impression share, which is low against competitors.

Mature the WhatsApp recovery flow as the flow's data builds.

Continue the monthly cumulative report, with September following the same multi-source format.

Who ran it

  • Pratik Chauhan

    Business head

  • Rushani

    Client lead

About these figures

Monthly online revenue. The latest figure is a tracking run rate, not a closed month; the curve between these two points is illustrative, not measured week by week. Figures reflect the May to August 2026 engagement; the latest month is a tracking run rate.

The next step

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