Case study
How UrbanCart Achieved 3.2x ROAS While Cutting Ad Spend by 28%
UrbanCart — a fast-growing D2C home-goods brand — was burning budget on Google and Meta with a 1.1x ROAS. VR Digital's AI bid intelligence and landing page CRO programme rebuilt their paid ecosystem, tripling return on ad spend while simultaneously reducing total spend.
Client
UrbanCart
Industry
E-commerce
Engagement
5 months · ongoing
Services
Google Ads + Meta Ads
UrbanCart
UrbanCart — a fast-growing D2C home-goods brand — was burning budget on Google a…
ROAS
3.2x
↑ Client-reportedAd Spend
-28%
↑ Client-reportedConversion Rate
+190%
↑ Client-reportedCPA
-41%
↑ Client-reportedObjective
ROAS was 1.1x — effectively spending ₹1 to make ₹1.10, with no profitability headroom. No conversion tracking beyond basic GA4 — no revenue data in Google Ads. All campaigns running broad match with no negative keyword hygiene. Zero audience segmentation — cold, warm and retargeting traffic treated identically. Product landing pages had 78% bounce rate — no above-the-fold offer clarity. Zero creative testing process — one creative running for 4+ months.
Challenges & Solutions
What stood in the way, and what we changed.
ROAS was 1.1x — effectively spending ₹1 to make ₹1.10, with no profitability headroom.
- No conversion tracking beyond basic GA4 — no revenue data in Google Ads.
- All campaigns running broad match with no negative keyword hygiene.
- Zero audience segmentation — cold, warm and retargeting traffic treated identically.
- Product landing pages had 78% bounce rate — no above-the-fold offer clarity.
- Zero creative testing process — one creative running for 4+ months.
Installed server-side conversion tracking with revenue values — fed real ROAS data into the platforms.
- Rebuilt Google Shopping feed with AI-optimised titles, descriptions and custom labels by margin tier.
- Migrated to tROAS smart bidding with a 90-day learning budget; AI bid models updated every 15 minutes.
- Segmented audiences into five cold-to-warm tiers; separate creative and bid strategy for each.
- Built 12 landing page variants using AI copywriting + heat-map-informed layouts; ran continuous A/B tests.
- Launched Dynamic Search Ads and Performance Max with custom asset groups by product category.
- Meta campaigns rebuilt with DCO (Dynamic Creative Optimisation) — 320 creative combinations tested in 60 days.
Our approach. 4 phases
Four phases, each with a clear output.
Step 01
Audit and research
The starting point, the data available and the questions customers ask.
Step 02
Plan
Priorities, measures and the work agreed in writing.
Step 03
Build and launch
Tracking, pages, campaigns and content go live.
Step 04
Measure and improve
Results reviewed against the agreed measures, with changes logged.
Results.
ROAS improved from 1.1x to 3.2x over five months. Ad spend reduced by 28% (redirected savings to higher-margin product lines). Conversion rate on paid traffic improved 190% — from 0.8% to 2.3%. CPA fell 41% — making the paid channel profitable for the first time. Google Shopping impression share grew from 14% to 67%. Best-performing Meta ad creative generated ₹42L in revenue from a ₹3.8L spend.
ROAS
3.2x
↑ Client-reported
Over 5 months · client-reported
Ad Spend
-28%
↑ Client-reported
Across the full engagement period
Conversion Rate
+190%
↑ Client-reported
Across the full engagement period
CPA
-41%
↑ Client-reported
Across the full engagement period
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The starting state
ROAS was 1.1x — effectively spending ₹1 to make ₹1.10, with no profitability headroom. No conversion tracking beyond basic GA4 — no revenue data in Google Ads. All campaigns running broad match with no negative keyword hygiene. Zero audience segmentation — cold, warm and retargeting traffic treated identically. Product landing pages had 78% bounce rate — no above-the-fold offer clarity. Zero creative testing process — one creative running for 4+ months.
What we changed
Installed server-side conversion tracking with revenue values — fed real ROAS data into the platforms. Rebuilt Google Shopping feed with AI-optimised titles, descriptions and custom labels by margin tier. Migrated to tROAS smart bidding with a 90-day learning budget; AI bid models updated every 15 minutes. Segmented audiences into five cold-to-warm tiers; separate creative and bid strategy for each. Built 12 landing page variants using AI copywriting + heat-map-informed layouts; ran continuous A/B tests. Launched Dynamic Search Ads and Performance Max with custom asset groups by product category. Meta campaigns rebuilt with DCO (Dynamic Creative Optimisation) — 320 creative combinations tested in 60 days.
“We went from dreading the monthly ad report to actually looking forward to it. The ROAS improvement alone paid for the entire agency retainer in the first month.”
Priya Nair
Co-founder & CMO, UrbanCart
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Case study FAQs
How should I interpret the reported results?+
Read the figures alongside the stated challenge, timeframe and channels. Ask about the metric definitions and supporting evidence before using a result in your own forecast.
Will my business achieve the same outcome?+
Results vary with audience, competition, budget, offer and execution. A published case study is an example of an engagement, not a guarantee for another business.
Which parts of the approach can be applied elsewhere?+
The research, tracking and testing process can inform other projects. The channel mix and messaging need to fit your own customer journey.
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We review the starting point, available data and commercial priorities, then agree deliverables and relevant success measures.
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- Founder industry experience
- 8 years
- Based in India
- India
- Digital services
- 9
- Strategy & automation
- Custom