E-Commerce Revenue Growth via Paid Search
| Client
E-Commerce Brand |
Duration
Jan – Mar 2024 (3 months) |
Total Budget
$1,100,000 |
1. Executive Summary
This case study documents a high-performance Google Ads campaign executed for a Bangladesh-based e-commerce brand between January and March 2024. The objective was to significantly improve paid search ROI, reduce cost-per-acquisition, and scale revenue through structured campaigns spanning Search, Shopping, Display, and Remarketing channels.
Within three months, monthly revenue grew from ৳1.02M to ৳4.2M — a 312% increase — while reducing cost per conversion by 42%. The campaign achieved a ROAS of 3.8x against a target of 3x, demonstrating the effectiveness of data-driven bidding, audience segmentation, and continuous optimization.
| $4.2M
Revenue Generated +312% |
3.8x
ROAS Target: 3x |
6.4%
Conv. Rate +178% |
$180
Cost/Conversion -42% |
2. Client Background & Challenge
2.1 About the Client
The client is a mid-sized Bangladeshi e-commerce brand selling lifestyle and fashion products online. Prior to this engagement, the brand had minimal paid search presence and relied primarily on organic social media and word-of-mouth for customer acquisition.
2.2 Key Challenges
- Low brand visibility in Google search results for high-intent product keywords
- High cost-per-click (CPC) due to unstructured campaigns and broad match keyword targeting
- No remarketing strategy in place — potential customers were not being re-engaged
- Shopping feed was not optimized, leading to low product impression share
- No conversion tracking properly configured — decision-making was based on incomplete data
- Limited A/B testing — ad copy had not been refreshed in over 6 months
3. Campaign Objectives
The campaign was designed around four primary KPIs aligned with the client’s business goals for Q1 2024:
| Objective | Target |
| Achieve minimum ROAS | 3.0x |
| Increase monthly revenue | $3.5M+ |
| Reduce cost per conversion | Below $250 |
| Improve impression share | 50%+ for top categories |
4. Strategy & Approach
4.1 Account Structure Overhaul
The first two weeks were dedicated to completely restructuring the Google Ads account. Campaigns were organized using a SKAG (Single Keyword Ad Group) approach for high-value terms, enabling precise bid control and highly relevant ad copy.
- Separated brand and non-brand campaigns to manage bids and budgets independently
- Created category-level shopping campaigns with custom labels for margin-based bidding
- Set up dedicated remarketing lists for cart abandoners, product page viewers, and past purchasers
- Implemented proper UTM parameters and Google Analytics 4 conversion tracking
4.2 Keyword Strategy
A comprehensive keyword audit was conducted using Google Keyword Planner, SEMrush, and competitor analysis. Over 2,400 keywords were researched and refined to approximately 380 targeted keywords grouped by intent:
- High-intent purchase keywords: exact and phrase match — 45% of budget
- Category-level research keywords: phrase match — 30% of budget
- Brand defense keywords: exact match — 10% of budget
- Competitor keywords: phrase match — 15% of budget
Extensive negative keyword lists (1,200+ terms) were built to eliminate irrelevant traffic and reduce wasted spend from the start.
4.3 Bidding Strategy
A phased bidding approach was adopted to accumulate conversion data before enabling Smart Bidding automation:
- Week 1-2: Manual CPC with conservative bids to gather baseline data
- Week 3-4: Enhanced CPC (ECPC) with bid adjustments by device, location, and time of day
- Month 2+: Target ROAS Smart Bidding activated after 50+ conversions per campaign
- Final phase: Portfolio bid strategies applied across related campaign groups
4.4 Creative Strategy
Responsive Search Ads (RSAs) were created with 15 headlines and 4 descriptions per ad group, following a benefit-led messaging framework. Three ad copy themes were tested simultaneously:
- Price and value proposition (e.g., ‘Free Delivery on Orders Over ৳500’)
- Urgency and scarcity messaging (e.g., ‘Limited Stock — Order Today’)
- Product quality and trust signals (e.g., ‘Authentic Products | 30-Day Returns’)
5. Performance Results
5.1 Overall KPI Summary
The following table compares baseline metrics (December 2023) against final campaign performance (March 2024):
| Metric | Before Campaign | After Campaign | Change |
| Monthly Revenue | ৳1.02M | ৳4.2M | +312% |
| ROAS | 1.2x | 3.8x | +217% |
| Conversion Rate | 2.3% | 6.4% | +178% |
| Cost Per Conversion | ৳310 | ৳180 | -42% |
| CTR (Search) | 2.1% | 5.8% | +176% |
| Avg. CPC | $38 | ৳22 | -42% |
| Impression Share | 28% | 67% | +139% |
5.2 Campaign-Level Breakdown
Performance varied across campaign types, with Shopping campaigns delivering the highest ROAS due to product feed optimizations:
| Campaign Type | Budget % | Conv. Rate | ROAS | Revenue Share |
| Search Ads | 45% | 7.2% | 4.1x | 48% |
| Shopping Campaigns | 30% | 8.5% | 5.2x | 35% |
| Remarketing (Display) | 15% | 5.1% | 3.4x | 12% |
| Display (Awareness) | 10% | 1.8% | 2.1x | 5% |
5.3 Monthly Progression
Revenue and ROAS improved consistently across all three months as Smart Bidding algorithms accumulated learning data and bid adjustments were refined:
| Month | Revenue | ROAS | Conv. Rate |
| January 2024 | $1.8M | 2.1x | 3.9% |
| February 2024 | $2.9M | 3.1x | 5.2% |
| March 2024 | $4.2M | 3.8x | 6.4% |
6. Key Learnings & Insights
6.1 What Worked
- Shopping campaign restructuring with custom labels based on product margin drove the highest ROAS (5.2x) across all campaign types
- Layering RLSA (Remarketing Lists for Search Ads) on top-performing search campaigns increased conversion rate by 34% for returning visitors
- Excluding mobile app placements from Display campaigns reduced wasted spend by 18%
- Dayparting analysis revealed that Thursday–Saturday 7PM–11PM generated 38% of all conversions; budget was shifted accordingly
- Dynamic remarketing ads with product-level creative outperformed generic display ads by 2.8x in ROAS
6.2 Challenges & Solutions
| Challenge | Solution Applied |
| Smart Bidding learning phase took longer than expected in January | Extended manual CPC phase by 1 week; set target ROAS conservatively at 2.5x initially |
| High CPC in fashion keywords due to competitor aggression | Shifted budget to long-tail keywords; reduced broad match usage by 60% |
| Shopping feed had missing attributes affecting impression share | Enriched Merchant Center feed with GTINs, size, color, and material attributes |
7. Tools & Technologies Used
| Tool | Purpose | Usage |
| Google Ads | Campaign management & bidding | Primary platform |
| Google Analytics 4 | Conversion tracking & attribution | Daily monitoring |
| Looker Studio | Reporting dashboard | Weekly client reports |
| SEMrush | Keyword research & competitor analysis | Initial research phase |
| Google Merchant Center | Shopping feed management | Ongoing feed optimization |
| Google Tag Manager | Tag & event deployment | Setup & maintenance |
8. Conclusion & Recommendations
This campaign successfully exceeded all four primary KPIs set at the outset. The structured account rebuild, combined with phased Smart Bidding adoption and continuous creative testing, delivered a 312% increase in revenue at a 3.8x ROAS.
8.1 Recommendations for Continued Growth
- Scale Shopping campaigns further by separating high-margin and low-margin product groups into separate campaigns with distinct ROAS targets
- Test Performance Max campaigns for broader reach while maintaining Shopping and Search separately for control
- Expand remarketing audiences by creating custom intent audiences based on competitor website visitors
- Introduce YouTube video ads as an upper-funnel brand awareness channel to support Search conversion rates
- Implement offline conversion import from CRM to improve bidding signal quality for high-LTV customers
| Skills Demonstrated
Google Ads • Smart Bidding • Shopping Campaigns • Remarketing • Keyword Strategy • A/B Testing • Analytics • Feed Optimization |