Google Ads Scaling: Case Study on eCommerce Growth

Anirban Sen
6 days ago
13 min read

Scaling Google Ads profitably requires more than increasing budgets - it’s about fixing tracking issues, restructuring campaigns, and using data-driven strategies.

Here’s how a U.S.-based eCommerce brand selling home fitness equipment ($80–$150) transformed its Google Ads performance:

Problem: Scaling budgets dropped ROAS below break-even (1.5–1.8), limiting growth. Tracking and campaign structure were disorganized, with missing data and inefficient spending.
Solution by Senwired:
- Fixed tracking errors and set profit-focused conversion values.
- Segmented campaigns by product category, price, and margin.
- Implemented disciplined scaling rules, targeting metrics like ROAS (3.0+) and MER (2.0+).
Results:
- Monthly ad spend increased 3–4x while maintaining profitability.
- Revenue grew 2–4x, hitting six-figure monthly sales.
- Google Ads became the primary growth driver.

Key Takeaway: Accurate data, clear segmentation, and structured scaling turn Google Ads into a reliable profit engine.

My Google Ads Strategy for Scaling Ecommerce Brands in 2025

Building the Foundation for Scaling

Before scaling budgets, Senwired tackled critical data and structural issues within the brand’s Google Ads account. Without accurate data and a well-organized campaign structure, scaling would have been like driving blindfolded. The account had several fundamental problems that needed fixing before moving forward.

Starting Point Assessment

Senwired began by auditing the Google Ads account to understand its current state. This deep dive revealed several challenges beyond the baseline numbers.

One major issue was the mixing of branded and non-branded search terms in the same campaigns. This made it impossible to distinguish revenue from loyal customers versus new customer acquisitions. Shopping campaigns were another pain point - they weren’t segmented by product category or performance tier, meaning high-margin best-sellers were competing for budget with slower-moving items. Performance Max campaigns were also running without proper asset groups or audience signals, leaving them to operate without direction.

To verify the data, Senwired cross-referenced Google Ads metrics with Google Analytics 4 (GA4). They found discrepancies: GA4 reported 5-10% fewer conversions than Google Ads, and revenue numbers didn’t align with the brand’s backend sales data. Tracking issues were a big part of the problem. Some conversions were double-counted due to incorrect tags, while others - especially mobile purchases - weren’t captured at all. Enhanced conversion tracking hadn’t been enabled, meaning Google’s algorithms were working with incomplete data.

Budget constraints also emerged as a recurring theme. Several Shopping and Search campaigns showed "Limited by budget" status throughout the month. When the brand had previously increased budgets, ROAS quickly declined, suggesting inefficiencies were being magnified rather than resolved. On top of that, impression share data revealed they were capturing only 30-40% of available impressions for high-intent keywords. Without knowing which products were profitable, raising bids on these terms wasn’t a viable option.

Senwired documented all these findings in a comprehensive report, breaking down performance by campaign type, product category, and customer segment. This report became the baseline for measuring progress. The ultimate goal wasn’t just to grow revenue - it was to create a system capable of scaling profitably and predictably. With these inefficiencies identified, the team moved on to address the core measurement flaws.

Fixing Measurement and Data Issues

The audit highlighted the urgent need to fix data inaccuracies. Without reliable data, Google’s algorithms couldn’t make informed bidding decisions, and the brand couldn’t identify which campaigns were driving profit.

The first step was implementing profit-focused conversion tracking. Instead of treating all conversions equally, Senwired worked with the brand to calculate the contribution margin for each product line. For instance, home fitness equipment priced at $80 had different margins than items priced at $150. Custom conversion values were set up in Google Ads to reflect actual profit - factoring in costs like shipping and payment processing - rather than just gross revenue. This allowed campaigns to optimize for profitable conversions rather than just sales volume.

Next, they tackled the technical tracking issues. Enhanced conversions for web were implemented across the site, using hashed first-party data (like email addresses from checkout forms) to improve attribution accuracy. This adjustment boosted match rates by 5-10%, ensuring more conversions were correctly linked to campaigns. The setup was verified using Google Tag Manager’s preview mode and Google Ads’ diagnostics tools to confirm purchase events were firing properly on both desktop and mobile.

Senwired also tightened the integration between GA4 and Google Ads. They imported key eCommerce events - such as add_to_cart, begin_checkout, and purchase - as conversions. Custom audiences were created in GA4 based on user behavior, like “viewed product but didn’t purchase” or “high-value customers.” These audiences would later be used for remarketing and segmentation. Tracking new versus returning customers was another critical addition, helping the team measure incremental growth.

The Merchant Center feed was overhauled to address missing GTINs, vague product titles, low-quality images, and incorrect availability statuses. Product titles were rewritten to highlight the most important details, such as brand name, product type, key features, and use case. For example, “Adjustable Weight Set” became “Brand Name Adjustable Dumbbell Weight Set for Home Gym – 5-50 lbs.” These updates significantly improved click-through rates by aligning titles with customer search behavior. Images were also upgraded, featuring clean white backgrounds and lifestyle shots where possible. Once these fixes were implemented, the brand’s available inventory in Shopping campaigns increased by about 15%, giving Google’s algorithms more products to work with.

Senwired introduced data-driven attribution in Google Ads, replacing the default last-click model. This shift gave credit to upper-funnel touchpoints, such as YouTube and Display ads, that contributed to conversions. By understanding the full customer journey, the brand could make more informed decisions about where to allocate budgets.

Throughout the process, Senwired maintained close collaboration with the brand’s team, explaining each adjustment and its anticipated impact. The focus was on building a reliable measurement system that would not only support immediate scaling but also enable ongoing optimization for the long term. With accurate tracking and a clean product feed in place, the account was finally ready for confident scaling.

Data-Driven Scaling Framework

With precise tracking and clean data in place, Senwired developed a structured framework to guide every scaling decision. The aim was simple: scale profitably using repeatable, data-backed rules. By focusing on multiple metrics, detailed segmentation, and a clear understanding of which sales were genuinely incremental, they ensured smarter, more effective growth.

Metrics Used for Scaling Decisions

Senwired relied on several key metrics to evaluate performance and make informed decisions.

At the account level, the Marketing Efficiency Ratio (MER) acted as the primary benchmark. This ratio, calculated as total revenue divided by total ad spend across all channels (not just Google Ads), ensured a holistic view of performance. Their target MER was set at 2.0x - meaning $1 spent should bring in $2. Additionally, they aimed for a blended ROAS of 3.0x. This approach prevented Google Ads from being optimized in isolation at the expense of other channels.

Campaign ROAS targets were tied to contribution margins. For instance, high-margin products like $150 fitness gear with a 60% margin could sustain a lower ROAS target (2.5x), while lower-margin items required stricter targets (4.0x or higher). By calculating contribution margin (revenue minus costs like product, shipping, payment processing, and ad spend), Senwired ensured scaling decisions were rooted in profit, not just revenue.

Metrics like impression share revealed untapped opportunities. For example, one Shopping campaign with a 3.8x ROAS captured only 65% of its impression share. Recognizing the missed potential, Senwired increased the budget by 20%, boosting impression share to 82% while maintaining the same ROAS.

At the campaign level, target CPA was calculated based on average order value (AOV) and target ROAS. For example, with an AOV of $80 and a target ROAS of 3.0x, the CPA was capped at $26.67. Cold traffic campaigns adhered to this strict CPA target, while remarketing campaigns were allowed a slightly higher CPA. YouTube remarketing campaigns, for instance, operated at a lower ROAS of 2.2x because they drove repeat purchases and boosted overall customer lifetime value.

This layered approach avoided over-reliance on any single metric. If MER stayed above 2.0x, blended ROAS hit 3.0x or more, and campaigns showed potential through metrics like impression share and contribution margin, they were deemed ready for scaling.

These metrics also informed a segmentation strategy designed to maximize efficiency.

Campaign Segmentation Strategy

To maintain control as they scaled, Senwired restructured campaigns by product category, price tier, and profit margin. This granular segmentation clarified which products drove profit and which drained budgets.

First, campaigns were divided by product category - apparel, accessories, and footwear each had their own Shopping and Performance Max campaigns. This structure allowed budgets to be fine-tuned based on category performance and inventory levels.

Next, products within each category were segmented by price tier: budget (under $50), mid-range ($50–$150), and premium (over $150). This enabled tailored bidding strategies. Premium products, with higher AOVs, could support higher CPAs and were given more aggressive bids to secure top ad placements. Budget items, on the other hand, required tighter cost controls.

Profit margin added another layer of precision. High-margin bestsellers were grouped into dedicated campaigns with lower ROAS targets (e.g., 2.8x) to maximize volume. Meanwhile, lower-margin or experimental products were placed in separate campaigns with stricter ROAS targets (e.g., 4.0x or higher) and controlled budgets.

Senwired also distinguished between proven bestsellers and slower-moving items. High-performing SKUs with consistent sales were prioritized for increased budgets, while new or less popular products were tested in separate campaigns with limited spending. Branded search was separated from non-branded search as well. Branded campaigns, with their higher conversion rates and ROAS, were optimized conservatively. Non-branded campaigns, which drove new customer acquisition at around 2.9x ROAS, were scaled more aggressively once they showed potential.

This structured segmentation not only simplified budget management but also provided clearer signals to advertising algorithms. By feeding segmented product groups with precise ROAS targets, automated campaign types like Performance Max and Smart Shopping optimized more effectively.

With segmented campaigns in place, Senwired ensured that additional spending translated into genuine incremental sales.

Incrementality and Attribution Adjustments

Even with strong metrics and segmentation, Senwired needed to confirm that increased spending led to truly new sales rather than just cannibalizing existing revenue. To achieve this, they relied on incrementality testing and adjusted attribution models.

Incrementality tests measured the actual lift from scaling efforts. For example, a four-week geo-experiment that increased budgets by 30% in select regions resulted in a 28% rise in incremental revenue after accounting for external factors.

Another test focused on branded search. By cutting branded search budgets by 50% in certain regions, Senwired discovered that only 15% of branded search revenue was lost. This revealed that most branded search conversions would have happened organically. Armed with this insight, they redirected budgets from overfunded branded campaigns to non-branded and Shopping campaigns with higher potential for incremental growth.

Senwired also moved from last-click attribution to data-driven attribution (DDA). This model used machine learning to assign credit across all touchpoints in the customer journey, better capturing the impact of upper-funnel campaigns like YouTube and Display.

The shift to DDA improved ROAS by 15–25%. For example, YouTube awareness campaigns, which appeared unprofitable under last-click attribution with a ROAS of 1.8x, showed a much healthier 2.8x ROAS under DDA. This justified allocating more budget to upper-funnel efforts that introduced new customers to the brand.

Scaling Tactics and Testing

With a solid framework and segmented campaigns in place, Senwired moved into execution - implementing strategies aimed at driving growth while safeguarding profitability. This required precise budget management, fine-tuning campaign structures, and methodical testing of creatives and audiences. Every adjustment was carefully tracked against key performance metrics to ensure that increased spending led to genuine revenue growth.

Budget and Bid Strategy Changes

Using the refined data and segmentation framework, Senwired made budget adjustments in a controlled and systematic way. Budgets were increased by 10–20% weekly, with close monitoring of ROAS (Return on Ad Spend) and MER (Marketing Efficiency Ratio). For instance, if a Shopping campaign consistently achieved a 4.0× ROAS over two weeks and showed a "limited by budget" status, the daily budget was raised. This gradual increase allowed Google’s algorithms to adapt without triggering a disruptive relearning phase.

Budgets were also adjusted dynamically based on inventory levels. When high-margin bestsellers were running low, budgets were reduced or ROAS targets tightened to maintain profitability. On the flip side, during clearance events or when inventory was plentiful, ROAS targets were relaxed slightly to encourage higher sales volumes.

Advanced bidding strategies were implemented in a phased approach. Campaigns began with Maximize Conversions to gather sufficient data. Once performance stabilized, Senwired transitioned to Target CPA, setting targets at about 30–35% of the average order value. After four to six weeks of consistent results, campaigns were upgraded to Target ROAS, with adjustments made to align with product margins. This step-by-step strategy allowed automated bidding to optimize effectively while minimizing risks.

With bidding strategies in place, attention turned to optimizing overall campaign structures.

Campaign Structure Optimization

As budgets scaled, maintaining the right balance between campaign granularity and data density became critical. Overly fragmented campaigns lacked the data needed for effective optimization, while overly broad campaigns risked losing control over spend allocation between high-margin products and underperformers.

Building on segmentation insights, Senwired consolidated underperforming campaigns into broader segments based on product categories and margins. This allowed for better ROAS optimization. High-performing SKUs were moved into dedicated Smart Shopping or Performance Max campaigns to take advantage of cross-channel reach across Search, Shopping, Display, YouTube, and Gmail. Meanwhile, lower-performing or new products were kept in broader campaigns with tighter budgets and stricter ROAS targets.

Regular search term audits played a key role in reducing wasted spend. By excluding irrelevant keywords like "cheap", "wholesale", or competitor names, Senwired improved ROAS and trimmed unnecessary costs. Similarly, SKU-level exclusions helped refine campaign performance - products with low conversion rates relative to clicks were removed from high-budget campaigns and placed into testing campaigns with controlled budgets. Additionally, optimizing product feeds with richer titles, detailed descriptions, accurate categorization, and high-quality images boosted ad relevance and Quality Scores, reducing CPCs and increasing impression share.

Creative and Audience Testing

Continuous testing of creatives and audiences further fine-tuned campaign performance. Senwired followed a structured cadence, introducing three to five new creative variations every two to three weeks across Search, Shopping, Display, and YouTube campaigns.

For Search campaigns, Responsive Search Ads were tested with different value propositions. Headlines like "Free 2-Day Shipping" were compared to alternatives such as "Free Shipping on Orders $75+" or "30-Day Money-Back Guarantee." One test showed that the "Free 2-Day Shipping" headline increased click-through rates by 12% and conversion rates by 8%, leading to its broader implementation in high-intent campaigns.

U.S.-specific offers resonated strongly with local audiences. Seasonal promotions aligned with American holidays like Memorial Day, Fourth of July, and Labor Day were tested with tailored ad copy and landing pages. Pricing was localized (e.g., $49.99), and urgency cues like "Limited Stock" or "Ships Today if Ordered by 3 PM EST" were added to boost conversions.

In Shopping and Performance Max campaigns, creative testing focused on product imagery. Lifestyle images showing products in real-world settings were tested against clean, product-focused shots. Separate asset groups were created for each style, enabling Google’s automation to determine which creative worked best in different scenarios.

YouTube remarketing was used to re-engage users who had visited the site or watched product videos without converting. Video ads were tested with varied messaging - some highlighting product benefits and others focusing on social proof, such as "Over 10,000 Five-Star Reviews." High-performing video creatives were repurposed across campaigns for consistency.

Audience testing followed a funnel-based strategy. Cold audiences - such as in-market segments, custom intent audiences built from high-value search terms, and lookalike audiences based on high-LTV customers - were tested in Discovery, YouTube, and Performance Max campaigns. Warm audiences, including past site visitors, cart abandoners, and previous buyers, were layered into Search, Shopping, and remarketing campaigns with bid adjustments to prioritize high-intent users. For example, a custom audience targeting users searching for terms like "premium running shoes" or "best trail sneakers" achieved a 3.4× ROAS, proving its scalability and justifying a budget increase. Audience overlap was carefully managed by excluding remarketing lists from cold prospecting campaigns and ensuring high-value customer segments were not targeted in broad Discovery campaigns. This ensured each audience segment played a distinct role in the marketing funnel.

Results and Lessons Learned

After addressing structural and data issues, the eCommerce brand experienced strong, profitable growth through scaling efforts. By sticking to disciplined budget practices, making data-driven decisions, and continuously refining their approach, they turned Google Ads from a limiting factor to a reliable profit engine. Let’s dive into the key performance shifts, campaign contributions, and the main factors behind this success.

Performance Before and After Scaling

Initially, tight budgets and poor targeting held back the account’s potential. However, by implementing a focused, data-driven strategy, one case study revealed impressive results: revenue grew by 2.3×, and Return on Ad Spend (ROAS) improved by 50%. This shift highlights how reallocating resources to high-impact areas can unlock significant growth.

Growth by Campaign Type

Each campaign type played a distinct role in driving growth:

Shopping and Performance Max campaigns: These utilized Google's automation to target high-intent shoppers across Search, Display, and YouTube.
Search campaigns: Focused on capturing high-intent queries, ensuring that the brand appeared where potential customers were actively searching.
YouTube remarketing: Re-engaged past visitors, keeping the brand top-of-mind and encouraging conversions.
Performance Max: This campaign type became increasingly important, leveraging Google's AI to uncover new opportunities that manual strategies might have missed.

As the strategy evolved, the revenue mix shifted, with Performance Max campaigns taking a larger share. This allowed for broader reach and incremental growth opportunities that manual campaign management couldn’t achieve.

What Drove Success

Three key principles emerged as the driving forces behind this success:

Product Feed Quality: Detailed product titles and high-quality images helped Google’s algorithms connect with the right shoppers, improving targeting and conversion rates.
Campaign Segmentation: By dividing products into categories - like high-margin bestsellers versus others - the team could scale top performers aggressively while minimizing risks with less predictable items.
Disciplined Budget Management: Gradual budget increases gave Google’s algorithms time to adapt and optimize without triggering disruptive relearning cycles.

These strategies proved that a combination of precise optimization and thoughtful segmentation can lead to sustainable and profitable growth.

FAQs

How do fixing tracking issues and using profit-based conversion values enhance Google Ads campaign performance?

Accurate tracking is the backbone of any successful Google Ads campaign. It ensures you’re working with reliable data, which is essential for fine-tuning performance and making informed decisions. Without it, you risk basing your strategies on incomplete or incorrect insights - leading to underwhelming results.

Incorporating profit-based conversion values is another game-changer. This approach shifts the focus from simply chasing clicks or raw conversions to prioritizing actions that genuinely boost your business's profitability. By aligning bids with actual profit margins, you can maximize ROI, cut down on unnecessary ad spend, and concentrate your efforts on campaigns that truly drive growth.

Why is data-driven attribution better than last-click attribution for scaling Google Ads campaigns?

Data-driven attribution outshines last-click attribution by taking into account the entire customer journey. Instead of giving all the credit to the final click, this method distributes credit across all the touchpoints that played a role in a conversion. This means you get a clearer picture of which channels and interactions are driving results.

With this deeper understanding of each interaction's value, you can make smarter decisions, fine-tune your ad performance, and allocate your budget more effectively. Over time, this approach helps you scale your Google Ads campaigns with greater confidence and precision.

How does segmenting Google Ads campaigns by product category, price range, and profit margin improve budget management and scaling?

Segmenting your Google Ads campaigns based on product category, price range, and profit margin helps you fine-tune your budget and track performance more effectively. Grouping products with similar traits lets you craft ad strategies that align better with customer intent, boosting your return on investment.

For instance, you might allocate a bigger ad budget to products with higher profit margins, while adopting more frugal bidding strategies for lower-margin items. Breaking campaigns down by price range also ensures your ads resonate with the right audience, delivering tailored messages that minimize wasted spend and enhance campaign efficiency.