Business Challenge

Our e-commerce business, specializing in premium water softeners, had strong product offerings but we struggled with scaling online sales through traditional search campaigns. So my task was:

1. Increase online sales for high-ticket water softener systems (the Perla Silk Wifi water Softeners).

2. Improve return on ad spend (ROAS) while reducing the cost-per-acquisition (CPA).

3. Reach new customers through more efficient use of Google Ads, especially with a relatively niche product.

Given the industry's competitive nature and high product prices, we needed a strategy that balanced visibility with efficient targeting to attract relevant traffic while avoiding wasted spending.

Strategy and Approach

To overcome these challenges, we built a comprehensive Performance Max (P-Max) campaign using the following steps:

1. Audience and Keyword Strategy

Keyword Research: We conducted detailed keyword research using SEMrush, Google Keyword Planner, and Google Transparency Ad Centre to identify relevant keywords and competitors' ad content. The approach included:

• Broad Match to Exact Match Funnel:

  • Phase 1 (Discovery): We started with broad match keywords related to water softeners, such as “water softener systems,” “hard water solution,” and “best water softeners,” which helped us gather data on search trends and user behaviour.

  • Phase 2 (Narrowing): Once data was collected, we transitioned to phrase match and exact match keywords based on high-performing search terms. These included keywords like “buy water softener online,” “water softener installation,” and specific product names (branded keywords).

This tiered approach allowed us to explore new opportunities while ensuring budget was spent on highly relevant traffic as we optimized.

2. Segmentation of Ad Groups

Segmentation by Product Types:

• I segmented my ad groups based on the following:

  • Ad Group 1: High-end water Softeners (e.g., premium systems targeting luxury homeowners or commercial buyers)

  • Ad Group 2: Mid-Range Products (for general residential users)

  • Ad Group 3: Budget-Friendly Water Softeners (targeting cost-conscious buyers)

Custom Intent Audiences:

• Built custom audiences based on Google Analytics data, using signals like:

  • Users searching for terms related to water hardness problems.

  • Competitor product research (searching for competitor brands).

  • Online DIY or home improvement enthusiasts looking for plumbing solutions.

3. Smart Bidding Strategy with Target ROAS

Bidding Strategy:

• We employed Smart Bidding with Target ROAS (tROAS) to maximize return on ad spend. We set an initial tROAS goal of 400% based on historical data.

• As P-Max is designed to leverage machine learning, we allowed Google's algorithm to automatically adjust bids based on real-time data signals, such as user location, device, and time of day, ensuring we reached the right audience with the right bid.

Automation & Machine Learning:

• Performance Max campaigns use Google's AI to optimize across Search, Display, YouTube, and Shopping (Shopping was excluded to enable create room for our merchant to advertise without competing with them):

  • Search Ads for users actively looking for water softeners.

  • Display Ads to retarget users who previously visited the website.

  • YouTube Ads for users consuming home improvement content, showcasing product demonstration videos.

Execution

4. Ad Creative Strategy and Formats

Ad Copy:

• Focused on benefit-driven messaging highlighting the value of soft water (e.g., "Say Goodbye to Hard Water!" or "Extend the Life of Your Appliances with a Water Softener").

• Used urgency and offers, like “Free Shipping” or “10% Off First Order,” to incentivize clicks.

Ad Types:

• Dynamic Search Ads for relevancy.

• Responsive Search Ads with multiple headlines (e.g., “Buy Water Softener Online,” “Best Water Softener for Hard Water”) and descriptions for testing various combinations.

• Shopping Ads to highlight product pricing and features directly in search results.

5. Ad Scheduling & Budget Allocation

Ad Scheduling:

• Focused on peak hours based on Google Analytics data, primarily during weekday mornings and late afternoons when users were most active.

Budget Allocation:

• We allocated 70% of the budget to high-intent product-based search terms, while 30% went toward exploratory broad match terms to uncover new opportunities.

• Continuously monitored budget distribution and shifted more toward high-performing segments.

Results

Within the first 90 days of launching the Performance Max campaign, we achieved outstanding results:

1. Sales Growth:

   • The business experienced a 28% increase in online sales for water softeners.

2. ROAS Achievement:

   • The campaign hit a 500% ROAS within 3 months, exceeding the initial target of 400%.

3. Conversion Rate Improvement:

   • The conversion rate increased by 35%, thanks to better-targeted ads, smart bidding, and optimized audience segments.

4. Cost Efficiency:

   • The CPA decreased by 20%, as Performance Max’s machine learning optimized bids and adjusted ad spend automatically across channels to focus on high-performing audiences.

5. Ad Reach & Engagement:

   • Dynamic creative assets resulted in a 22% increase in ad engagement, especially on Display and YouTube ads where visually appealing content resonated well with users.

Key Takeaways For Me

1. Performance Max Campaigns are highly effective for full-funnel optimization, allowing businesses to reach users across multiple channels (Search, Display, Shopping, YouTube) with one unified campaign.

2. Smart Bidding with Target ROAS ensured efficient use of budget by focusing on conversions and delivering high returns (though the campaign was switched later to target CPA)

3. A broad match to exact match keyword strategy helped balance exploration and efficiency, ensuring we captured valuable long-tail traffic while minimizing wasteful spending.

4. Custom Intent Audiences were crucial for targeting niche customer segments, especially in a competitive and specific market like water softeners.

Conclusion

By leveraging Google’s Performance Max campaign, I successfully increased e-commerce sales, optimized ROAS, and decreased CPA for our water softener campaign. This approach combined a smart, data-driven bidding strategy with automated creative optimization across Google’s ecosystem, ensuring continuous improvements and scalable growth.

This case study highlights the power of Performance Max campaigns and a targeted approach for e-commerce stores looking to maximize online sales. My data-centric approach ensures that every dollar spent drives measurable results, making me confident in my ability to deliver similar success for future clients.