Notre approche
OneOdio had already seen solid success with their Google Shopping campaigns, but they wanted to take their ad performance to the next level. The challenge was to scale their sales without sacrificing the Return on Ad Spend (ROAS), while also expanding into new markets and customer segments. They needed a strategy that would help them grow their visibility across Google Ads platforms and optimize their campaigns for both performance and cost-efficiency.
WeAdU’s Strategy and Implementation
To help OneOdio achieve their goal of increasing sales while maintaining a strong ROAS, we developed a multi-pronged strategy that went beyond typical optimization tactics. We focused on feed-only Performance Max (PMax) campaigns, combined with several unique, data-driven approaches to boost visibility, drive conversions, and ensure budget efficiency.
WeAdU’s Strategy and Implementation
- Feed-Only Performance Max Campaign with Intelligent Product Prioritization: For our Performance Max strategy, we start with one feed-only Asset Group per product category. This approach prioritizes Google Shopping, the top-performing network for conversions, enabling us to gather quality data on purchase-ready audiences. Dynamic Remarketing also allows us to display targeted ads to returning visitors. After each Asset Group achieves a strong conversion base, we gradually introduce additional assets to reach more networks—Search, Display, YouTube, and Discovery—capitalizing on high-quality conversion data to drive further growth.
- Advanced Audience Signals for Hyper-Targeting: By integrating first-party customer data, such as past purchases and website interactions, we created custom audience segments (e.g., frequent buyers, high-value customers). We also utilized Customer Match to target existing customers with personalized ads for product upgrades and complementary items. To enhance targeting, we leveraged RLSA (Remarketing Lists for Search Ads) to retarget visitors who didn’t convert, using dynamic offers. Additionally, we used lookalike audiences for new potential buyers and high-intent audiences to attract users actively searching for audio products
- Granular Product Group Segmentation in PMax: By segmenting products into distinct groups based on performance, demand, and profitability, we developed strategies tailored to each category. For high-performing lines, we applied aggressive bidding, while for lower-performing items, we implemented more conservative tactics. We applied segmenting products by price and margins strategy to bid more aggressively on high-margin or best-selling products, ensuring efficient budget allocation.
- Dynamic Price and Inventory-Based Ad Adjustments: Integrated dynamic pricing and inventory updates ensured that ads always reflected the most competitive offers. If prices changed or products went out of stock, the ads were updated in real-time.
- Performance-Based Budget Allocation: Rather than a uniform budget allocation, we adopted a performance-based budget approach. We increased the budget for campaigns that consistently hit ROAS targets, while reassessing underperforming segments. Weekly budget reviews helped us allocate funds dynamically, focusing on campaigns delivering the best results. During high-demand periods we scaled campaigns aggressively to capture more market share, maximizing ROAS.
- Dynamic Search Ads (DSA): To capture a wider audience, we utilized Dynamic Search Ads (DSA), allowing us to expand keyword reach beyond typical keyword-based campaigns. This enabled us to target potential customers searching for terms that standard Shopping campaigns might miss. DSAs proved especially effective for expanding product discovery in a competitive space.
- Optimizing for Customer Lifetime Value (CLV): We emphasized maximizing customer lifetime value by focusing on high-value customers who were likely to make repeat purchases. We adjusted bids based on customer history, ensuring a long-term profitability focus. By identifying high-value, long-term customers, we optimized bids and strategies to improve campaign profitability over time.
