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In this study, we break down how digital marketer Caomhan Daly turned a simple Shopify dropshipping store into a nearly $150,000 monthly revenue machine using predominantly Google Shopping Ads. No fancy brand, no massive team—just a focused niche, data-backed research, and an efficient ad setup that drives a steady 3–4x daily ROAS.
Rather than a general store, Daly built a single-niche shop offering a high-search-volume product. He picked items with at least 10,000 monthly searches and $30 profit margin. A clear, simple layout and social proof (customer reviews) boosted trust. The site loads fast, navigation is minimal, and product descriptions run 5–6 paragraphs to cover all buyer questions.
To spot winners, Caomhan used spy tools like Ecom Hunt and Drop Point. He scanned top-performing ads, checked search volumes in Google, and validated competitor pricing. For example, an eyelash curler with 74,000 monthly searches and strong profit margin became a top seller. No endless split testing—just clear data guiding product choice.
About 95% of traffic came from Google Shopping, 5% from Bing Shopping. He ran a standard Shopping campaign first, then added a Performance Max campaign once ROAS was positive. By optimizing the product feed—using SEO-friendly titles like "Best Eyelash Curler for Women" and detailed descriptions—Google’s algorithm found the right shoppers without manual intervention.
Daily revenue averaged $1,294.87, leading to $147,450.17 in one month. The store saw 952 sessions per day. Feed optimization and Google’s automation meant 3–4x ROAS with low fluctuation. Post-purchase upsells lifted net profit margins to around 35%, making the business highly scalable.
By focusing on one niche product, leveraging spy tools for research, and mastering feed optimization in Google Shopping Ads, Caomhan Daly achieved nearly $150K in monthly revenue with a small overhead. The automated nature of Shopping campaigns means founders can step back and reinvest time elsewhere while profits roll in.
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