Are you leveraging Google Retail Collaborative Ads for maximum ROI?
In July 2025, Google officially launched its revolutionary "AI Max Search Matching Mode." This innovation uses deep neural networks to analyze landing page content and historical data to automatically match ads to high-potential search intent, fundamentally changing the traditional keyword-based advertising model. This marks Google's move toward a "keyword-free advertising" era, where advertisers simply need to enable the feature at the account level and eliminate invalid traffic using negative keywords. In response to this industry shift, international companies like Otto and Rakuten Ichiba have already adapted their strategies, but many Google Ads beginners are still unaware of the pitfalls of traditional operations. This article will delve into the three most common pitfalls for beginners and provide specific solutions and success stories to help you stand out in the new era of AI-driven digital advertising.
I. Overview of Common Pitfalls for Google Ads Beginners
1. Excessive Manual Control and Complex Settings
Otto's case perfectly illustrates the negative impact of excessive manual control. The company's previous performance marketing system, like a delicate clock, consisted of countless small gears that required manual adjustment. Over time, the system's complexity grew, ultimately hindering growth. The specific problems manifested themselves on three levels: First, excessive fragmentation of advertising campaigns. Otto had long relied on numerous small-scale campaigns and exact-match keywords, making strategy development and management significantly more complex. Second, overly granular keyword management reduced clarity and made it difficult for the team to quickly identify new search trends. Finally, a rigid ROAS model overemphasized short-term efficiency while neglecting strategic goals like customer acquisition. These issues ultimately prompted Otto to undergo a radical overhaul, transitioning to broad match and AI automation. The results were astonishing: while reducing the number of keywords by 50%, conversions increased by 7%, clicks by 20%, and cost per click decreased by 27%. This demonstrates that in the AI era, micromanagement is no longer optimal; trusting the algorithms and simplifying the architecture are the keys to achieving greater efficiency.
2. Data Latency and Incomplete Tracking
Data issues are common, yet subtle, pitfalls for Google Ads novices. Otto suffered from these issues even before the transition: conversion data transmitted to Google's systems through internal attribution was delayed by up to 48 hours, and data quality was inconsistent. This delay severely impacted AI-driven smart bidding tools, as the lack of real-time signals prevented the system from making optimal bidding decisions. Furthermore, Otto's original lack of direct Google conversion tracking meant that key metrics like engagement and conversions were not systematically factored in, indirectly reducing advertising effectiveness. These data gaps are particularly critical in an AI-driven advertising environment, as machine learning relies heavily on a real-time, accurate data stream to optimize performance. Otto's solution was a comprehensive upgrade of its tracking architecture, adopting server-side GTM and Google Ads conversion tracking to ensure all relevant data was instantly available. This transformation laid a solid foundation for subsequent AI automation and highlighted the critical importance of data immediacy in modern digital marketing. New advertisers must understand that in today's fast-paced digital environment, data older than 48 hours may no longer reflect current market conditions. Establishing a real-time data pipeline is no longer an option but a necessary investment.
3. Siloed strategies and budget inflexibility
Siloed strategies are common obstacles hindering the full effectiveness of Google Ads. Before the transformation, Otto faced severe channel fragmentation: its website and app strategies operated independently, lacking synergy. This siloed management led to misallocation of resources and inability to maximize overall effectiveness. Even more serious was the issue of budget rigidity. Otto's original fixed budget constraints prevented it from dynamically adjusting spending based on market opportunities. This lack of flexibility was particularly detrimental in the ever-changing digital market, potentially leading to under-budgeting during high-potential periods and overspending during less-productive ones. Otto's breakthrough came in implementing an "app-first" strategy, using Web to App Connect to direct mobile search users directly to installed apps and assigning higher conversion values to app installs. GeoX incrementality testing showed that in regions where budgets for app install ads were increased, users engaged more frequently with shopping ads, resulting in significantly higher conversion rates and average purchase amounts. This demonstrates the dramatic benefits of breaking down channel silos and implementing dynamic budget allocation. For beginners, this emphasizes the importance of integrated strategic thinking—in today's cross-platform user journey, optimizing any single channel is limited; only omnichannel collaboration can unlock maximum value.
II. Key Lessons from the Success Story
1. Rakuten Ichiba's Cross-Channel Retail Media Strategy
Rakuten Ichiba's cross-channel retail media strategy provides an excellent example of integrated thinking in Google Ads. As a leading global e-commerce platform, Rakuten Ichiba discovered that the consumer overlap between its on-site (on-site website) and off-site (through platforms like Google Ads) retail media was only 4%, meaning that the two reached distinct audiences. Based on this insight, Rakuten Ichiba accelerated its investment in off-site retail media solutions, partnering with Google to launch the Rakuten Promotion Platform Expansion. This innovation allows brands to leverage Rakuten Ichiba's first-party data to optimize advertising campaigns on the platform and reach highly relevant customers outside of the e-commerce platform. Rakuten's experiment yielded two key findings: First, showcasing a wider range of products in off-premise retail media advertising (rather than the traditionally limited selection of select items) significantly boosted sales; second, optimal sales growth was achieved when daily budgets were increased to at least twice the product's unit price. These findings overturn many assumptions about traditional retail advertising, demonstrating that in the modern, fragmented consumer journey, expanding reach and increasing investment can actually yield better returns. For newbies, this emphasizes the importance of cross-channel strategies and a spirit of experimentation—only through systematic testing can one discover the advertising approach that works best for their business.
2. Monks' AI-Powered Personalized Advertising Results
Monks' AI-powered personalized advertising campaign for sleep health company Hatch demonstrates the incredible potential of combining Google Ads with AI-powered creative. Faced with time and budget constraints, Monks used Google's Gemini AI to quickly analyze market trends, identify high-potential audiences, and generate highly personalized ad creative. The results were impressive: an 80% increase in click-through rate, a 46% increase in website visitor engagement, and a 31% reduction in cost per acquisition compared to traditional campaigns. Even more astonishing is the efficiency gains—compared to traditional methods, time commitment was reduced by 50% and costs by 97%, with the entire process from conception to launch taking only half the usual time. Monks' secret lies in deeply integrating AI into the creative process: first, using Gemini for audience research and persona development, they identified three core groups: stressed-out professionals, biohackers, and health enthusiasts. AI was then used to generate ad creative tailored to each group's aesthetic preferences and lifestyles. Finally, through a performance-maximizing campaign, Google AI instantly determined which ad variant each user would see. Brittany Blanchard, Executive Vice President of Digital Media at Monks, noted, "If we only consider efficiency gains, we're only scratching the surface." This case study demonstrates that when creative strategy is deeply integrated with the AI capabilities , not only can efficiency be improved, but personalized scale is also possible, "impossible" with traditional methods.
III. Topkee's Google Ads Solutions
Topkee provides professional and comprehensive one-stop Google Ads services, helping companies effectively improve online advertising effectiveness and achieve business growth goals. Our services encompass the entire advertising lifecycle, from pre-launch evaluation to post-launch optimization, providing tailored solutions for businesses of all sizes.
Pre-launch, we begin with a comprehensive website assessment and analysis. Using advanced website scoring tools, we conduct an in-depth examination of our client's website, generating a detailed SEO problem report and optimization recommendations. This phase not only includes technical SEO structural adjustments but also focuses on content optimization to ensure that website information accurately reflects potential customers' search intent, thereby improving organic search rankings and landing page conversion efficiency.
To achieve targeted advertising, we utilize a proprietary TTO tool for systematic setup. This tool features multi-account management, integrating core operations such as media budget allocation, ad account authorization, and tag ID association. Through automated conversion event setup and data synchronization, clients can track advertising effectiveness in real time, significantly reducing manual operations costs. We also implement TM tracking technology, a more flexible system than traditional UTM. This system allows for customized tracking parameters based on multiple dimensions, such as ad source, media type, and campaign name, generating unique TMID links, allowing clients to clearly understand advertising performance data across various channels.
During the advertising strategy planning phase, our professional team collects multi-dimensional data and generates creative marketing proposals based on the client's industry characteristics and market needs. Through a systematic theme proposal process, we can quickly generate a large number of customized campaign plans, effectively saving clients time in planning. Keyword research is another core service. We use competitor analysis and keyword tools to identify core keywords with high conversion potential. Combined with smart bidding strategies and broad matching technology, we expand advertising reach while maintaining precision.
Conclusion
In the new era of AI-driven digital advertising, Google Ads' operational philosophy has undergone a fundamental shift. Success stories from Otto, Rakuten Ichiba, and Monks demonstrate that excessive manual control, data latency, and siloed strategies are the three most common pitfalls for beginners to be wary of. Instead, trusting AI automation, ensuring data immediacy, and maintaining strategic flexibility are the keys to achieving optimal advertising results. Google's launch of AI Max search matching in 2025 heralds the advent of "keywordless advertising," shifting operational focus from "how to select keywords" to "how to define business goals and value propositions." Newcomers shouldn't be intimidated by these changes; instead, they should view them as an opportunity to surpass established competitors. In the AI era, adapting to new thinking is more important than accumulating experience. If you encounter implementation challenges, we recommend seeking the assistance of professional consultants. They can help you translate your business strategy into AI-enabled optimization logic, accelerating your maturity journey. Remember, in this digital marketing revolution, the greatest risk isn't making mistakes, but stagnating.
