Why your retail ads struggle? How AI-driven Google Ads boosts conversions by 35%
The cross-border e-commerce market is expected to exceed $1.2 trillion by 2025, with non-English markets becoming the primary growth engine for mobile game downloads. Recent data from CSA Research shows that 82% of consumers prefer to purchase products advertised in their native language, while 42% of US consumers abandon a transaction due to payment method discrepancies. This conflict between globalization and localization is being cleverly resolved by AI technology. Google Ads video dubbing tool can automatically generate natural audio in 200 languages, while pet supply retailer Fressnapf achieves precise targeting in Southeast Asia through its Performance Max campaigns. While traditional marketing teams still manually translate ad copy, AI has already increased localization efficiency by 400%, signaling the need for marketers to transition from execution to strategic decision-making.
I. The Transforming Role and Core Value of Marketers in the AI Era
1.1 Key Differences Between Traditional and AI-Driven Marketing
There are fundamental differences between traditional and AI-driven marketing. These differences are reflected not only in execution efficiency but also in a shift in the overall marketing mindset. In traditional marketing, marketers must manually conduct keyword research, create campaigns, and develop delivery strategies. This is not only time-consuming and labor-intensive, but also difficult to adapt to rapidly changing market conditions. Alexandra Meyer, Senior Market Management Specialist for Southeast Asia at Fressnapf, initially expressed reservations about AI tools, reflecting this traditional mindset. She admitted, "We always ask ourselves: Is the information we're using correct? Some pages might contain outdated information that users won't see, but the Google Ads AI will discover and leverage it." This concern about data accuracy reflects the psychological shift among marketers from manual control to trust in AI decisions.
1.2 The Marketer's Path from Executor to Strategic Decision-maker
The introduction of AI isn't meant to replace marketers; rather, it frees them from mundane execution tasks and allows them to transition to a more strategic role. Fressnapf gradually realized this through its participation in the Google Ads program. The eight-month program not only provides personalized consultations but also exposes participants to industry experts and the latest AI use cases. Alexandra Meyer discovered that AI performance improved significantly when she took the time to explicitly exclude certain areas. This experience made her realize that marketers' new role is "AI trainer"—guiding AI tools to maximize their effectiveness by setting the right parameters and boundaries. This shift allows marketers to focus more time on high-value tasks such as market strategy, brand positioning, and customer experience design.
II.Google Ads' AI Technology Architecture and Practical Applications
2.1 How Performance Max Campaigns Work and Effectiveness Analysis
Performance Max is a revolutionary advertising solution launched by Google Ads. Powered by goal-driven AI technology, it automatically optimizes ad delivery strategies across all Google platforms, including search, display, and Maps. The core of this technology lies in its dynamic learning capabilities—the system continuously analyzes user behavior data to automatically adjust ad creative, bids, and delivery timing. According to official Google data, advertisers using Performance Max have seen an average 18% increase in conversion rates. Fressnapf's case is particularly compelling. Initially skeptical of AI, they even proactively limited certain features. Ultimately, testing showed that Performance Max helped them achieve significantly higher conversion rates within the same budget. This success is no accident; it builds on years of breakthroughs in Google's large-scale language models, which have improved their understanding of human language by approximately 50%, enabling more precise ad matching.
2.2 Data Training and Optimization Examples for Broad Match Keyword Technology
Broad match keyword technology represents another major innovation in Google Ads AI. Traditional keyword matching methods require marketers to manually input a large number of exact or phrase match keywords, while broad match allows the AI system to automatically expand to relevant queries based on a small number of seed keywords provided by advertisers. Data shows that campaigns that switched from exact to broad match saw an average conversion rate increase of 35%. Fressnapf's experience is particularly valuable. Their initial testing of broad match saw limited results, but through the Google Accelerate program, they realized the problem lay not with the technology itself but with the quality of the training data. Once they took the time to explicitly exclude irrelevant queries, the AI system's performance immediately improved significantly. This demonstrates that the successful application of broad match technology requires close collaboration between marketers and AI systems—marketers provide clear business boundaries, while the AI system explores the best solutions within those boundaries.
III. Analysis of Successful Case Studies in AI Marketing in the Retail Industry
3.1 How Fressnapf Overcame Trust Barriers in AI
Fressnapf's AI transformation journey provides a valuable model for building trust in the retail industry. This European pet products giant was initially skeptical of AI tools, particularly regarding core marketing functions like ad creation and delivery. Alexandra Meyer admits, "You always assume manual processing will yield better results, and you need evidence or data to prove otherwise." This skepticism initially led them to limit their use of AI features, even proactively disabling certain automation options. The turning point came after joining the Google Accelerate program. Through a scientific maturity assessment and eight months of personalized coaching, the program helped the Fressnapf team build a healthy trust in AI systems. Notably, this trust wasn't blind; it was based on rigorous testing and data validation. For example, they found that providing clear exclusion criteria significantly improved the accuracy of broad-match keywords. This experience has gradually shaped the Fressnapf team's philosophy for working with AI: rather than blindly accepting or rejecting AI recommendations, the team guides the AI system in the desired direction through clear business rules and continuous performance monitoring.
3.2 Budget Allocation Strategies for Rakuten Ichiba's Cross-Platform Retail Media
The retail media collaboration between Rakuten Ichiba and Google reveals a new approach to budget allocation in the AI era. Traditional retail advertising is often confined to e-commerce platforms. However, Rakuten's promotion platform expansion solution allows brands to leverage Rakuten Ichiba's first-party data to precisely reach customers on external platforms like Google Ads. Data shows that the consumer overlap between Rakuten Ichiba's on-site and off-site retail media is only 4%, indicating that a cross-platform strategy can effectively expand brand reach. A more groundbreaking discovery came from budget allocation experiments: optimal sales growth was achieved when brands increased their daily advertising budget to at least twice the unit price of their products. This contrasts sharply with the traditional "break-even" approach and demonstrates that AI-driven advertising systems, with sufficient budget support, can identify and seize high-value opportunities that are difficult for human marketers to foresee. Rakuten's case demonstrates that in the AI era, advertising budgets shouldn't be viewed as fixed costs, but rather as dynamically adjustable growth levers. Allocation strategies need to be based on real-time data rather than historical experience.
IV. Topkee's Google Ads Solution
Topkee provides one-stop online advertising services based on Google Ads, aiming to help businesses increase leads and boost sales. Regardless of client size, Topkee offers tailored solutions covering the entire advertising process, from initial evaluation to optimization.
First, Topkee conducts a comprehensive website assessment and analysis, using the latest website scoring tools to thoroughly examine the client's website and produce a detailed SEO problem report with recommended solutions. This service not only includes technical SEO optimization but also focuses on improving content quality, ensuring that website information complies with search engine frameworks and is valuable to users, thereby improving search rankings and conversion rates. Regarding technical integration, Topkee has developed the TTO tool, an efficient multi-account management system. Through TTO, clients can centrally manage multiple advertising accounts, enabling operations such as account opening, budget association, and permission settings. Furthermore, TTO supports multi-tag ID concatenation, enabling precise tracking of data across channels and automatically syncing it to the advertising backend based on conversion goals, significantly reducing manual operation costs.
For ad tracking needs, Topkee's TM tool offers a more flexible solution than traditional UTM. TM allows clients to customize tracking rules and generate unique TMID links based on metrics such as ad source, media type, and campaign name. This allows marketing teams to monitor ad effectiveness in real time and adjust their campaign strategies accordingly. During the ad strategy planning phase, Topkee gathers information based on market trends, competitive analysis, and other factors, tailored to the client's business needs, to produce professional and innovative campaign proposals. These proposals are proven feasible, enabling clients to quickly launch high-quality marketing campaigns and saving time in pre-production. Keyword research is one of Topkee's core services. The team analyzes the client's industry characteristics and competitor strategies to identify highly relevant core keywords. Combining these with smart bidding and broad match techniques, Topkee expands ad reach while maintaining targeting accuracy. This approach effectively improves ad reach and conversion efficiency.
Conclusion
From Fressnapf's AI trust journey to Rakuten Ichiba's data-driven breakthroughs, these cases clearly demonstrate that AI doesn't replace marketers; it elevates them to a more strategic role. Google Ads, as the key interface between AI technology and marketing practices, is redefining the rules of retail competition. When marketers learn to set the right business rules, interpret AI-generated insights, and remain committed to their brand's core values, they can unleash AI's true potential. It's worth noting that these transformations don't happen overnight. Fressnapf spent eight months in the Accelerate program to build solid AI capabilities, a reminder that success requires patience and systematic investment. If your business is considering an AI marketing transformation, consider starting with Google Ads' Performance Max or Broad Match features and gradually gaining experience. Seeking professional guidance from programs when needed will significantly reduce the learning curve. The AI marketing revolution is here; the question is no longer whether to participate, but how best to participate.