How to optimize Google Ads performance with AI integration?

On May 30, 2024, Google Ads officially announced the full launch of the newly designed Google Ads operating interface, marking an important moment of change in the digital advertising field. According to the official announcement, it has been mandatory on 30, 2024, and the old interface will be completely deactivated. This change is not only a visual update, but also represents Google's strategic layout for the deep integration of AI technology. In the current economic environment, consumer behavior is becoming more cautious, and corporate marketing budgets are tightening. How to improve advertising effectiveness through technological innovation has become a key issue. 12% of large and medium-sized companies in Spain have begun to use AI technology to optimize marketing strategies, and the revision of Google Ads solutions is precisely to respond to this new normal of data-driven, AI-enabled marketing. This article will deeply analyze the focus of this revision, AI technology application cases, and how companies can seize this opportunity for change and create a competitive advantage.

I, Operation Interface Revision Focus Analysis

1. Navigation Structure Optimization and Functional Module Reorganization

The most notable change in the new version of Google Ads is its redesigned navigation system. Traditional sidebar navigation has been replaced by more intelligent situational navigation, capable of dynamically displaying relevant functional options based on the user’s current work content. For example, when users focus on audience management, tools related to audience analytics, lookalike audience creation, and predictive audience applications are automatically highlighted. This situational awareness design significantly reduces the learning curve for new users while improving the productivity of senior users. The restructuring of the functional modules also reflects Google’s in-depth understanding of the advertiser’s workflow, clearly segmenting the three stages of “planning”, “execution” and “analysis” to ensure that each marketing goal is supported by a corresponding toolchain. In practice, this change enables smoother cross-team collaboration, with members of different functions quickly locating desired functions.

2. Visual Design Updates and User Experience Improvements

The update of visual design is not only an aesthetic change, but also an innovation in the way information is presented. The new version of the interface adopts a higher information density design, integrating more valuable data and controls in a single view. The redesign of the color system reinforces the visual salience of critical calls to action (CTAs) and key metrics to help advertisers quickly identify areas of concern. In terms of data visualization, the new version adds more interactive charts, allowing users to perform hover, filter or down-drill operations directly on the data points without having to switch to a separate report page. These improvements are particularly conducive to scenarios that require quick decision-making, such as holiday promotions or limited-time events, where advertisers are able to monitor results and adjust strategies in real time. In addition, the optimization of the adaptive layout ensures a consistent operating experience across different screen sizes and resolutions.

3. Cross-Device Support Strategy (Desktop-Specific Design)

Google has particularly emphasized that the new design is exclusively optimized for desktop computers, which reflects deep insights into the professional ad management scenario. Unlike the action-first design philosophy, the new version of Google advertisements takes full advantage of the spatial advantages of large screens to provide richer simultaneous operation capabilities. For example, users can now preview presentations on different devices in the same window while modifying ad copy, a parallel workflow that significantly improves creation efficiency. Improvements to multi-tab browsing also allow advertisers to quickly switch contrasts between different campaigns without worrying about performance degradation. For advanced users using a multi-display setup, the new interface supports more flexible window management, allowing you to place data dashboards, editors, and live reports on separate screens to create a truly professional-grade ad management environment.

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II, Artificial Intelligence Integration and Marketing Practical Applications

1. AI-Powered Advertising Personalization Technology (Making Science as an Example)

The breakthrough progress of AI technology in advertising personalization can be verified by the “ad-machine” solution developed by Making Science for the tourism industry. This natural language generation (NLG)-based system transforms product data into highly personalized Google copy in real time. Iberostar Hotel Group, for example, has traditionally been able to maintain only three static ads per hotel, and with the introduction of AI solutions, a single ad can derive more than 100 personalized variants that automatically adjust messages based on real-time room status, prices and offers. This dynamic optimization has increased Iberostar’s return on ad spend (ROAS) by an astonishing 4x. The core advantage of AI personalization lies in its ability to capture users’ immediate intentions and cognitive biases, generating messages that best meet the needs of the moment, rather than relying on pre-set limited templates. This technology is particularly suitable for industries with variable product attributes and frequent price fluctuations, such as tourism, e-commerce and financial services.

2. Data Activation Case: CDP Architecture and ROAS Improvement of Riu Hotels

The case of Riu Hotels shows how first-party data activation can achieve a qualitative leap through AI. The group has partnered with Making Science to build a dedicated customer data platform (CDP) on Google Cloud that integrates multi-dimensional data such as booking cancellations, loyalty programs and consumer behavior. The key to this transformation lies in breaking down traditional departmental data silos and unifying the goals and workflows of IT, data analytics and marketing teams. With an AI solution named Gauss, Riu is able to predict which users are most likely to cancel their reservations, thereby adjusting their bidding strategy to focus on high-value customer segments. The results showed that this data-driven approach not only increased bookings by 99%, but also doubled ROAS. This case highlights a key shift in modern marketing: from the pursuit of maximum exposure to precision value acquisition. Enterprises need to build a cross-functional data collaboration culture and invest in AI infrastructure capable of processing and analyzing first-party data in real time to maximize the value of data under the premise of privacy compliance.

3. Seasonal Demand Breakthrough: PortAventura’s AI Audience Prediction Model

The seasonal demand challenge faced by the theme park PortAventura World is a typical dilemma for many tourism and leisure ventures. With partner T2ó’s Vimana AI solution, they successfully identified audience segments with potential demand during non-traditional peak seasons, such as during Mardi Gras in February. This system combines enterprise first-party data with market intelligence, using statistical models and machine learning to predict consumption intentions. After activating these insights on the Google Marketing Platform, PortAventura not only gained 30% new customer growth, but also increased total transaction volume by 68%. This case demonstrates the value of AI audience forecasting in breaking down inherent patterns in the industry: its ability to uncover subtle demand patterns that human analysts may overlook and translate these insights directly into executable media strategies. For industries with significant seasonal fluctuations, such as tourism, retail and education, investment in AI-driven demand forecasting has shifted from a competitive advantage to an essential strategy.

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III, In-Depth Analysis of Industrial Benchmark Cases

1. Baur Versand’s Predictive Audience Test Results

Baur Versand’s six-week test during Black Friday 2022 provided valuable insight into predicting actual audience performance. The German mail orderer originally relied on customer segmentation (A/B/C category) defined by an internal BI system, but GA4’s predicted audience helped them discover a new high potential customer base of 70%. Notably, Baur’s team took a step-by-step approach to testing: first using the predicted audience for dynamic display ads, and the Bid Manager team gradually adjusting the goal from maximizing conversions to optimizing CPA. This prudent transition strategy ensured data quality during the learning period while controlling for risk. The most impressive of the test results was the ability to maintain low customer acquisition costs during the highly competitive Black Friday, which demonstrates the robustness of AI predictions under market pressure. Baur’s next step is to combine predicted audiences with existing customer segments to create a more granular audience matrix and explore prediction-based dynamic creative optimization (DCO) possibilities.

2. MediaMarktSaturn’s PIPA System and Shopping Advertising Optimization

MediaMarktSaturn's PIPA system represents a mature example of AI applications in the retail industry. The four-year-operating system continues to accumulate benefits, and its success is key to three levels: technology centralization, data quality assurance, and interface optimization. Technically, PIPA is built on Google Cloud, unifying the workflow of ad management, analytics and creative generation. At the data level, MMS invests significant resources to ensure the integrity and timeliness of product data, including inventory status, competitive price and localization factors. In interface design, PIPA’s deep integration with Google Ads solutions enables insights to translate directly into bid adjustments and product selection. Particularly worth drawing on is MMS’s cross-departmental collaboration model, where from marketing to operations teams are involved in the continuous optimization of PIPA to ensure that the system reflects real business needs rather than just a technical perspective. This organization-wide AI adoption allows MMS to maintain its leadership in the European electronics retail market, achieving double-digit growth even in a high-inflation environment.

3. Cross-Industry AI Marketing Solution Migration Possibilities

Analyzing AI application cases in tourism, retail and e-commerce can extract key patterns of cross-industry migration. Making Science’s advertising machine was originally designed for the tourism industry, but its NLG core technology is equally applicable to the automatic generation of e-commerce product descriptions or personalized advice for financial services. Riu Hotels’ customer data platform (CDP) architecture can also be replicated to any industry with rich customer interaction data, such as telecommunications or health insurance. The key to successful migration lies in identifying industry-specific variables: tourism is concerned with housing conditions and seasonality, retail is focused on inventory and price elasticity, and B2B services may be more focused on sales cycles and decision hierarchies. In practice, enterprises should start with closed testing, selecting a specific use case with well-defined KPIs (such as shopping ad automation or predictive audience targeting), verifying feasibility before expanding incrementally. Pia Media has begun applying the PIPA system to tourism and finance, proving that an excellent AI marketing framework does have cross-industry adaptability.

IV. Topkee’s Google Ads Solution

Topkee’s Google Ads solution provides an enterprise’s one-stop online advertising service that effectively enhances lead generation and sales results through professional digital marketing strategies, regardless of client size. The solution covers a complete service chain from pre-evaluation to post-optimization. At the technical integration level, Topkee's exclusive TTO tool can centrally manage multiple advertising accounts, realize automated operations such as budget association, permission setting, and accurately track multi-dimensional data through tag ID concatenation;

For campaign planning, the Topkee team performs in-depth keyword research based on customer industry characteristics, expands the keyword library through competitive analysis and intelligent matching strategies, and combines artificial intelligence technology to generate highly relevant advertising copy and visual materials. Particularly noteworthy is its remarketing technology, which analyzes user behavior data through the TTO system and builds a precise audience segmentation model. According to statistics, this personalized remarketing strategy can increase the purchase intention of ad clickers by more than 70%. In terms of performance management, in addition to regularly providing complete reports containing core metrics such as conversion rate and ROI, it also makes dynamic optimization suggestions from budget allocation, bidding strategy and other aspects to help customers continuously adjust their delivery strategies to achieve the best return on investment. Overall, Topkee helps enterprises build efficient and cost-effective digital marketing architectures in the Google advertising ecosystem through systematic data analysis tools and customized marketing strategies.

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Conclusion:

The revamp of Google Ads solutions is not only an interface update, but also marks a new stage of AI-driven digital advertising. From Iberostar’s 4x ROAS to PortAventura’s 68% transaction growth, leading businesses have demonstrated the transformative impact of AI in advertising personalization, audience prediction and value optimization. As the August 30 deadline for a comprehensive revamp approaches, companies should act now: assess first-party data readiness, refactor KPI frameworks, and train teams to adapt to AI collaboration models. Successful transformation requires a balance of strategic vision and practice execution—both grasping the long-term potential of AI and accumulating empirical experience through controllable testing. In this era of variable consumer behavior and high economic uncertainty, data-driven agile advertising strategies are no longer a luxury, but a necessity for survival. We encourage readers to apply the practical insights of this article to their own business. If you need professional guidance, Topkee and other partners with Google AI advertising experience can provide all-round support from evaluation to execution to help you win the opportunity in this AI advertising revolution.