How to Leverage AI in Google Ads for a 400% ROAS Increase?

With the rapid development of artificial intelligence technology, Google advertisements is experiencing a revolutionary change. According to the latest official announcement from Google, the newly designed Google Ads interface will be fully launched in 2025. This is not only a visual update, but also a milestone in the deep integration of AI. In Spain, 12% of medium and large companies have used AI to improve marketing effectiveness, and the winning cases of the Google Marketing Partner Award (GMPA) show that AI-driven advertising strategies can create an amazing 400% increase in ROAS. This article will explore in depth how AI reshapes the Google Ads ecosystem and use tourism.

I. Future development trends of Google Ads combined with AI

1.1 Google Ads 2025 new design and AI integration

Google has officially announced that it will fully launch a redesigned Google Ads interface in 2025. This change is not only a visual update, but also a manifestation of the deep integration of AI technology. The new design will adopt a desktop-first strategy, optimize the operation process, and allow advertisers to use AI tools more intuitively. It is worth noting that the old interface will be fully deactivated after August 30, 2024, which means that advertisers must adapt to the new system in a short period of time. According to Google's internal testing, the new interface has received significant praise in terms of user experience and operational efficiency, especially in terms of the discoverability of AI functions. Although the new design is temporarily unavailable on the mobile side, Google emphasizes that all core functions will be fully retained and AI-driven advertising optimization suggestions will continue to be strengthened.

1.2 Analysis of the core advantages of AI in advertising

The core advantages of AI technology for Google Ads service are mainly reflected in three aspects: precise positioning, dynamic optimization, and large-scale personalization. Taking the ad-machine technology developed by Making Science as an example, this natural language generation (NLG) system can instantly convert product data in the tourism industry into highly personalized advertising copy, which has increased the ROAS of the Iberostar hotel chain by four times. The advantage of AI is that it can handle multi-dimensional variables at the same time, including user search intent, real-time price fluctuations, inventory status and cognitive biases, and generate the most suitable advertising content in milliseconds. Compared with traditional manual operations, AI systems can manage hundreds of thousands of advertising variations at the same time and continuously optimize based on real-time feedback, which is a scale and efficiency that is difficult for human beings to achieve.

1.3 Desktop-first AI advertising operation interface evolution

The new design of Google Ads chooses desktop computers as the priority platform, reflecting the demand for complex operations of AI advertising management. Unlike simple monitoring and report viewing, AI-driven advertising strategies often require multi-tasking data visualization, model parameter adjustment and cross-channel comparison, which can be maximized on larger screens and more complete input devices. The new interface is expected to strengthen the integration of AI tools, such as directly providing smart bidding suggestions at the ad group level, or embedding generative AI-assisted creation functions in the material library. This evolution direction shows that future advertising operations will tend to be more strategic, and tedious execution work will be handled by AI.

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II. Empirical cases of artificial intelligence improving advertising effectiveness

2.1 Application examples of NLG technology by Making Science

The ad-machine technology developed by Making Science is a classic example of the application of natural language generation (NLG) in Google Ads. The system can automatically parse product feeds in the tourism industry and convert boring data into vivid and personalized advertising copy. Taking the Iberostar Hotel Group as an example, each hotel can only maintain 3 general advertisements under the traditional model, but after introducing AI, a single hotel can generate more than 100 dynamic variants, and each advertisement is adjusted in real time according to the user's search terms, device type and past behavior. This "one-to-one" personalized communication greatly improves the relevance of advertisements, and ultimately leads to a 400% growth in ROAS. This case proves that AI can not only improve efficiency, but also redefine the quality of interaction between advertisements and consumers.

2.2 Riu Hotels’ data-driven AI marketing transformation

The case of Riu Hotels & Resorts demonstrates the amazing potential of combining first-party data with AI. Faced with a 30% cancellation rate, Riu worked with Making Science to build a customer data platform (CDP) that integrates marketing data from booking engines, contact centers, and various channels. Through the Gauss AI system, they can accurately predict which users are likely to cancel their orders and adjust bidding strategies to focus on high-value customers. This transformation requires organizational changes. Riu reorganized the previously independent IT, data, and marketing teams to establish a data-centric collaboration model. The results are amazing: bookings increased by 99% and ROAS doubled, proving that AI is not only a technical tool, but also a catalyst for corporate transformation.

2.3 PortAventura’s breakthrough in seasonal demand forecasting

PortAventura World theme park uses AI to solve the most difficult seasonal problem in the tourism industry. Through the Vimana AI system of their partner T2ó, they analyzed historical data and market intelligence and successfully identified potential customers in non-traditional peak seasons. The AI model helped them design precise audience structures and implement omnichannel strategies through Google Marketing Platform. The results showed that during the newly opened Carnival period, the number of new customers grew by 30% and the total transaction volume soared by 68%. This case highlights the value of AI in demand forecasting and timing capture, which can help companies break through the limitations of traditional marketing cycles and create new growth curves.

III. Three key application scenarios of AI advertising technology

3.1 Personalized message real-time generation technology

NLG technology is reshaping the production model of advertising creativity. Traditional advertising creation is a linear and time-consuming process, while AI systems can generate highly relevant messages in real time based on user context. Making Science's ad-machine shows how to convert product data into multi-language and multi-variant advertising copy to meet different market needs. In the future, this technology will become more mature. Combined with the progress of generative AI, advertisers can achieve true "dynamic creative optimization", that is, each advertisement is tailored to the real-time context. This not only increases click-through rate, but also improves user experience and reduces fatigue caused by irrelevant advertisements.

3.2 Intelligent activation strategy of first-party data

With the withdrawal of third-party cookies, the value of first-party data is becoming increasingly prominent. The case of Riu Hotels proves that AI can transform scattered customer data into actionable insights. The key is to establish a unified data architecture and use AI models for predictive analysis. For example, by analyzing the common characteristics of users who cancel reservations, AI can identify high-risk customers in advance, giving the marketing team the opportunity to save orders through personalized communication. This data activation strategy not only improves marketing efficiency, but also strengthens customer relationships and builds long-term loyalty.

3.3 Market demand forecasting and timing capture model

The success of PortAventura shows the accuracy of AI in demand forecasting. Traditional seasonal adjustments rely on rules of thumb, while AI can analyze multi-dimensional data, including search trends, weather patterns, economic indicators, etc., to find the most ideal marketing opportunities. For retailers, this means being able to plan promotional activities in advance; for tourism operators, it means being able to optimize dynamic pricing strategies. In the future, as predictive models continue to evolve, companies will be able to more proactively grasp the pulse of the market rather than passively react.

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IV. Inspiration from AI advertising practices of industry benchmarks

4.1 Technical architecture that increased ROAS in the tourism industry by 400%

The case of Iberostar reveals the gold standard of AI advertising in the tourism industry. Its success is based on three technical pillars: real-time data integration, NLG engine, and dynamic bidding system. First, all room types, prices, and promotional information must be updated to the central system in real time; second, the NLG engine converts these structured data into natural language ads; and finally, the bidding system automatically adjusts according to the expected conversion value. This architecture is particularly suitable for highly variable tourism products, ensuring that ads always display the latest and most relevant information. Other industries can learn from this architecture and adjust the implementation focus according to their own product characteristics.

4.2 AI prediction system for reversing booking cancellation rate

Riu Hotels’ AI prediction system demonstrates how to turn business challenges into competitive advantages. The system uses supervised learning to analyze the characteristic patterns of historical cancellation orders and establish a risk scoring model. High-risk customers are directed to specific communication strategies, such as flexible cancellation policies or value-added offers. This not only reduces cancellation rates, but also improves customer satisfaction. This model can be applied to various appointment-based businesses, such as medical clinics and beauty services, to help companies manage capacity utilization more effectively.

4.3 Audience targeting method for 68% growth in off-peak season performance

The key to PortAventura’s breakthrough of seasonal restrictions lies in the “demand creation” thinking. Through AI analysis, they found that specific customer groups (such as families without school-age children) have a higher acceptance of off-peak travel. Based on this, they designed targeted package itineraries and communication messages to create demand that did not exist before. This approach is particularly valuable for highly seasonal industries, such as ski resorts and island hotels, and can effectively smooth revenue fluctuations throughout the year.

V. Topkee's Google Ads solution

Topkee's Google Ads solution is a complete digital marketing service system that focuses on helping companies achieve business growth through the Google advertising platform. The solution covers the entire process from early evaluation to later optimization, and is suitable for the needs of companies of different sizes. In the early stages of the service, Topkee will use professional website scoring tools to conduct a comprehensive diagnosis and make optimization suggestions for website structure, content SEO and technical aspects. These measures can effectively improve the visibility of the website in search results and lay the foundation for subsequent advertising. At the account management level, the TTO tool system developed by Topkee can achieve centralized control of multiple accounts, including core functions such as media budget allocation and advertising account permission settings, and complete accurate data tracking through automated tag ID association technology. Compared with traditional UTM parameters, its original TM tracking system can customize tracking rules based on 11 dimensions such as advertising source and media type, generate exclusive links with TMID, and make advertising effect monitoring more detailed.

In the advertising strategy formulation stage, Topkee will produce customized marketing theme solutions for customers based on industry databases and market analysis. At the same time, it will mine high-value phrases through in-depth keyword research, and combine intelligent bidding strategies and matching mode optimization to ensure that the advertisements reach the target audience. In terms of creative production, AI-assisted design processes are used, and professional teams produce graphic materials that meet Google advertising standards, taking into account both visual appeal and message delivery efficiency. For the user conversion link, the solution especially strengthens remarketing technology, establishes a multi-dimensional customer group stratification model through behavioral data analysis of the TTO system, and delivers differentiated advertising content based on user characteristics at different interaction stages. Data shows that this personalized remarketing based on attribution analysis can increase conversion efficiency by more than 70%.

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Conclusion

The combination of AI and Google Ads is reshaping the competitive landscape of digital marketing. From Iberostar's ROAS increase of 400% to PortAventura's non-peak season performance growth of 68%, empirical cases show that AI has entered the stage of commercial value creation from proof of concept. The key to success is to strategically integrate technology, data, and organizational capabilities, rather than importing tools in isolation. With the launch of the new version of Google advertisements in 2025, AI capabilities will be more deeply integrated into the workflow, providing advertisers with more powerful competitive weapons. We encourage companies to seize this wave of transformation. If you need professional consultants to help plan AI advertising strategies, please contact our expert team.