Are You Prepared to Leverage AI for 80% ROAS Growth?
The 2025 Artificial Intelligence Trends Report reveals a market reality that cannot be ignored: AI is fundamentally reshaping every aspect of the customer experience (CX). In an era where consumer expectations change dynamically with every swipe, click, and voice command, traditional marketing methods are no longer able to cope. Lisa O'Malley, senior director of industry products at Google Cloud, pointed out that AI is no longer just a buzzword, but a necessary tool for companies to remain competitive. Especially in the field of Google Ads, we have witnessed the process from simple automation to a fundamental change in working mode. Car rental companies are facing the dilemma of a 30% order cancellation rate, Ito Yokado has achieved an amazing result of 80% ROAS, and IKEA Greece has created a 7-fold revenue growth through search behavior capture. These cases all prove that the combination of AI and Google Ads has become the core engine of marketing transformation. This article will deeply analyze the technical architecture and strategic thinking behind these successful cases, and provide companies with an actionable AI marketing implementation blueprint.
I. Trends and background of artificial intelligence reshaping customer experience
1.1 AI Trend Report 2025 reveals the potential for CX transformation
The most striking finding in Google Ads latest 2025 Artificial Intelligence Trend Report is the profound impact of AI on customer experience (CX). The report shows that leading companies no longer limit AI to back-end process automation, but use it as a core strategic tool to reshape the customer journey. In the car rental industry, Economy Car Rentals Group faces a 30% cancellation rate, which not only causes serious revenue losses, but also exposes the limitations of traditional statistical models in predicting customer behavior. The company, which spans more than 120 countries, found that the cancellation rate in some regions even exceeded the industry average, and the traditional historical data grouping method could not effectively predict the cancellation pattern of long-term bookings and niche markets. This dilemma precisely reflects the challenges faced by global companies: in today's exponentially growing data complexity, traditional analytical tools can no longer provide sufficient business insights.
1.2 Dynamic customer expectations drive the necessity of AI technology
The behavior patterns of contemporary consumers are unprecedentedly dynamic. The case of IKEA in Greece shows that the way consumers buy furniture has fundamentally changed. People prefer to search by room type (such as "living room furniture") rather than specific products, and the demand for the convenience of online shopping has increased dramatically. This behavioral change has caused the traditional keyword strategy to fail and the website traffic has continued to decline. At the same time, Japanese retail giant Ito-Yokado is facing the double blow of declining subscription rates for paper flyers and limited audience age groups. It is urgent to find new channels that can accurately reach digital native consumers. These market changes highlight a key fact: customer expectations have entered a state of continuous change, and companies must have the ability to adapt in real time, which is the core value of AI technology-through continuous learning and adjustment, keep pace with customer expectations.
II.Google Ads real-world examples
Case 1: Car rental group uses AI to predict cancellation rate and optimize advertising bidding strategy
Order cancellation rate is as high as 30%, resulting in revenue loss; traditional statistical models cannot accurately predict the booking value of long-term/niche markets; advertising budget is wasted on customers with high cancellation risk. Google Ads solution: Cooperate with Delve to build an AI model based on Google Cloud to analyze 20+ factors such as customer age, payment amount, rental days, etc., and predict cancellation probability in real time (accuracy rate is 99%). Through Google Ads value-based bidding (VBB), the bidding weight of orders with high cancellation risk is automatically reduced, and the budget is concentrated on "safe" customers. According to the prediction results, the target ROAS is lowered by 10%-15% to expand the coverage of high-value customers. The accuracy of short-term booking prediction is increased to 99%, and the overall booking prediction accuracy is increased by 20%; advertising expenditure is saved by 1 million euros per quarter, and profits increase by 10%; confidence in expanding new markets is enhanced, and advertising efficiency is significantly improved.
Case 2: IKEA Greece reversed traffic decline through Demand Gen and broad match keywords
Consumers turned to local stores, and online traffic was lost; traditional keyword strategies could not cover "non-brand searches" (such as "living room furniture" general demand). Google Ads solution: Enable broad match keywords (such as "living room storage furniture") to automatically capture related long-tail words (such as "TV cabinet" and "coffee table"); use Demand Gen to deliver visual video ads to showcase furniture scene-based usage and inspire purchase inspiration. Create ad groups by room category (living room, bathroom), and combine competitive keywords (such as "XX brand sofa") to expand coverage. Website traffic increased by 62% year-on-year, ROAS increased by 20%, and click costs decreased by 40%; online revenue increased by 7 times, successfully reaching browsing customers who had not originally considered IKEA.
III. Empirical evidence of cross-industry applications: P-MAX campaign effectiveness
3.1 B2B e-commerce MonotaRO’s ROAS increased by 48%
The case of MonotaRO, a Japanese B2B e-commerce giant, demonstrates the excellent adaptability of P-MAX campaigns in corporate procurement scenarios. Faced with the dilemma of low coverage and ROAS of traditional search and shopping campaigns, the company’s marketing team adopted an incremental innovation strategy: first, small categories with unsatisfactory ROAS were selected for P-MAX trial operation, and breakthrough results of 48% increase in ROAS and 44% increase in new corporate customers were achieved in just four weeks. In-depth analysis shows that P-MAX’s cross-channel integration capabilities are particularly suitable for the complex decision-making process of B2B procurement. When engineers search for product specifications on Google, watch tutorials on YouTube, and receive quotations in Gmail, the system can automatically identify the same purchasing intention behind these scattered touchpoints. MonotaRO further discovered that P-MAX's AI allocation mechanism is particularly effective for long-tail industrial categories. It can automatically allocate more budget to low-search volume but high-profit products such as "hydraulic cylinder seals", while traditional campaigns are difficult to optimize such categories due to insufficient data. Based on the success of the pilot, the company has migrated all categories to P-MAX and plans to invest the saved media expenses in supply chain optimization to form a virtuous cycle of marketing and operations.
3.2 Optimization of conversion value rules for SaaS service freee
The case of Japanese cloud accounting service freee reveals the multiplier effect of P-MAX and value-oriented strategies. The core challenge facing the company is how to shift from simply pursuing member registrations to acquiring high-quality users - real potential customers who will eventually purchase advanced functions. The freee marketing team designed a sophisticated "conversion value ladder" to give different conversion paths differentiated weights based on 28 days of user behavior data: the value coefficient of only registering for basic services is 1, while the value coefficient of opening the bank docking function is 3.2. The team also found that the lifetime value (LTV) of PC users is 40% higher than that of mobile users, so they set the value rules of device types in Google Ads. When these deep insights are combined with P-MAX's intelligent delivery, amazing synergy is produced: while the conversion value increased by 169%, the number of member registrations still maintained a 7.2% growth, proving that AI can not only improve efficiency, but also redefine what is "effective conversion". freee's innovative practice provides a replicable model for the SaaS industry—"translating" business goals into machine-understandable optimization parameters through value weight design.
3.3 Used car dealer Gulliver's potential customer quality double growth
The transformation process of Gulliver (IDOM Co., Ltd.), Japan's largest used car platform, shows how P-MAX solves the most difficult "quantity-quality balance problem" in marketing. The company originally used the number of form submissions as its core KPI. Although it obtained a large number of potential customers, the store transaction rate continued to be sluggish. By analyzing historical data through BigQuery, the team established a "transaction probability prediction model" and found that the customer transaction rate from specific advertising materials (panoramic interior photos) was 60% higher than the average. Gulliver converted these insights into P-MAX's value bidding rules, while integrating the creative resources originally scattered in display ads and discovery ads. Within one month of the strategy adjustment, not only did the number of potential customers continue to grow, the cost per action (CPA) decreased by 30%, but more importantly, the return on investment increased by 10%, achieving a rare "quantity and quality increase". Particularly inspiring in this case is the offline effect attribution method - by having the store clerk mark the actual transaction customers in the CRM system and then matching them with the ad click ID, the data breakpoint problem from online to offline (O2O) was solved. Gulliver's experience proves that when companies combine domain knowledge (what kind of customers are easy to close) with P-MAX's scale capabilities, they can break through the efficiency ceiling of traditional marketing.
IV. Topkee's Google Ads solution
Topkee's Google Ads solution provides one-stop online advertising services for enterprises. Regardless of the size of the customer, it can effectively improve potential customer development and sales conversion rates through professional digital marketing strategies. The solution covers a complete service chain from pre-evaluation to post-optimization. First, it uses advanced website scoring tools to conduct comprehensive diagnosis, which not only produces detailed SEO problem reports and improvement suggestions, but also conducts structured analysis of page content to ensure that all information meets search engine optimization specifications, thereby improving natural search rankings and exposure. On the technical level, Topkee's exclusive TTO tool realizes centralized management of multiple accounts. Customers can complete account opening applications, budget allocation and permission settings through a single platform, and realize cross-channel data tracking through tag ID concatenation. The system supports automatic setting of tracking events based on conversion goals, and synchronizes data to the advertising background, greatly reducing manual operation costs.
In terms of traffic tracking technology, the TM system developed by Topkee is more flexible than traditional UTM parameters. It can customize tracking rules based on 12 dimensions such as advertising source, media type, and activity name, and generate exclusive tracking links with TMID. This technology can accurately identify the quality of traffic from each channel and help customers adjust their delivery strategies in real time. In the advertising planning stage, the team will integrate industry trends and competitive intelligence to produce highly customized marketing theme proposals. The keyword research service uses AI tools to analyze the competitive vocabulary, combined with intelligent bidding strategies and broad matching technology, to dynamically expand keyword combinations with high conversion potential. Creative production uses an AI-assisted design process. The system first generates text and visual material prototypes, which are then optimized by professional designers to ensure that the advertising content has both visual appeal and message effectiveness.
Remarketing strategy is the core advantage of Topkee's solution. Through the user behavior data collected by the TTO system, the team can establish an accurate audience segmentation model. Analysis shows that personalized remarketing ads designed for specific interactive scenarios have a conversion efficiency that is more than 70% higher than conventional ads. The effectiveness evaluation stage provides a three-dimensional reporting system: real-time advertising data reports track exposure and click performance, conversion reports analyze the quality of potential customers, and ROI reports evaluate overall marketing effectiveness from the perspective of return on investment. Based on these data, professional analysts will make optimization suggestions in terms of budget allocation, bidding strategy, keyword combination, etc., forming a closed-loop system of continuous improvement. This data-driven service model enables customers to dynamically adjust Google Ads strategies to maximize advertising return on investment while controlling costs.
Conclusion:
From the 99% accurate cancellation prediction in the car rental industry to the 80% ROAS breakthrough in the retail industry, these cases together depict the transformative energy released by the combination of AI and Google Ads. This transformation is not only a technological upgrade, but also a fundamental reconstruction of marketing thinking—from guessing customer needs to accurate predictions, from unified message push to contextualized interaction, from pursuing single conversions to optimizing lifetime value. As AI models become more mature, we are entering the era of "super-personalized" marketing, where each customer touchpoint can be dynamically adjusted according to its immediate context and long-term value. However, technology is only an enabling tool, and true success belongs to those organizations that can deeply integrate data insights, creative inspiration and business strategies. If your company is preparing to embark on the AI marketing transformation journey, it is recommended to start with a small and precise pilot and gradually expand after accumulating experience. Professional Google Ads consultants can help you design AI solutions that fit your business and avoid common implementation pitfalls. In this era of rapidly changing customer expectations, only by embracing AI-driven smart marketing can you stay ahead of the competition.