Why your Google Ads ROI is low? AI-driven demand generation holds the key

Against the backdrop of tight global marketing budgets in 2025, the latest research by Google and WARC reveals startling data: marketing strategies that focus only on short-term benefits will lose up to 50% of potential long-term returns. This is like piecing together an unfinished puzzle, and you will never be able to witness the full value of marketing investment. When European brand data shows that the media investment return rate in the first four months is equivalent to the sum of the next 20 months, we have to face up to how AI-driven Google Ads is completely rewriting the rules of demand generation. The traditional marketing funnel is dead, replaced by the 4S consumer behavior model of seamless switching of "browse-scroll-search-shopping". This article will reveal the three core architectures of the new era of AI marketing through the offline and online integration of insurance giant Santalucía, the P-MAX miracle of the fireworks festival in Kameoka City, Japan, and the Demand Gen transformation case of eyewear brand Fielmann.

I. AI Technology Architecture for Demand Generation

1. Analysis of the Core AI Functions of Google Ads

Google Ads has evolved into an intelligent system with predictive neural networks. The core of its AI is the ability to simultaneously process more than 2 million signal parameters. Taking the smart bidding system in the insurance industry as an example, the algorithm not only considers traditional indicators such as click-through rate, but also integrates offline data such as call center call duration and quotation conversion rate to establish a dynamic value assessment model. When the system detects that the conversion rate of a certain type of user who watches a product video on YouTube and calls customer service within 48 hours increases by 37%, it will automatically adjust the cross-channel budget allocation. This deep learning capability enables Google Ads to predict the user's demand stage, from Gmail ads in the cognitive stage to search ads in the decision-making stage, forming a closed-loop optimization. Even more amazing is that the system will adjust its learning mode according to the characteristics of different industries. For example, short-term activities such as fireworks festivals use explosive learning, while the insurance industry uses continuous incremental learning.

2. Cross-Channel Data Integration Technology

The transformation case of Santalucía Insurance in Spain perfectly demonstrates how to solve the problem of data silos. By developing a dedicated digital CRM API, offline interaction data from the call center (including call transcriptions and quote acceptance rates) is instantly sent back to the Google Ads system. When a user searches for "car insurance" and calls customer service, AI automatically marks the call result as a "high-value potential customer" and pushes corresponding enhanced messages to the user when browsing Gmail within the next 72 hours. The key to this cross-channel integration technology is to establish a unified data layer to standardize event tracking across different channels such as search, Gmail, and display ads. The P-MAX application of the Kameoka City Government in Japan goes a step further and sets up three conversion point tracking on the official website of the fireworks festival, from browsing the information page to picking up tickets at convenience stores, forming a complete offline and online behavior map.

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II. Actual Combat Case: AI Demand Generation Transformation in the Insurance Industry

1. Santalucía Insurance's Offline and Online Data Integration

Santalucía Insurance, which has a century-old history, faces a key challenge in digital transformation: how to integrate the offline call center process, which accounts for 65% of its business volume, into the digital marketing system. The "three-stage value engine" it developed first uses natural language processing (NLP) to analyze customer service calls and automatically mark "high-intention signals" such as key phrases such as "I need insurance immediately" and "Compare three quotes". These data are connected to Google Ads through real-time APIs. When the system detects that 42% of potential customers brought by a certain advertising campaign generate quote requests, it will automatically increase the bid limit of the campaign by 27%. Even more sophisticated is that offline behavior feeds back to online strategies. They found that the average value of the policy of users who entered through specific long-tail keywords (such as "hurricane damage insurance coverage") is 64% higher than that of general searches, and the entire keyword combination is reconstructed accordingly.

2. Construction of a Three-Stage Value Assessment Model

The core of Santalucía's AI transformation lies in an innovative value assessment framework. The first stage "potential customer scoring system" uses machine learning to analyze historical conversion paths and finds that users who complete online health questionnaires have a conversion rate 3.2 times that of those who only browse the page, and a dynamic scorecard is established based on this. The second phase of "offline data return" technology solves the pain points of the industry. By cooperating with telecom operators to obtain the MD5 encryption value of the call center's caller number, 99.7% advertising exposure matching accuracy is achieved. The third phase of "product value matrix" is the most revolutionary. AI will dynamically adjust budget allocation according to the marginal benefits of different insurance categories (such as the cross-selling potential of pet insurance is 1.8 times that of auto insurance). In the first year of implementation, amazing results were achieved: the proportion of high-value policies increased by 58%, while the cost of acquiring customers decreased by 31%, proving that the AI model can accurately identify "hidden high-value customers" - those high-quality customers who have never clicked on ads but called directly through brand search.

III. Topkee's Google Ads Solution

Topkee provides a one-stop online advertising service based on Google Ads, aiming to help companies effectively increase the number of potential customers and sales performance. Regardless of the size of the company, we can provide tailor-made solutions to ensure the best results of advertising campaigns through systematic service processes and professional technical tools. In the initial stage of service, we will conduct a comprehensive website evaluation and analysis, use the latest scoring tools to deeply review the current status of the website, produce a detailed SEO problem report and provide specific improvement suggestions. This stage includes page content detection and structure optimization to ensure that the content meets the search engine specifications and has market value, thereby improving search rankings and exposure.

In the strategic planning stage, we will collect information from multiple dimensions and produce theme proposals based on the characteristics of the enterprise and market dynamics. Through a professional keyword research process, we not only analyze the dynamics of competitors, but also use advanced tools to expand the core keyword library, combine smart bidding and broad matching strategies to ensure that advertisements accurately reach the target audience. In terms of creative production, the team integrates AI technology and design expertise to produce high-quality graphic materials based on product characteristics and trends. From copywriting to visual design, strict control is carried out to ensure that the content is both attractive and convertible. The remarketing strategy tracks user behavior data through the TTO system, conducts customer segmentation and attribution analysis, and designs personalized content for different interactive features. Data shows that this strategy can increase the purchase intention of click users by more than 70%.

During the performance monitoring phase, we regularly provide three types of key reports: advertising execution status report, conversion performance report and ROI analysis report. The report covers core indicators such as budget utilization, click-through rate changes, conversion costs, and provides specific optimization suggestions. The team will also formulate follow-up plans from bidding strategies, keyword adjustments, etc., and help customers maximize the benefits of advertising investment through data-driven continuous optimization. Overall, Topkee's Google Ads solution covers the entire chain from technical infrastructure to strategy execution. Through the combination of professional tools and methodologies, it creates an efficient and measurable digital marketing system for enterprises to achieve business growth goals.

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Conclusion:

From the offline and online integration of the Spanish insurance giant, to the P-MAX innovation of local government activities in Japan, to the Demand Gen transformation of the German eyewear brand, the marketing paradigm of 2025 has clearly emerged: AI-driven demand generation is no longer a future option, but a current must-have. These cases together prove that when enterprises can deeply integrate Google Ads' intelligent system with their own data assets, they can increase their return on investment by an average of 64% and reduce customer acquisition costs by 31%. More importantly, we need to learn to think in a "full value cycle" way. As research shows, brands that ignore long-term benefits are losing 50% of their potential revenue without realizing it. The strategic choices marketers make at this moment will determine their voice in the market over the next three years. When you are ready to start your AI transformation journey, it is recommended to start by breaking down a key data island, such as integrating website analytics and CRM basic data. Remember, in this revolution, the biggest risk is not moving too fast, but standing still. If you need to further evaluate how Google Ads' AI solutions can be applied to your specific industry, please contact our digital marketing consultant team for personalized transformation roadmap recommendations.