Are You Looking to Maximize Advertising Efficiency with Google Ads?

At the 2025 Cannes International Creativity Festival, Joy Robbins, the global chief advertising officer of The New York Times, revealed a key trend: how can a media giant with more than 10 million digital subscribers create a win-win strategy that maintains user trust and improves advertising effectiveness through the combination of first-party data and Google Ads. This is not only a change in the media industry, but also a new track for global companies in the field of digital marketing. According to the latest research by Google and Accenture, 85% of companies in the Asia-Pacific region have entered the planning and implementation stage of AI marketing, and Google Ads has become the core platform for data-driven marketing. From Lifenet Insurance's 35% conversion rate increase to Delivery Hero's 52% return on advertising expenditure, behind these success stories is a wonderful story of how Google Advertising redefines advertising effectiveness through the collaboration of AI and first-party data.

I. The core value of Google Ads and the efficiency of corporate advertising delivery

1.1 Data-driven Precision Delivery Mechanism

The core competitive advantage of Google Advertising lies in its precision delivery capability based on huge data. Traditional advertising is like shooting arrows in the dark, while Google Ads transforms advertising into "measurable conversations" through search intent analysis, user behavior tracking, and cross-device recognition technology. Take Japan's Lifenet Insurance as an example. The company has established a complete conversion value tracking system through the integration of Google Analytics 4 and . It can not only see clicks and conversions, but also accurately calculate the expected profit of each insurance contract. This data transparency enables the marketing department to establish a common performance language with departments such as finance and sales, and transform the originally vague marketing expenditure into a clear return on investment calculation. When companies can directly link "ad clicks" with "customer lifetime value", marketing will transform from a cost center to a profit engine.

1.2 AI Automation Technology Reduces Operating Costs

The smart bidding function in Google Ads represents how AI reshapes the daily operations of advertising. The traditional manual bidding method is time-consuming and difficult to cope with market fluctuations, while' machine learning algorithm can analyze hundreds of signals in real time, including device type, geographic location, time period, and even the weather of the day, and automatically optimize each bid. NTT Docomo's case shows that when they combined the "product maximization campaign" with the intent matching of search ads, the customer acquisition cost was reduced by 25%, while the acquisition volume increased by 90%. This "both" result is the power of AI - it is not limited by the linear thinking of human decision-making and can find the optimal balance between multiple goals. For marketing teams, this means handing over repetitive decisions to AI and focusing on strategic thinking and creative output.

1.3 Cross-platform Integration Expands Audience Coverage

Modern consumers' attention is scattered across different platforms and devices, and Google Advertising' cross-platform integration capabilities have become the key to solving this challenge. From search ads, to app promotion,  provides a unified interface to manage omnichannel marketing. The New York Times not only places ads in traditional news content, but also integrates ads into Wirecutter product reviews, Games game section and even Wordle puzzle games, ensuring that ads appear in the most relevant contexts through Google Ads' audience targeting technology. This integration not only expands coverage, but more importantly, improves the contextual relevance of advertising - when the advertisement is highly consistent with the user's current interests, the click-through rate and conversion rate will naturally increase, forming a positive cycle.

II. Empirical Case Analysis: How Global Companies Use Google Ads to Improve Efficiency

2.1 Lifenet Insurance's ROAS Strategy Transformation

The case of Lifenet Insurance shows how Google Ads can help companies redefine marketing value. In the context of the implementation of International Financial Reporting Standards (IFRS), this online life insurance company faces the challenge of how to evaluate the long-term return on marketing investment. By establishing an "executive summit", Lifenet broke down the long-term profit structure of insurance contracts into quantifiable indicators and implemented a "value-based bidding strategy" in Google Advertising. They analyzed historical data, calculated the average term and expected profit of each contract, and converted them into "conversion value" that Google Ads can understand. The results are amazing: the conversion rate increased by 35%, the number of contracts increased by 11%, and the proportion of strategic customers aged 20-30 increased significantly. This proves that when companies can translate business goals into data language, Google Ads' AI algorithm can become a super assistant to achieve these goals.

2.2 Retail Media Data Integration of Japan's 7-11

The transformation story of Japan's 7-11 reveals how traditional retail can achieve transformation through the combination of Google Ads and first-party data. This 50-year-old convenience store giant carried out a major organizational reorganization in 2022 and established a dedicated marketing headquarters to integrate the originally scattered product development and store promotion into a data-driven closed-loop system. By linking user behavior data collected by the "Seven-Eleven App" with the store POS system, 7-11 can not only conduct precise personalized marketing, but also transform the app into a "retail media platform" so that consumer brands can advertise in the 7-11 ecosystem through Google Ads. The core of this transformation is to recognize that data is not a by-product, but a core asset-when retail data is combined with the delivery capabilities of Google Advertising, convenience stores are upgraded from sales endpoints to media platforms, creating new sources of revenue.

2.3 NTT Docomo's LTV Maximization Practice

The challenges faced by NTT Docomo are common in many large companies: each business unit operates independently and pursues different KPIs, resulting in scattered marketing resources and inefficiency. The company's breakthrough was to introduce "lifetime value (LTV)" as a unified indicator for the entire company and achieve this goal through Google Ads. They found that traditional advertising was overly focused on brand words and remarketing, making it difficult to expand the potential customer base. By introducing "product maximization campaigns" and intent-matched search ads, NTT Docomo allowed Google AI to freely explore the acquisition path of high LTV customers. The results exceeded expectations: while the cost of customer acquisition was reduced by 25%, the acquisition volume increased by 90%, and the investment amount increased by 50% year-on-year. This case proves that when companies are willing to give AI enough decision-making space, it can find customer groups and channel combinations that are difficult for humans to find.

III. Key Factors for the Success of Enterprises

3.1 Establish a Cross-departmental Dialogue Platform (such as Lifenet Executive Summit)

Lifenet Insurance's "Executive Summit" model reveals a key insight: the successful application of Google Ads is not a technical problem, but an organizational problem. When departments such as marketing, finance, and sales use different performance languages, data-driven marketing is difficult to implement. Lifenet uses regular high-level dialogues to transform the abstract "brand value" into quantitative indicators that are agreed upon by all departments: the average term of insurance contracts, the claims risk of different customer groups, and the balance point between customer acquisition costs and lifetime value. This dialogue not only aligns goals, but more importantly, it builds cross-departmental data literacy - when the CFO can interpret the marketing funnel and the marketing chief can calculate the return on investment, Google Advertising is upgraded from a tactical tool to a strategic asset. Although small and medium-sized enterprises do not need formal summits, regular cross-departmental workshops can also break down silos and create a common language.

3.2 Cultivate Internal AI Application Promoters (Mastermind Role)

The roles of Noriyuki Okajima and Katsuki Sugiura, the marketing directors of 7-11 in Japan, are worth further study. In promoting AI marketing in traditional retail organizations, they face a dual challenge: educating senior executives to understand new concepts such as "retail media" and coordinating internal resistance to change. Their success lies in their dual identities as "missionaries" and "doers": on the one hand, they constantly explain the vision of data-driven marketing to all levels, and on the other hand, they accumulate credibility through quick and small wins. Google and Accenture found that this "mastermind" role is a common element of AI marketing success - it does not have to be a senior executive, but it must have a clear vision and political wisdom. The revelation to companies is that before introducing Google Ads, it is more important to identify or cultivate such internal change makers and give them sufficient authorization and resources than simply purchasing technology.

3.3 Continuous Experimentation and PDCA Cycle Execution

NTT Docomo's case shows that AI marketing is not a magic that "set it and forget it", but a process that requires continuous optimization. The company adopts a "rapid experimentation" strategy: first test new methods on a small scale, such as changing matching types or bidding strategies, and quickly analyze the results before deciding whether to expand. This agile approach reduces the risk of change while accelerating the learning curve. The key is to establish an experimental culture: accept "failed" tests as necessary learning costs, design a rigorous A/B testing framework, and ensure that data visualization is understood by the entire team. The experimental tools provided by Google Ads and the analytical capabilities of GA4 are the technical foundations that support this PDCA cycle. When companies can turn "test-learn-optimize" into muscle memory, the benefits of AI marketing can continue to grow.

IV. Topkee's Google Ads Solution

Topkee provides one-stop online advertising services based on Google Advertising, focusing on helping companies increase potential customers and increase sales. Our service architecture fully covers the entire process from pre-evaluation to post-optimization, and can provide customized solutions regardless of the size of the customer. In the early stage of the service, we will conduct a comprehensive website evaluation, use the latest scoring tools to diagnose SEO problems, and provide specific improvement suggestions. This includes website structure optimization, content SEO detection, etc., to ensure that the website content not only meets the search engine specifications, but also provides users with valuable information, thereby improving search rankings and conversion rates.

On the technical level, Topkee has developed the TTO tool system, which is an integrated marketing management platform. Through TTO, multiple advertising accounts can be centrally managed to automate administrative operations such as account opening applications, budget allocation, and account authorization. The system supports multi-tag ID association, can accurately track data from various channels, and set tracking events according to conversion goals with one click. All data are automatically synchronized to the advertising background, greatly improving work efficiency. Compared with traditional UTM parameters, our innovative TM tracking technology is more flexible. It can customize tracking rules based on multiple dimensions such as advertising source, media type, and event name, and generate exclusive TMID links, so that customers can clearly grasp the advertising effectiveness of various channels.

In the strategic planning stage, Topkee will collect data from multiple dimensions such as market trends, competitors, and product positioning based on the characteristics of the customer's business, and produce professional marketing theme proposals. Our keyword research service not only covers core business vocabulary, but also expands long-tail keywords through intelligent tools, combining broad matching and intelligent bidding strategies to accurately target potential customers. In terms of creative production, the team uses AI technology to generate preliminary copywriting and visual concepts, which are then further implemented by professional designers to ensure that the advertising materials are both creative and convertible.

Conclusion:

From the trust economy of The New York Times to the value prediction model of Delivery Hero, these leading enterprise cases reveal a common truth: in the era of AI and first-party data, Google Ads has evolved from a simple advertising platform to a core component of the enterprise growth system. Success no longer belongs to brands with the largest budgets, but to those organizations that can turn data into decisions and AI into partners. Whether you are just beginning to explore Google Ads or looking to further optimize your existing strategies, remember: change begins with the first step - maybe setting the first conversion value, running the first experiment, or holding the first cross-departmental data dialogue. When you are uncertain, seeking the help of professional consultants can accelerate your learning curve. In this new era of AI marketing, opportunities belong to those companies that dare to reimagine possibilities. Now is the best time to start.

Appendix:

1. Google Ads Official Learning Center
2. GA4 and Google Ads Integration Guide
3. Think with Google Asia Pacific AI Marketing Trend Report