Why your Google Ads campaigns struggle? Brand Recommendation AI transforms results with 351% lift

In June 2024, Google Ads officially announced the launch of the revolutionary "Brand Recommendation" feature on the Google Ads platform. This innovative tool driven by Google AI is reshaping the rules of the game in the field of digital marketing. According to the latest statistics, advertisers who use AI optimization suggestions have increased their brand reach efficiency by an average of 28%, and early adopters such as Turkish Airlines have achieved a 60% increase in conversion rate. In this era of fragmented consumer attention, how to accurately capture the target audience has become the biggest challenge for marketers. This article will deeply analyze the technical architecture and practical application of the latest brand recommendation function of Google Ads, and combine advanced tools such as coverage planner to help you master a complete solution from data insights to effectiveness measurement.

I. Overview of data-driven decision-making and brand advertising effectiveness

1.1 The core position of data-driven decision-making in modern marketing

Today's marketing environment has entered the era of "microsecond competition", and data-driven decision-making is no longer a choice but a necessity for survival. Michael Bailey, senior director of Google Media Lab, pointed out that traditional marketing decisions have serious lags, and complete insights can often be obtained only after the end of the event. The Google Ads brand recommendation function compresses the decision-making cycle to minutes by analyzing historical account data, industry trends and audience behavior in real time. Take Turkish Airlines as an example. It uses AI to analyze 2 million past advertising exposure data, accurately predict audience preferences in different regions, and increase the efficiency of advertising creative adjustment by 70%. This ability to convert data into action suggestions is the most needed competitive advantage for modern marketers.

1.2 The market value of Google Ads brand recommendation function

The brand recommendation function is located on the Google Ads "Recommendation" page and uses deep learning technology to continuously scan account data. Unlike traditional rule-based recommendations, it can identify non-linear associations, such as discovering abnormal performance of specific advertising materials during connected TV time periods. According to Liuhan Foreign Trade's actual test, after the system recommended adjusting its CPM advertising frequency from 3 times to 5 times, the brand search volume increased by 351%. More importantly, this feature perfectly connects performance-based and brand-based advertising. When Koçtaş runs CPA and CPM ads at the same time, AI automatically identifies the overlap between the two audiences and recommends transferring 15% of the budget to the cross-funnel ad group, ultimately reducing conversion costs by 31%.

II. In-depth analysis of Google Ads brand recommendation function

2.1 AI technical foundation and operation mechanism of brand recommendation function

The core of the brand recommendation function uses Google's latest multimodal AI model, which can simultaneously analyze the characteristics of text, images and video materials. The technical white paper reveals that the system scans more than 50 creative features per hour, from basic elements such as brand logo location to advanced features such as emotional atmosphere in the video. When analyzing Hepsiemlak's real estate ads, AI found that the click-through rate of materials containing the "virtual tour" button was 22% higher, and immediately recommended full account promotion. Even more amazing is the self-learning ability. When Turkish Airlines tested 6-second ads in the European market, the system established a regional optimal length model within 48 hours, increasing the conversion value by 65%.

2.2 Specific application scenarios of five types of brand suggestions

The five types of suggestions generated by the system form a complete optimization closed loop. In terms of "advertising material resources", AI will detect creative fatigue. When the same set of ads for Xiaodao massage chairs is exposed more than 7 times, it will automatically recommend a new version for rotation. The "bid strategy" suggestion shows dynamic intelligence. In the SuperStep case, the system found a downward trend in CPM costs on weekends and recommended increasing the budget to grab low-priced traffic. The "audience expansion" suggestion is particularly eye-catching. By analyzing similar audience characteristics, it helps Koçtaş find a new customer group of "home renovation enthusiasts", which increases the conversion rate by 125%. The "delivery time" and "device combination" suggestions achieve refined regulation. Liuhan Yangxing concentrated the exposure of TV devices after 8-11 pm according to the suggestion, and the cost of touch was reduced by 2.2 times.

III. Advanced application skills of coverage planner

3.1 Key parameter setting for target audience coverage simulation

The power of coverage planner begins with precise parameter setting. Advanced users should master the three-level audience definition skills: primary demographics (such as women aged 25-44), secondary interest tags (such as fitness enthusiasts), and excluded audiences (such as recent buyers). In the case of Liuhan Foreign Trade, the system found through cross-analysis that the "health-conscious group" responded best to vitamin ads, and recommended increasing the weight of this group by 30%. Another parameter that is often overlooked is the "frequency cap". Actual tests show that when it is set to 3-7 times, it can balance memory and fatigue. After the Kojima massage chair was adjusted accordingly, the brand search volume increased by 351%.

3.2 Integration of budget curve and TRP indicators

Professional marketers use the budget curve simulator to perform marginal benefit analysis. When the total budget range is entered, the tool will draw a trade-off curve for reach and frequency. In practice, Koçtaş found that when the TRP exceeds 75 points, the cost increases by 40% for each additional point, so the best benefit point is set in the 72-78 range. Even smarter is the cross-platform integration. The system can calculate the total TRP of YouTube and TV ads, helping Turkish Airlines reach 92% of the target audience coverage in the European market. It is recommended to perform a "budget sensitivity test" once a month to fine-tune the allocation ratio of each channel.

 IV. Successful Case Studies

​​Case 1: Yuhan Corporation - Data-driven Omnichannel Brand Building

How to efficiently reach the target audience and enhance brand awareness while optimizing advertising costs in the midst of economic uncertainty. Use Google Ads "Coverage Planner" to simulate advertising combinations, accurately target the core population of 25-64 years old, and shift budgets from traditional TV ads to YouTube. Combine brand promotion (CPM bidding) and performance advertising (CPA bidding) to balance coverage and conversion efficiency. Increase brand exposure through non-skippable ads and interstitial ads, and then guide conversions through call-to-action ads. Use the "Unique User Coverage" metric to measure the actual number of people reached to avoid wasting budget through repeated exposure. Confirm through Brand Lift analysis that the advertisement increased audience attention by 351%. The target population coverage rate is as high as 64.3%, and the cost of reaching is reduced by 2.2 times. Covering 27.3 million target users, brand awareness has been significantly improved.

​​Case 2: Kojima Massage Chair - Digital Omnichannel Marketing for New Product Launches

How to efficiently launch new products through pure digital channels and verify the direct impact of advertising on sales. For the first time, 100% of digital channels were adopted, abandoning traditional TV advertising. Combining brand advertising (to improve awareness) and performance advertising (direct conversion) to cover the entire journey of users from awareness to purchase. Using "Rich Media Campaigns" to automatically combine 6-second non-skippable ads and skippable ads to reduce CPM. Using "Product Feed Extensions" to directly display products in video action ads to shorten the conversion path. Using "Share of Voice (SOV)" analysis to compare competitor exposure and seize market share. Measure the contribution of brand advertising to the final conversion through cross-channel attribution. The target age group covers 92 million people and the conversion rate increased by 7.1%.

V. Topkee's Google Ads solution

Topkee's Google Ads solution is a professional digital marketing service system built on the Google advertising platform, designed to help companies achieve accurate customer acquisition and performance growth. The solution covers the entire process from pre-preparation to post-optimization, and is particularly suitable for the digital transformation needs of companies of different sizes. In terms of technical architecture, Topkee has integrated a number of self-developed intelligent tools and data analysis systems, including the TTO account management platform, TM customer tracking technology, and AI-driven creative generation system to form a complete advertising ecosystem.

The core of the service begins with a comprehensive website evaluation phase, using professional scoring tools to perform SEO diagnosis on customer websites, conduct a 360-degree analysis from technical architecture, content quality to user experience, and provide specific optimization suggestions. This basic work can effectively improve the visibility of the website in search results and lay a foundation for traffic for subsequent advertising. At the account management level, the TTO tool realizes centralized control of multiple accounts, supports cross-platform budget allocation, permission management and data tracking functions, and its conversion event automatic synchronization technology can instantly transmit key behavioral data back to the advertising system, greatly improving the accuracy of delivery. Compared with traditional UTM parameters, the TM tracking system provides a more flexible URL tagging solution, which can customize tracking rules based on 12 dimensions such as advertising source, media type, and event theme, and establish a data foundation for subsequent effect attribution.

For the advertising planning stage, Topkee will customize theme proposals and keyword strategies for customers based on industry big data and competition analysis. Its keyword research not only covers core business vocabulary, but also establishes a three-dimensional keyword matrix through industry vocabulary expansion and intelligent matching technology. Data shows that the strategy of combining broad matching and intelligent bidding can increase advertising exposure by more than 40%. In terms of creative production, the team adopts AI-assisted design mode, where the algorithm first generates the draft and then optimizes it by the designer to ensure that the visual elements and copywriting are in line with Google advertising specifications and brand tone.

Conclusion

In today's rapidly evolving digital marketing, Google Ads brand recommendation functions and AI-driven tools have become indispensable competitive weapons. Successful cases from Liuhan Foreign Trade to Turkish Airlines have proved that data-driven decision-making can bring significant benefits. Whether it is to increase brand awareness, optimize advertising or measure omnichannel results, these innovative tools provide strong support for marketers. We encourage all marketers to actively embrace these technological changes. If you encounter any challenges in practical application, you can always contact Google's certified professional consultant team to obtain personalized implementation suggestions and technical support.

Appendix

• Brands that thrive in uncertainty: Branding that survives constant change
• Power Pair stratejisiyle bir adım öne çıkanlar: Hepsiemlak, SuperStep, Koçtaş ve Türk Hava Yolları
• Inside Google Marketing: How we’re using AI