Google Ads has changed significantly, adjusting to the latest technologies and users’ evolving online behavior. Previously, advertisers had to manage campaigns closely. Now, there’s a bigger focus on using data and automation.
This has led to the introduction of the Hagakure method. This approach:
- Simplifies Google Ads management.
- Encourages using broad match options.
- Takes advantage of Google’s automation tools.
Here’s what Hagakure means for Google Ads advertisers and how it can help you manage your paid search campaigns.
The origins of Hagakure
“Hagakure” comes from an 18th-century Japanese book about samurai teachings written by Yamamoto Tsunetomo. It emphasized values like loyalty and purpose. The word means “hidden by the leaves,” suggesting deep wisdom in plain sight.
Connecting this to digital advertising, the Hagakure method in Google Ads is about finding a clear and simple way to advertise successfully, much like how samurais sought clarity in their actions.
This approach aims to simplify digital advertising, capturing the clear purpose that samurais valued.
Single keyword ad groups (SKAG) and single-themed ad groups (STAG) are foundational structures in search ads.
- SKAG focuses on crafting individualized ads for every keyword, offering precision and potentially improved Quality Scores.
- STAG, on the other hand, clusters related keywords under one theme, providing a balance between detail and ease of management.
In contrast, Hagakure emphasizes automation and scale, capitalizing on Google’s machine learning.
While SKAG and STAG prioritize manual management and/or more control, Hagakure harnesses automation and Smart Bidding technologies for on-the-fly optimizations.
Moreover, Hagakure promotes simplicity by reducing the number of entities like ad groups and campaigns, making it easier to handle as accounts expand.
Importantly, Hagakure isn’t exclusive; advertisers can blend it with SKAG or STAG techniques, enabling a tailored approach that leverages granularity and automation.
Audience attribution automation (AAA) and “modern search” were once related buzzwords for a simpler account structure. Just as Hagakure seeks to cut through the complexities:
- AAA was about precisely targeting audiences with automation.
- Modern search aimed to bring clarity and focus to search strategies by directly addressing user intent.
However, in recent times, the term Hagakure has taken center stage in Google’s communications, becoming more mainstream.
While modern search remains relevant, Hagakure has become the dominant narrative, emphasizing a clear and straightforward approach in the evolving digital advertising landscape.
Core principles of the Hagakure strategy
Hagakure confronts two key messages.
- Oversegmentation blocks machine learning.
- Data sets must be compact and concentrated for smart bidding to work at maximum.
The Hagakure method suggests a simpler account setup by merging similar themes and removing extra ad groups. This enables:
- Better campaign management.
- Quick adjustments to market shifts.
- Smarter resource use.
In short, organized simplicity is key.
Hagakure is necessary because accounts have become too messy. Marketers tried to control every little detail and ended up with too many campaigns, ad groups, and keywords.
Instead of keeping data together, data silos existed, which made it quite hard for automated bidding strategies and automation in general to work well.
Rather than narrow, hyper-specific targeting, Hagakure uses broad match types and automation, ensuring a wider yet relevant audience is reached.
Google’s Smart Bidding, powered by machine learning, adjusts bids based on real-time audience behaviors, directing resources towards high-conversion segments.
The strategy dynamically interacts with audiences, letting algorithms set the optimal bid for each auction based on various influencing factors. Merging audience insights with automation, Hagakure reduces manual tasks. Advertisers don’t constantly adjust bids; the system automates it for efficiency.
One of the most common misconceptions surrounding the Hagakure approach is the notion of broad match.
While Google often recommends using broad match, it’s essential to recognize that it’s not a mandatory component. The suitability of broad match varies depending on individual campaign objectives and nuances.
Hence, marketers should feel empowered to experiment with the Hagakure method with or without this match type. Tailoring your strategy to the unique situation is vital for its effectiveness.
Potential drawbacks and considerations when implementing Hagakure
Adopting the Hagakure method can be a big change for advertisers used to traditional setups. It’s about changing the campaign structure and adapting to a new mindset. Those used to detailed campaign management might find the initial shift challenging.
A common worry with the Hagakure method is feeling a loss of control. Combining ad groups and using more automation can make advertisers feel they’re less hands-on. While the method emphasizes simplicity, it’s crucial not to lose sight of campaign details.
Using broad match can extend reach, but it has risks. Without careful monitoring, ads might appear for unrelated searches, wasting money and potentially misrepresenting the brand.
Despite Hagakure’s emphasis on simplification, advertisers must regularly check and refine their broad match strategies using negative keywords.
While the Hagakure structure was initially designed to be built from existing campaign data – restructuring established accounts to optimize performance – my experience in 2025 has shown that launching directly with Hagakure can be remarkably effective, even without historical performance data.
When implemented from scratch, these modern search campaigns demonstrate an impressive capability to aggressively gather data in the crucial first 72 hours.
I now regularly recommend new accounts start directly with Hagakure, using maximum clicks or maximize conversions bidding strategies during the initial 1-3 day period. This approach creates a concentrated data collection phase that rapidly trains Google’s algorithms, establishing the foundation for efficient performance much faster than traditional campaign structures.
The key advantage is that advertisers can bypass the prolonged learning periods typically associated with new accounts, achieving data maturity and optimal performance in a fraction of the time previously required.
Power Pair – High Risk High Reward
At Google Marketing Live 2024, Google unveiled what they’re calling the “Power Pair” – a strategic approach to campaign architecture that combines modern search structure with extensive broad match usage and Performance Max campaigns.
The Power Pair strategy aims to maximize search query coverage through a complementary system: modern search campaigns capture high-intent keywords while broad match expands to relevant new queries, with Performance Max filling any remaining gaps.
Google designed this architecture to ensure advertisers achieve comprehensive coverage across the customer journey.
It’s worth acknowledging that broad match in 2025 has evolved significantly from its earlier iterations. The machine learning capabilities powering today’s broad match are vastly improved compared to what we worked with in 2020 or before.
That said, even with these improvements, implementing broad match at scale still carries inherent risks that advertisers should carefully consider.
In my assessment, the Power Pair represents a high-risk, high-reward approach. By adopting this strategy, advertisers are essentially opting for maximum reach and aggressive auction participation.
The fundamental premise is to rapidly gain campaign data, allowing smart bidding algorithms to optimize performance. However, this configuration tends to perform effectively for only a subset of advertisers.
The primary challenge lies in broad match’s inherent volatility. For this strategy to deliver positive returns, businesses need several foundational elements in place.
Sufficient inventory and category depth are essential prerequisites to benefit from increased reach; otherwise, the additional queries simply drain budget without delivering conversions. Additionally, a 2024 Optmyzr study shows that full-asset Performance Max campaigns often underperform compared to more focused feed-only Performance Max campaigns.
I typically recommend a modified version of the Power Pair. My approach begins by establishing a solid modern search campaign architecture without broad match, then systematically introducing broad match testing over a 3-4 week period, contingent on the campaign’s baseline performance stability.
I generally maintain Performance Max campaigns in feed-only mode, allowing the modern search campaigns to handle the search landscape more precisely. This prevents Performance Max from potentially interfering with search campaigns.
When a strong baseline is set, a full media mix Performance Max can be tested.
Hagakure vs. AI Max
The Hagakure approach has established itself as the gold standard for advanced advertisers seeking performance and control. Google introduced AI Max for Search campaigns, positioning it as the next evolution in search advertising – but does it truly deliver on this promise?
Google’s AI Max is being rolled out globally in beta, offering what they describe as “a suite of targeting and creative enhancements” powered by Google AI. Google claims impressive metrics: advertisers activating AI Max in Search campaigns “typically see 14% more conversions or conversion value at a similar CPA/ROAS,” with campaigns previously using exact and phrase keywords seeing even higher uplift at 27%.
However, these headline figures deserve scrutiny.
Dig deeper. AI Max: Google’s new AI ad tool, explained
The Modern Search/Hagakure approach has consistently delivered excellent performance through a thoughtfully constructed architecture that balances automation with strategic control. Its foundation lies in well-organized, tightly-themed ad groups with streamlined keyword sets and focused landing pages – creating a symbiotic relationship between human strategy and machine learning.
AI Max, by contrast, leans heavily into broad match and “keywordless technology” to expand reach into new query territories. While Google positions this as helping advertisers “show up on more relevant searches,” the reality is that this approach surrenders significant control to Google’s algorithms.
The “text customization” feature – rebranded from “automatically created assets” – further diminishes advertiser control over messaging.
What Modern Search gets right, and where AI Max falls short, is in maintaining the critical balance between automation and strategic oversight. Modern Search enables advertisers to:
- Create meaningful campaign segmentation that aligns with business objectives.
- Exercise granular budget control across different product lines or services.
- Implement sophisticated testing frameworks at the ad group level.
- Maintain brand voice consistency through carefully crafted messaging.
Google has attempted to address control concerns by adding features like “locations of interest” and “brand controls” at the campaign and ad group level, along with enhanced URL parameters for tracking. They’ve also promised reporting improvements including “headlines and URLs in the search terms report” and “improved asset reports.” However, these additions appear to be compensating for the fundamental control sacrifices inherent in the AI Max approach.
Iny my view, a modern search structure remains superior for advertisers who value strategic input alongside automation.
Rather than rushing to adopt AI Max, marketers should continue refining their Modern Search implementation, selectively testing AI Max features where appropriate, but maintaining the core Hagakure principles that have consistently delivered strong performance.
For now, Modern Search provides the optimal balance: leveraging Google’s machine learning capabilities while preserving the human strategic intelligence that no algorithm can fully replace in the near future.
Case study: Scaling search ads with Hagakure
There is no better way to showcase the potential of Hagakure than talking about a real case study. One of my biggest clients so far, a shoe retailer with over a thousand brands and almost a million SKUs live, is the perfect example.
After beginning our collaboration, shopping contributed to about 90% of the revenue, prompting us to devise a search strategy. Over three years, the search setup grew, and by early 2019, we were managing:
- 555 campaigns.
- 56,673 ad groups.
- 86,290 ads.
- 190,159 keywords.
Though this expansion increased search revenue, the account became too huge and difficult to manage. While popular, the prevalent structures, SKAG and STAG, weren’t scalable. This setup also introduced several challenges.
Even with the expansive setup, sales either remained stagnant or saw slow growth, especially when factoring in the vast inventory. Managing over 500 search campaigns became an overwhelming task.
Most revenue growth came from adding new search campaigns tied to new brands or product lines in the inventory. Additionally, the setup struggled to adjust to varying product availability and changing seasonal items.
Despite initial challenges, we tapped into a modern search approach, which catalyzed a remarkable 300% search growth using the existing setup.
By October, our year-over-year search campaign revenue jumped from €180,000 to over €480,000.
The impressive part? We achieved this with the same brands, products, keywords and ad assets.
- The key change was refining account structures to unleash the potential of Smart Bidding fully.
- We streamlined the process by consolidating several detailed campaigns into one concise campaign. What used to be individual campaigns were transformed into ad groups.
- Only high-converting brands were retained. The number of campaigns for fashion brands was drastically reduced from 450 down to just three.
- We grouped the top eight brands, which made up about 75% of search sales, into a single top-tier campaign. Each main keyword had its own dedicated ad group.
Additionally, to serve as a backup and discover new keyword opportunities, we established a Dynamic Search Ads (DSA) ad group for every brand.
However, it’s crucial to employ a negative keyword strategy to prevent the DSA from overlapping with or “stealing” traffic from your keyword-focused ad groups.
So instead of having 450 data silos working for themselves, we streamlined all important entities into three campaigns, supercharging the smart bidding capabilities and ultimately leading to a foundation for stable growth.
And this is what Hagakure is all about – maximizing results with minimum entities. It’s comparable to the Pareto principle, where 80% of the results are achieved with 20% of the resources.
Practical implementation tips
To check your account’s eligibility for this approach, I highly recommend running this script from Google to check the current account structure.
Although a single script might not be enough to take care of individual cases, it’s an excellent tool for high-level reporting on campaign, ad group and keyword data to see if there’s potential in restructuring the account.
Choose your keywords
If you struggle to pick the right keywords for your new setup, follow this approach:
- Pick your top campaigns you want to transform into a modern search.
- Run the search query report for the last 30/60/90 days and filter for search queries with >1 conversion.
- Run the keyword overview report for 30/60/90 days and filter for keywords with >1 conversion.
- Join those two reports and identify the terms that bring in conversions. Eliminate the rest.
- To protect you from seasonality, it is recommended to check the reports again for the last 3/6/9 and 12 months.
- Try to limit your keyword set to a maximum of 20 keywords per ad group.
Succeed with top responsive search ads
Use this framework if you see issues getting enough assets into the campaigns. Try to make use of keyword insertion and ad adjustments.
Use the native Google Ads tag for conversion tracking
Avoid imported conversions from Google Analytics 4 or other third-party tools when you can.
The native Google Ads tag is the quickest for data transfer to the ads account, while other methods can cause delays.
Adjust your attribution model
Set attribution to data-driven and keep the conversion window at 90 days. Ensure Enhanced Conversions are on.
Streamline audiences
Pick audiences that fit the purchase cycle and try to avoid duplicates and too many overlaps. You don’t want to clog your campaigns with hundreds of audiences.
Track your performance
Do proper reporting after implementation. Make sure to count in the conversion delay and learning period.
