The Hagakure Method on Google Ads: How to Make the Most of the Potential of Smart Bidding

The Hagakure method is a new way of structuring and optimizing campaigns on Google Ads in order to maximize results thanks to the action of machine learning algorithms . This system is based on the use of automated tools such as smart bidding strategies and dynamic search ads (DSA) . The term Hagakure originates from Japanese tradition. This is the Estonia WhatsApp Number List title of the work of the samurai Yamamoto Tsunetomo, written in the 18th century. There he explained to his apprentices the bushido or “warrior’s path”, the code of honor to which the samurai were subject.

By taking up this concept of path in the digital domain, Google offers a new way for the management of Google Ads. Applying Hagakure involves abandoning atomization and semantics and, in return, relying on structural simplification to take advantage of the full potential of machine learning. The Hagakure method, a change of strategy (and mentality) in Search The Hagakure method proposes to simplify the account structure in order to maximize the amount of data at the campaign level . This way, each campaign has two large ad groups capable of gathering the volume of data sufficient to feed the machine learning algorithm of Google Ads. More specifically, the minimum figure indicated by Google is 3,000 weekly impressions by Ad group .

To Reach This Volume and Successfully Exploit the Advantages Offered

by smart bidding, Google offers three steps: 1. Structure the account by taking the destination URLs instead of the keywords as a reference. 2. Favor certain types of matches, such as “broad match” or “modified broad match” which attract more volume as opposed to others which limit it (exact). 3. Use DSAs to capture searches that we hypothetically don’t cover with the rest of the ads . It is possible to consider a DSA ad group for each type of product or a single group for all products. The advantages of the Hagakure method on Google Ads The Hagakure system makes the accounts more efficient because: It facilitates reporting tasks and data interpretation by reducing account complexity.

It achieves better results without having to increase the budget, which allows to optimize the advertising investments and to obtain higher profitability of the accounts. It boosts the productivity of the account team by focusing its efforts on making strategic decisions based on the main KPIs of the company. The increase in data recording allows for faster learning of Google’s automated functions (RSA, DSA, audiences, etc.). How to apply the Hagakure method on Google Ads? The following phases should be followed: 1. Study in detail the current account structure It is essential to understand why the account was designed this way and to study the variables that explain it such as, for example, the profit margin of each product, the way in which the geographical location affects the trade or even the flagship products.

With Whom He Works, Among Others. Let’s Take an Example

Previously, the account structure of an appliance e-commerce would have been divided into a myriad of campaigns like “brand refrigerators”, “generic refrigerators”, “brand dishwashers”, “generic dishwashers”. “,” Generic TV “,” brand tv “… With Hagakure, the restructuring of the account would be based on reducing the number of campaigns to work with a broader research umbrella. If this e-commerce has defined different profit margins for household appliances, on the one hand, and for televisions, on the other hand, this will be the criterion to be used to reduce the number of campaigns and group them according to this division.

Then, in each of them, it would be necessary to go in the history of the data to detect the groups of advertisements answering the principle of 3,000 weekly impressions . These can be maintained independently, while those with lower volume should be grouped together. El esquema muestra un ejemplo de estructura ideal siguiendo el método Hagakure The diagram shows an example of an ideal structure according to the Hagakure method 2. Adapt the strategy of the new structure according to the commercial objectives Setting the goal of each smart bidding campaign will determine how the machine learning algorithm will learn and the decisions it will make.

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