Concept and types of attribution models in Google Ads

An attribution model is a method of distributing conversions between different traffic sources, advertising campaigns, or other elements. In each model, each user's interaction with an advertisement or website is assigned a certain value or the entire conversion.

For example, in the last interaction model, the value is assigned to the source from which the last click was made before the conversion. In the model of the first interaction, on the contrary, the value is assigned to the point that became the beginning of the chain of interactions.

Why do we need different attribution models?

Today, when shopping online, users go through a difficult path, interacting with your site through various traffic sources. They can repeatedly use contextual advertisements for different queries, pausing between visits. All these steps lengthen the process of making a purchase decision.

Attribution models play an important role in assessing the contribution of all user interaction points to the final conversion. These models, based on various algorithms, provide data on the significance of each user contact. They allow you to determine which touch points turned out to be the most significant and which ones were less important. This makes it possible to more objectively evaluate the effectiveness of various marketing tools and traffic sources.

Using attribution models, marketers can get a detailed view of the customer's path and, based on this, improve their promotion strategy. Understanding how different interactions affect a purchase decision helps you allocate your advertising budget more efficiently and optimize marketing campaigns to maximize returns.

What is the difference between attribution in Google AdWords and Google Analytics

Many people are familiar with the attribution models in Google Analytics, which show the contribution of various traffic sources to the conversion.

In Google Ads (formerly known as Google AdWords), attribution allows you to distribute the value of a conversion between points of interaction within an advertising system. This includes campaigns, keywords, ads, and devices.

For example, if a user first clicked on the "buy bed linen" request, looked at the products on the site, left and later returned through a branded advertisement, you will be able to track this path in reports. By choosing one of the attribution models, you can redistribute the value depending on the contribution of each advertising campaign. Unlike Google Analytics, attribution reports in Google Ads also take into account interactions across multiple devices.

This allows you to see which devices users most often start their journey from and on which devices they make conversions. This understanding helps to more accurately assess the effectiveness of various advertising elements and optimize strategies to achieve better results.

What attribution models do Google AdWords have?

In most cases, users visit the site several times before making a purchase. Therefore, it is important to analyze all aspects of the campaign, ads and ways of presenting information based on user activity data.

Currently, Google Ads (formerly Google AdWords) offers six attribution models available for use in the advertising cabinet:

By the first action:

Using the first-action attribution model, you get complete information about the customer's path to conversion, with all priority and value given to the user's first click, which subsequently led to the conversion. This model emphasizes the importance of the user's first contacts with your site or advertising, allowing you to understand which campaigns, ads, or keywords attract new users, and thus helps optimize the attraction strategy.

Thanks to this model, it is possible to determine which traffic channels or advertising campaigns are most successful in attracting the attention of new potential customers, which allows for more efficient budget allocation at the stages of attraction. You can analyze which traffic sources or marketing tools are most effective as initial points of interaction, which helps optimize initial touches to attract more potential customers.

This model also supports long-term strategies, helping to understand which marketing efforts have an impact on generating interest in your product or service in the long run. The first click symbolizes the beginning of the client's journey and plays a key role in the subsequent interaction. It demonstrates exactly what attracted the user's attention and prompted him to start the conversion path. The first contact often forms the first impression of a brand, and successful first touches can significantly affect the user's further interaction with your site.

The analysis of the first clicks allows you to determine which advertising channels are most effective for attracting new users, which is important for expanding the customer base. Understanding which ads or keywords are most effective at attracting users allows you to create more targeted and engaging content. Using the first-action attribution model helps you better understand which marketing efforts effectively attract new customers and generate their interest in your product or service, which ultimately contributes to more effective planning and implementation of marketing strategies.

By the last action:

Using the last action attribution model, the entire value of the conversion is attributed to the user's last interaction before the conversion. This model focuses on the final stages of the user's journey, showing which specific actions prompted the user to make a purchase or other targeted action.

The last action model has a number of advantages. Firstly, it is clear and simple, as it directly connects the final action with the conversion. Marketers can quickly identify which campaigns or ads directly led to the conversion. Secondly, this model focuses on the final touches, allowing you to assess their importance and impact. This helps to understand which elements of the marketing strategy are most effective at the final stage of the customer's journey. Thirdly, it helps to determine which specific actions, ads or keywords turned out to be decisive for the user's purchase decision, which is especially useful for optimizing advertising campaigns and increasing their effectiveness.

The practical application of this model includes optimizing advertising campaigns, improving content and creativity, improving the effectiveness of the final stages and creating more effective retargeting and remarketing campaigns. Knowing which ads or keywords lead to conversion allows marketers to focus on the most effective elements of the campaign, allocate the advertising budget more efficiently and increase the overall return on investment. Analyzing recent interactions before conversion helps you understand which messages, suggestions, or creatives have the most impact on users, which allows you to create more targeted and compelling content. Understanding which incentives and offers work at the final stage of the customer's journey allows you to fine-tune marketing campaigns to achieve maximum conversion. Information about recent interactions can be used to create more effective retargeting and remarketing campaigns aimed at users who have already expressed interest in the product or service.

Using the last-action attribution model helps marketers accurately assess which marketing efforts were most effective at the final stage of making a purchase decision. This knowledge allows you to optimize advertising campaigns, improve content strategy and increase the overall effectiveness of marketing initiatives.

Depending on the duration of the interaction:

The last action attribution model is a method for evaluating the effectiveness of marketing channels that attributes all the value of a conversion to the user's last interaction before performing a targeted action, such as a purchase or subscription. This model is based on the principle that the most significant is the last step in the buying journey, which is often the key moment in making a decision.

When using the last action model, each user interaction with advertisements, campaigns, or content is taken into account, but the weight or significance of each interaction decreases as it moves away in time from the moment of conversion. This allows marketers to identify which specific marketing efforts or channels played the most critical role in completing the transaction.

The advantages of the last-action model include ease of data interpretation and understanding the contribution of each channel in the final stages of the buying journey. It also helps optimize advertising costs by focusing on the most effective channels and ads that result in the highest number of conversions.

The practical application of the last-action attribution model includes the analysis of advertising campaigns, evaluating the effectiveness of keywords and ads, as well as improving the content strategy based on data on recent user interactions before purchase. This helps companies to better target their marketing efforts and increase the overall conversion rate.

Linked to a specific position:

The attribution model, where most of the value is attributed to the first and last user interactions, followed by an even distribution of the remainder between all other touch points, plays a key role in analyzing the effectiveness of marketing efforts.

With this approach, the first interaction is often seen as an initial impulse that attracts the user's attention and initiates their interest. Then, the last interaction before the conversion is perceived as the final act, which leads to the actual completion of the target action, such as buying a product or subscribing to a service.

The remaining touch points between the initial and final stages also play a role, but their contribution to the overall conversion value is evenly distributed and is considered less important compared to the first and last stages. This helps to fairly assess the impact of various marketing channels and ads on the user's decision to take a targeted action.

The practical application of such a model includes analyzing the effectiveness of advertising campaigns, optimizing traffic channels and improving the user experience at each stage of the customer cycle. Identifying the most significant and influential touch points allows companies to build marketing strategies more effectively and focus on achieving maximum results in conversions and return on investment.

Linear attribution:

The uniform attribution model is a method for evaluating the effectiveness of marketing channels, in which the entire value of the conversion is evenly distributed between all points of contact of the user with advertising sources. This approach is based on the assumption that every interaction in the purchase process has the same importance for achieving the target action, whether it is the purchase of an item, registration or subscription.

The main features of the uniform attribution model include simplicity and transparency in data interpretation. Since all interactions are equal, analysts can easily assess the impact of each advertising channel or ad on the total number of conversions. This is especially useful in situations where the buying path is not too complicated and users quickly move from the first interaction to the conversion without multiple stages and delays.

The practical application of the uniform attribution model includes optimizing advertising budgets, identifying the most effective advertising sources, as well as improving marketing strategies based on data on evenly distributed conversion values. This approach helps companies to build their marketing campaigns more precisely, increasing the overall effectiveness and return on investment in advertising.

Data-based attribution model:

The attribution model, which uses machine learning to analyze conversion data and distribute value between touch points, is a highly accurate method for evaluating the effectiveness of marketing campaigns. It differs in that it takes into account the unique characteristics of the business and user behavior, which allows you to more accurately determine the impact of each stage of interaction with a potential client on the conversion.

The main essence of the model is to use machine learning algorithms to analyze numerous data about user actions before conversion. This allows you to take into account not only the initial and recent contacts with an advertisement or website, but also the intermediate stages of the purchasing process. The model is able to automatically identify non-linear relationships between different touch points, which is especially important for complex marketing strategies.

The use of a machine learning-based attribution model helps companies effectively optimize advertising costs, identify the most influential channels for attracting customers and improve interaction with the target audience. This approach provides a deeper understanding of the contribution of each marketing channel to the overall result and contributes to more accurate forecasting and adaptation of marketing strategies in a dynamic online business environment.

Each of these models has its own characteristics and is used depending on the goals and objectives of the advertising campaign. Considering their specifics will help you choose the most appropriate attribution method for more accurate analysis and optimization of your marketing strategy.