Any company measures its growth and revenues in terms of sales. When a company launches a product it heavily relies on Marketing efforts which in turn get converted to sales. Marketing is a constant for any organization because it is the only way to attract new customers and sometimes retain existing ones. Earlier, the spend on Marketing used to be quite high (For example banners, radio ads, television ads, etc.). Now, the digital era has given companies a huge benefit by offering digital platforms as a source of Marketing their products and services.
Digital Marketing has now evolved from traditional advertisements to product promotions with blogs, influencers, etc. It has become essential to continuously monitor these Marketing efforts in a measurable and quantifiable way and make changes as required. Google Ads is one of the most popular digital advertising and business promotion channels in the online world. In this article, you will learn about Attribution models for Digital Marketing and different Attribution Model Google Ads.
Table of Contents
Understanding Basic Terminologies
First, let’s get you familiar with certain important terms, which will help you understand more advanced concepts as we discuss down the line.
1. Conversion
Image Source: Dominion
Conversion is said to occur when someone finally buys your service or product or performs the final committing action that you desire.
2. Touchpoints
Image Source: Magestore
Touchpoints are the total number of ad clicks, keyword searches, visits, and impressions; that happened before the actual conversion happened. They are a series of interactions or points of influence, between the customer and your product/service, that finally lead to the conversion. If you trace the full path to conversion, touchpoints are like stations in between.
What is Attribution Models
Image Source: AgencyAnalytics
Attribution Model is a methodology to decide which channels receive credit for a conversion, and how much of it. An Attribution Model helps you distribute the credit based on certain criteria, like time spent on the channel, placement of the channel (or keywords), and the role of the channel, in the conversion process. Attribution Models let you choose how much credit each ad interaction gets for your conversions.
Hevo Data, a No-code Data Pipeline helps to integrate data from100+ sources (including free data sources Google Ads, etc.) to a Data Warehouse/destination of your choice to visualize it in your desired BI tool. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also transforming it into an analysis-ready form without having to write a single line of code.
Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.
Get Started with Hevo for free
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-day Free Trial!
Understanding the Need for Attribution Models
Before someone buys your product or service, he/she will visit your ad a few times or visit your blog more than once, just to make sure they’re getting what they want. At times they may click a retargeting ad or visit your Facebook page, before finally paying or committing. So it is important to analyze what feature is having the most conversions or how likely a feature will help in conversion.
Whenever you thought of using Digital Marketing channels to promote your business or optimize your existing online Marketing spends, you must have thought about some questions like:
- How is online Marketing conducted and what are the revenue and payment (commission) models?
- For which keywords does my ad appear in the top three lines in Google search results, and which keywords give me the most traffic or sales?
- Which model fits my use case best, or which model is optimal for my current needs?
- How do I reach my customers earlier and make them buy my product/service quickly?
- Which ad worked the most in converting them as your customer?
- How much credit does a particular channel/ad deserve for their respective contributions?
- How do you distribute the commission payout to different ads/channels, which the customer visited before finally converting?
You must have pondered about the way to answer these questions and make up your mind faster. This is where Attribution Models come into the picture.
Understanding Different Types of Attribution Model Google Ads
Image Source: SEOSydney
The Attribution Model Google Ads gives you more control over how much credit is distributed between touchpoints that lead to your desired conversion. Also, they help you zero in on your target customers’ subset, quickly.
Example Scenario for Attribution Model Google Ads
Let’s discuss with an example so that things get clear. We will be using the same scenario given below to discuss the various Attribution Model Google Ads.
You own a Hotel in New York and have subscribed for Google Ads. A customer books a room for 2 nights by clicking your ad after performing searches with the keywords “hotels in new york”, “budget hotels in new york”, “business hotel in new york”, “hotels near manhattan, new york “, “hotels with in house spa, new york”. Now, let’s see how the different Attribution Model Google Ads will distribute credit in the same scenario using some popular Attribution models with respect to Google Ads
Here are some of the Attribution Model Google Ads supports:
1. Last Click
As the name suggests, the last ad clicked and its keyword is the one that finally leads to the conversion, the ad, and its corresponding keyword gets all the credit. Any contribution by other clicks and keywords prior to the last one, during the customers’ journey, is totally ignored in this Attribution Model Google Ads.
This model gives 100% credit to the last keyword, “hotels with an in-house spa, new york”.
2. First click
In this Attribution Model Google Ads, all the credit for the conversion is given to the first ad clicked and its corresponding keyword.
This model gives 100% credit to the first keyword, “hotels in new york”.
3. Linear
This one equally distributes credit to all the ads that finally lead to the conversion. Each ad in the conversion path gets the same credit, making this the most democratic Attribution strategy.
This model distributes credit evenly amongst the 5 keywords, each keyword gets 20% credit.
4. Time Decay
There can be times when a user searches for a few keywords and clicks on a few ads but does not convert at that moment in time. After a few days or hours, the customer returns, maybe searches for a few more keywords/ads, and finally gets converted.
The Time Decay Attribution Model Google Ads gives more credit to those ads that occurred closer to the actual conversion time, and lesser credit to the ad interactions that happened a few days earlier. Typically, if an ad interaction took place more than a week before the actual conversion gets half as much credit as the interaction that happened later, say 1 day prior to the conversion. This method is called a “7-day half-life” method. Another prevalent method is “1-day half-life”, where the half-life is just 1 day.
Hence, the ads that got clicked on the day of conversion get 50% of the credit, and the touchpoints that occurred 2 days before the conversion get only 25% of the credit.
In this model, the keyword nearest to the conversion, “hotels with an in-house spa, new york”, gets the biggest credit. The keyword farthest from the conversion, the first keyword “hotels in new york”, gets the least credit.
5. Position-Based
Now, this Attribution Model Google Ads works based on the position of a touchpoint in the customer’s conversion journey. The very first touchpoint is considered very important as it initiates the customer to a path that finally leads to buying the product. The last touchpoint is considered as the final trigger that leads to the conversion and gets more importance.
So, the first and the last touchpoint get 40% credit each, totalling 80%. The remaining 20% credit is distributed evenly amongst all intermediary touchpoints.
In this model, The first and the last keywords share 80% of the credit distributed evenly (40% to each of the two), and the 3 keywords in between get 20% of the credit distributed evenly (6.66% to each of the three).
6. Data-Driven
This is the most complex one, as it takes into account the past conversion data for the product/service in question.
Based on past data for similar room bookings for the same hotel+room, each keyword receives a part of the credit. The keywords that are used prior to conversion can be classified as Display (seen on a display ad), Generic (most common, between you and your competitors), and Brand (lead to your specific brand or specific attributes of the brand purchased).
This algorithm accepts that generic keywords are important at the start of the search journey, whereas brand keywords (specific attributes of the brand purchased) play a key role near the conversion.
This way the last 2 keywords, “hotels near manhattan, new york ” and “hotels with in house spa, new york” would get 60% of the credit distributed evenly among them, i.e. 30% each; The first keyword, “hotels in new york”, is considered to display and gets 10% of the credit, and the middle 2 keywords split the remaining 30% (15% each to “budget hotels in new york” and “business hotel in new york”) amongst them.
Conclusion
Knowledge of Attribution Model Google Ads will help you in choosing the best model for your needs. You now understand how credit will be distributed when customers interact with multiple keywords/ads from the same advertiser. Hope this will result in you setting up an optimal Attribution Model Google Ads for conversion tracking and bidding. Also, you will now be able to change to appropriate models based on your short-term and long-term goals.
Integrating and analyzing Marketing data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo is a No-code Data Pipeline and has awesome 100+ pre-built integrations (including free data sources such as Google Ads, etc.) that you can choose from. Hevo can help you integrate data from numerous sources and load them into a destination to analyze real-time Marketing data with a BI tool. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure.
Visit our Website to Explore Hevo
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.
Share your experience of learning about Attribution Model Google Ads in the comments section below! We would love to hear your thoughts!
Talha is a seasoned Software Developer, currently driving advancements in data integration at Hevo Data, where he have been instrumental in shaping a cutting-edge data integration platform for the past four years. With a significant tenure at Flipkart prior to their current role, he brought innovative solutions to the space of data connectivity and software development.