Last Updated: August 20, 2022

What is Attribution Modelling in Google Analytics?

Google Analytics Attribution modelling is the process of understanding and assigning conversion credits to marketing touchpoints on a conversion path.

These marketing touchpoints are primarily digital marketing channels (like paid search, display advertising, Facebook, etc.) but can also include offline touchpoints (like store visits, phone calls, etc.)

A conversion credit is the amount of credit you/algorithm gives to a marketing channel for completing a conversion.

Learn more: What is Attribution Modelling in Google Analytics?

What are the objectives of Attribution Modelling?

Following are the primary objectives of attribution modelling:

1. Fix attribution issues.
2. Understand the buying behaviour of website users.
3. Determine the most effective marketing channels for investment at a particular point in time.

What is an attribution issue and when it occurs?

An attribution issue occurs in your online marketing when you can not determine the primary source of conversion. It occurs when you can not determine the path followed by your customers to make a purchase on your website.

To learn more about attribution issues, check out this article: What is Attribution Problem in Online Marketing

The adverse impact of Attribution Modelling in an organization

Attribution modelling can affect employees’ salaries and bonuses.

Whenever you are talking about attribution, you are basically talking about who and what gets the conversion credit. You are basically deciding employees’ future bonuses, perks, and salaries.

Attribution modelling can also result in the firing of employees. In the worst-case scenario, the closure of an entire department.

Attribution modelling can create resistance within your organization

Every time you present a report, you are holding someone in your team or company responsible for the results (whether good or bad).

And people will do everything in their power to protect themselves, their perceived abilities and their job. Not many people will welcome the idea of being scrutinised in the name of attribution modelling.

Expect resistance, especially from those people or departments whose work may have been devalued in the past because of your attribution modelling efforts. These people may be living in constant fear of being made redundant.

Talking about attribution modelling results could be just like openly discussing management salaries.

If you go around talking about attribution with everybody in your organisation, from managers, colleagues, supervisors, and the cleaners, you may be surprised at how quickly you make enemies.

People will not appreciate your efforts to increase ROI across all marketing channels. They will more than likely hate you for deciding their future in the company.

Because of this, it is wise not to discuss the results of your attribution modelling efforts with everybody in your organisation. Involve only the right people, preferably only top management executives.

How does attribution modelling help in understanding the buying behaviour of website users?

Through attribution modelling, you can get answers to business questions like:

• Why do people buy from my website?
• What happens before they make a purchase?
• What prompted them to make a purchase or complete a predefined goal?
• What role did prior website referrals, searches, and ads play in conversion?

The answers to such questions help in understanding the buying behaviour of website users.

To learn more about the benefits of attribution modelling, check out this article: Benefits of Attribution Modelling in Online Marketing and Business

Why you need formal training in Attribution Modelling?

For many people, Attribution Modelling is still a mumbo jumbo.

They may have heard about it somewhere. And they may have a vague idea of what it is but they don’t really know how to use and benefit from it.

And that is because attribution modelling is full of jargon.

We have got ‘last click conversions’, we have got ‘first click conversions’, ‘data-driven conversions’.

We have got ‘click-through conversions’, we have got ‘view-through conversions’.

Then we have got dozens of different attribution windows, conversion windows, attribution models.

And then under each attribution window, conversion window and attribution model, we can have a range of different conversion volume and conversion values. 

Then we have got different types of cost per acquisitions (CPA) like Last Interaction CPA, Last non-direct click CPA, Linear CPA, Time Decay CPA…. and the list goes on and on. 

And the biggest headache of all is to decide which attribution window, conversion window and/or attribution model to use for optimizing marketing campaigns and interpreting data. 

All of this has made web analytics and optimization incredibly hard and complex. 

However, you need to learn all of this attribution modelling jargon.

And not just learn it but truly understand it, if you want to remain relevant in the current marketplace and in demand. 

Gone are those days when you were advertising on just one marketing platform and device, and attributing sales was as easy as looking into an analytics report. 

Analytics reports are not what you see is what you get. They are what you interpret is what you get. 

Now in the world of multi-channel and multi-device marketing and the ever-increasing number of marketing platforms, attributing conversions to the right touchpoint is becoming progressively complex and hard. 

Let’s be honest. Most of the online marketing that is exact today is not based on science or any formalized process. People are doing all sorts of different things to generate sales.

They blog, they produce videos, they are on Instagram, they do podcasts, they speak on stages throughout the world. But they don’t really know what is working and what is not working. 

They are just copying each other, like blind leading the blind. They do not have any data to back up their theory, formulate a hypothesis, or increase the budget.

They have got no way of knowing which marketing channels are actually working.

They can not put their finger on anyone marketing activity and can say with any degree of confidence that this is the marketing activity which has the most impact on our sales. 

What they have got is an ‘attribution problem’.

So you can no longer ignore attribution modelling unless you don’t mind getting obsolete.

Because markets reward those who continue to add value to the marketplace. Those who refuse to upgrade themselves or believe they know it all are left behind. 

How does Attribution work?

Attribution works just like a football team.

A football team is made up of players. Each player passes the ball to the other players with the aim of scoring a goal. The end goal of the team is to score as many goals as possible.

In the case of online marketing, your team is made up of different marketing channels, campaigns, keywords and devices. These are your players. All of these players work together to create sales and other conversions. 

All of these players pass customers through different user experiences which lead to sales. The end goal of your marketing team is to generate as many sales as possible.

In a football match, no single player is solely responsible for winning a match. Different players work together to win a match. The win is always a team effort.

Similarly, in a multi-channel marketing world, no single channel is solely responsible for generating conversions and sales.

Different marketing channels work together to create the user experience that leads to sales and conversions. 

That is why in attribution modelling we do not focus on measuring and optimizing just one marketing channel or campaign.

We do not create attribution strategies around a single channel or campaign.

Instead, we focus on measuring and optimizing the mix and weight of the portfolio of marketing channels or campaigns.

Every football team has good and bad players. In order to improve the overall performance of the team, you need to find and separate the good and bad players. 

Once you do that, you can then either focus on improving the performance of bad players or replace them with new players. 

Similarly, in the case of online marketing, you will have good and bad marketing channels, campaigns, ads, keywords and devices. 

You measure the performance of these online players in terms of their return on ad spend and/or their influence in assisting and closing sales.

In order to improve the overall ROI of your marketing efforts, you need to find and separate the good and bad players (campaigns) in your team.

For example, if a marketing campaign is consistently not assisting in any sale then it is a bad player in your team. 

You can either focus on improving the performance of this bad campaign or replace it with a brand new campaign.

In a football team, some players really shine in defense (i.e. stopping a competitor from scoring a goal) and some players really shine in offense (i.e. scoring a goal for their team).

However, if you do not understand their natural skills and abilities and end up placing a player which is good in defence in an offensive position, he will not be able to score as many goals as required to win a match.

Similarly, in the case of online marketing, some channels and campaigns are really good at initiating a customer’s purchase journey, whilst some are really good at building relationships and some are really good at closing sales. 

For example, display advertising (when done right) can help a lot in creating brand awareness and retention but it usually does not result in any direct sales.

So if you evaluate a display ad campaign by its ability to close sales, you are likely to face disappointment and may choose to de-invest in it. It is like judging a fish by its ability to climb a tree.

Similarly, some marketing campaigns are really good at getting ‘add to carts’, some are really good at ‘initiating checkouts’ and some are really good at closing sales.

As an online marketer, you need to optimize your campaigns for the right conversion objective.

If you optimize all of your campaigns only for closing sales, you are going to lose money. 

Just like if you instruct your entire football team to only play offense, the competing team could easily score more goals and win. 

In a football team, the performance of players can improve or deteriorate over time. 

There is always the possibility that the players you once identified as good, have now turned bad and the players you once identified as bad have now turned good.

In the same way, in the online marketing world, the performance of marketing channels and campaigns improve or deteriorate over time. 

There is always the possibility that the campaigns you once identified as good have now turned bad and the campaigns you once identified as bad have now turned good.

Because of this, you need to continuously find and separate good and bad marketing channels, ads and campaigns. 

You then need to either improve the performance of the bad campaigns or replace them with brand new campaigns. 

That is why attribution modelling is not a one-time activity. 

It is not something that you can do once and then forget about it. 

What is the difference between Attribution Modelling and Marketing Mix Modelling?

Attribution modelling and marketing mix modelling are not the same things.

Marketing mix modelling is carried out to measure and forecast the impact of various offline marketing activities on sales and ROI.

Whereas, Attribution Modelling is carried out to measure the impact of various online marketing activities on sales and ROI.

So in the context of digital media, the marketing mix modelling can be referred to as attribution modelling.

To learn more about the difference between Attribution Modelling and Marketing Mix Modelling, check out this article: Marketing Mix Modeling vs. Attribution Modeling. Which one is right for your business?

What are the different types of Attributions?

There are four types of attributions:

1. Multi-channel attribution.
2. Multi-device attribution.
3. Online-offline attribution.
4. Hybrid attribution.

What is Multi-channel attribution?

In the case of ‘multi-channel attribution’, we determine the impact of multiple digital marketing channels (paid search, organic search, email marketing, direct traffic, referral traffic, display advertising, etc) on conversions.

Multi-channel Attribution is the most popular attribution and when marketers talk about attribution they are generally referring to this attribution.

We try to understand how different digital channels work together to create user experience and conversions and how the credit for conversions should be distributed among various channels.

Google Analytics supports this type of attribution via multi-channel funnel reports and attribution reports.

The multi-channel attribution can be single-touch or multi-touch.

Single-touch attribution models include first and last touch attribution models. Whereas multi-touch attribution models include time decay, linear and position-based attribution models.

What is Multi-device Attribution?

In the case of multi-device attribution, we determine the impact of multiple devices (desktops, tablets, smartphones, smart TVs, etc.) on conversions.

We try to understand how different devices work together to create user experience and conversions and how the credit for conversions should be distributed among different devices.

What is Online-offline attribution?

In the case of online-offline attribution, we determine the impact of digital marketing channels on offline marketing channels and activities, and vice versa.

We try to understand how online and offline marketing campaigns work together to create user experience and conversions. We determine how the credit for conversions should be distributed among the different online and offline channels.

What is Hybrid attribution?

Hybrid attribution is the combination of multi-channel, multi-device and online-offline attributions.

It takes into account the back and forth activities of customers, between both online and offline channels and devices while distributing credit for conversions. 

In the real world, customers can go back and forth between online and offline marketing channels or they can go back and forth between tablet and desktop depending upon what stage they are in their purchase process, what they are buying, where they are, what device they own and what is their comfort level.

For example, some customers are more comfortable buying from a store than buying online. Whereas some customers are more comfortable buying online than buying from a store.

Some customers don’t purchase high price items online. Some customers always make a purchase offline and use online channels only for research work.

Some customers always make a purchase from a desktop or laptop. Some customers never make a purchase from smartphones while some always do.

So there can be ‘N’ types of buying behaviour.

None of the existing attribution models (Multi-channel, Multi-Device, Online-offline ) takes these back and forth activities of customers between multiple devices both online and offline into account while distributing credit for conversions. 

Therefore, it is not possible to get a complete picture of the conversion path followed by customers without using hybrid attribution. 

The real-world attribution is the hybrid of online-offline, multi-device, and multi-channel attributions. 

It takes into account the back and forth activities of customers between multiple devices both online and offline while distributing credit for conversions.

Because of that property, the hybrid attribution model is much more complex and difficult to develop than any existing attribution model. But at that same time, it is the only true attribution model.

You can fix hybrid attribution issues by integrating attribution modelling with marketing mix modelling. 

If your business has both an online and offline presence and you carry out both online and offline marketing then you are an ideal candidate for integrating attribution modelling with marketing mix modelling. 

What is nonline marketing?

Contrary to popular belief, marketing especially in the developed world is neither purely online nor purely offline, it is online.

In nonline marketing, we do not prioritise online marketing channels/touchpoints over offline marketing channels/touchpoints and vice versa, as customers can go back and forth between the two.

What is nonline analytics?

In nonline analytics, we do not measure just the online customer purchase journey or just the offline customer journey but we measure the overall customer journey which includes exposure to both online and offline marketing channels/touchpoints.

To learn more about nonline marketing and analytics, check out this article: Introduction to Nonline Analytics – True Multi Channel Analytics

What is an Attribution Model in Google Analytics?

In the context of Google Analytics, an attribution model is a set of rules or algorithms that determine how credit for conversions should be attributed/distributed to various touchpoints on a conversion path.

A touchpoint (also known as an interaction) is exposure to a marketing channel.

A conversion path is the navigation path which a user followed to complete a conversion on your website.

It is made up of a sequence of interactions/touchpoints with digital marketing channels during the 1 to 90 day period that led to conversions.

Categories of Google Analytics Attribution Models

Attribution models in Google Analytics can be broadly classified into two categories:

1) Default attribution models – These are pre-built models that define how credit for conversion should be distributed to various interactions (or touchpoints) in a conversion path before the custom credit rules are applied.

2) Custom attribution models – These are user-defined attribution models. When you build your own attribution model, you create your own rules to assign credit to different interactions on a conversion path. These rules are known as custom credit rules in Google Analytics.

What are the different types of Google Analytics Attribution Models?

Google Analytics provides eight different types of default attribution models:

1. Last interaction attribution model (also known as the last touch attribution model)
2. First interaction attribution model (also known as the first touch attribution model)
3. Linear attribution model
4. Time decay attribution model
5. Position based attribution model
6. Last non-direct click attribution model
7. Last Google Ads Click
8. Data-Driven Attribution Model

To learn more about the different types of Google Analytics attribution models, check out this article: Default & Custom Google Analytics Attribution Models Explained

What is the advantage of using an Attribution Model in Google Analytics?

Through attribution models, you can value your marketing from different perspectives. When you use an attribution model, it impacts the valuation of your marketing channels.

Through attribution models, you can evaluate the effectiveness of your marketing campaigns. You can evaluate your assumptions in your conversion path data.

You use the attribution model output to increase or decrease investment in a marketing channel and then monitor how it affects your conversions and sales over time.

Through attribution models, you can test your assumptions by experimenting. Attribution models are used for experimenting and testing purposes

For example, under the last-click attribution model, your display advertising may be heavily undervalued because of your customers’ unique purchase behaviour.

Maybe the majority of your customers are getting influenced by your display ads in their conversion journey but they are not clicking on these ads before making a purchase.

But how can you know for sure whether or not display advertising is undervalued or overvalued without creating and comparing an attribution model with the last-touch attribution model. This is where custom attribution models come into the picture.

You create a hypothesis and then test it by creating a custom attribution model. You then compare your model with the last-touch model or some other attribution model. The hypothesis you create is based on your analysis.

Your hypothesis could be something like:

“If a user completes a goal conversion on my website within 12 hours after viewing (but not clicking) one of my display ads, then the display ad impressions should get two times more conversion credit than the other interactions in the conversion path.”

You can test this hypothesis by creating a custom attribution model. 

Additional Reading: How to Create Custom Attribution Model in Google Analytics

Before you start Attribution Modelling

Before you start attribution modelling make sure that you have acquired a very deep understanding of your client’s business, their industry, and their target market.

Because if you don’t, then you will end up applying/creating a wrong attribution model and lose a lot of money.

You have developed a great understanding of a business when you can do all of the following:

1. You can confidently look beyond data and raw numbers and can make business and marketing decisions based on: 

• Context.
• Faith (collective know-how of your organisation, target audience, and industry).
• All business and marketing activities outside the digital realm. 

2. You do not dismiss any claim just because it cannot be backed up with data.

3. You understand what your analytics tools and KPIs can measure and cannot measure, and where to trade-off. 

4. You know exactly what data needs to be collected, integrated and analysed, and what data should be overlooked. 

Before you start attribution modelling purge your analytics data

Any business or marketing decision based on flawed data will not produce optimum results and could also result in financial loss.

So for example, be careful what you track as Goal conversions in Google Analytics. I have seen analytics accounts where marketers track ‘visits to the home page’ as a conversion or ‘visits to the product category page’ as conversion.

Track only those goals which are beneficial to your customers and company. Irrelevant goals can drastically skew your conversion rate and the data in the multi-channel funnel reports.

Related Article: You are doing Conversion Tracking all wrong. Here is why

Barriers to implementing Attribution Modelling

The following are the top barriers to implementing attribution modelling:

1. A work culture that is not data-driven
2. Lack of business agility
3. A work culture that does not encourage innovation
4. Presence of organizational silos and other operational inefficiencies
5. Non-alignment of goals and KPIs across an organisation
6. Lack of authority
7. Lack of expertise
8. Lack of tools, technology, and processes
9. Data collection issues
10. Data integration issues

To learn more about these barriers, check out the article: The top barriers to implementing Attribution Modelling

Acquisition channels and Attribution Modelling

Just like hits and sessions, acquisition channels are the building blocks of Google Analytics data and reports. They are the very foundation of Google Analytics (GA) data interpretation. 

In order to understand user behaviour and website usage data, you need to understand:

1. How Google Analytics defines various traffic sources and mediums. For example, how GA defines organic search and how this differs to paid search. 

2. How Google Analytics defines campaigns and channels.

The knowledge of attribution channels will later help you to understand channel groupings, multi-channel funnel groupings and conversion paths, all of which are an integral part of various multi-channel funnel reports. 

Through multi-channel funnel reports, you can understand how different marketing channels work together to create conversions and sales on your website.  

What are Acquisition Channels?

In multi-channel funnel reports, the acquisition channels are commonly referred to as marketing channels or channels.

Also known as marketing channels, digital channels or channels, these are the sources of traffic to your website. For example, paid search, organic search, direct, social media, email, affiliate, referral, etc are all examples of acquisition channels.

To learn more about acquisition channels, read this article: Understanding Channels in Google Analytics.

What are Multi-Channel Funnel Reports in Google Analytics?

In Google Analytics, you can carry out Attribution Modelling through the multi-channel funnel reports.

You can access these reports by navigating to Conversions > Multi-Channel Funnels in your GA reporting view:

Google Analytics Multi-Channel Funnels provide the following eight reports for attribution analysis:

1. Multi-channel funnels overview
2. Assisted conversions
3. Top conversion paths (does not show conversion credit assigned to each touchpoint)
4. Time lag report
5. Path length report
6. Model comparison tool
7. ROI analysis (This report is available only in GA 360 enabled properties.)
8. Model explorer (This report is available only in GA 360 enabled properties.)

Through multi-channel funnel reports, you can determine how different marketing channels and campaigns work together to create sales and other conversions.

You can understand the role of marketing channels and campaigns in initiating, assisting and completing conversions.

To learn more about the multi-channel funnel reports, check out the article: Google Analytics Multi-Channel Funnel Reports – Tutorial

What are Channel Labels in Google Analytics?

Channel label is the label applied to a digital marketing channel in Google Analytics.

For example, paid search, organic search, social, display, etc. are all examples of channel labels.

There are two types of channel labels in Google Analytics:

1. Default channel labels
2. Custom channel labels

The default channel labels are the predefined channel labels.

For example, paid search, organic search, referral, display, email, social, direct and other are default channel labels:

The custom channel labels are the labels defined by a user.

Here, ‘Paid NB Keywords’, ‘Not Provided Keywords’, ‘Paid B Keywords’, ‘Organic NB Keywords’ and ‘Organic B Keywords’ are examples of custom channel labels.

What is Channel Grouping in Google Analytics?

Channel grouping is a set of channel labels. There are two types of channel grouping in Google Analytics:

1. Default channel grouping
2. Custom channel grouping

The default channel grouping is the set of predefined channel labels. The custom channel grouping is the channel grouping created by a user.

Defining channel labels is part of creating your own channel grouping. 

You can define a channel label by creating specific rules. Each rule is based on one or more conditions.

Note: You can create as many channel groupings as you want.

To learn more about defining your own channel labels and channel groupings in general, check out the following articles:

1. Default and Custom Channel Grouping in Google Analytics Explained
2. Understanding MCF Channel Groupings in Google Analytics
3. Using Custom Channels to understand Google Analytics Reports

What are First click, Assisted and Direct Conversions?

The First click conversions are the number of conversions initiated by a marketing channel.

The Assisted conversions are the number of conversions indirectly achieved by a marketing channel.

The Direct Conversions are the number of conversions directly achieved by a marketing channel.

Many optimizers still evaluate the performance of a marketing campaign according to the number of direct conversions attributed to it. However, this is a sub-optimal way of evaluating the performance of a marketing channel.

Because if a marketing channel is not directly completing a conversion, maybe it is initiating a conversion or assisting a conversion.

So before you stop investing in a marketing channel just because the direct conversions reported by Google Analytics are too low or zero, also determine the first click and assisted conversions of the channel.

If the marketing channel is being attributed a considerable amount of the first click and/or assisted conversions then think twice before de-investing in it.

To learn more about the first click, assisted and direct conversions, check out the following articles:

1. First Click Conversions and Value in Google Analytics Explained
2. Assisted Conversions and Value in Google Analytics Explained
3. Last Click or Direct Conversions and Value in Google Analytics Explained

What are direct and assisted marketing channels?

In the context of attribution modelling, marketing channels can be broadly classified into two categories: direct and assisted marketing channels.

The marketing channels which directly completed a conversion are called direct marketing channels. For example, a user clicked on your paid search ad and then made a purchase on your website.

Here paid search marketing channel directly completed the conversion. As such it is referred to as a direct marketing channel.

The marketing channels which indirectly completed a conversion are called assisted marketing channels.

For example, a user clicked on your paid search ad and then later returned to your website via organic search to complete a purchase.

Here paid search marketing channel indirectly completed the conversion. As such it is referred to as an assisted marketing channel.

So whether a marketing channel is referred to as direct or assisted depends upon the role it played on a conversion path.

The role of assisted marketing channels changes all the time.

It is important to continuously explore the role of different marketing channels and campaigns in assisting conversions. This is because the role of assisted marketing channels changes all the time.

The marketing channel or activity that helped you to generate a conversion today may not help you to generate the same conversion tomorrow.

Because of the ever-changing role of assisted marketing channels in conversion paths, your marketing campaigns tend to perform differently each month even if you do not directly make any considerable changes to them.

Related Article: What You Should Know about Historical Data in Web Analytics

Different Types of Conversion Values in Multi-Channel Funnels reports

In multi-channel funnel reports, there are three types of conversion values:

1. Assisted conversion value – It is the total economic value of assisted conversions.
2. Last click or direct conversion value – It is the total economic value of last interaction conversions.
3. First click conversion value – It is the total economic value of first click conversions.

To learn more about the different types of conversion values in multi-channel funnel reports, check out the following articles:

1. First Click Conversions and Value in Google Analytics Explained
2. Assisted Conversions and Value in Google Analytics Explained
3. Last Click or Direct Conversions and Value in Google Analytics Explained

How Assisted/Last click conversion of a marketing channel is calculated and what insight you can get from it?

The Assisted/Last click conversion is a ratio metric that is calculated as the number of assisted conversions / number of last-click conversions

To learn more about the Assisted/Last click conversion of a marketing channel check out this article: Assisted / Last Click or Direct Conversions in Google Analytics Explained

How First/Last click conversion of a marketing channel is calculated and what insight you can get from it?

The First/Last click conversion is a ratio metric that is calculated as the number of first click conversions / number of last-click conversions

To learn more about the First/Last click conversion of a marketing channel check out this article: First / Last Click or Direct Conversions in Google Analytics Explained

The future of Attribution Modelling

What will attribution modelling looks like in the near future when the third party cookies are gone? Will attribution modelling be dead?

You probably know that an increasing number of users are now using ad blockers. And browsers continue to restrict access to more and more users’ data.

All of these tracking restrictions are creating big data gaps in the conversion paths and making it very difficult to attribute conversions correctly. So how do you advertise profitably then?

Is this the end of website tracking as we know it? …..No. It is actually the dawn of new and more powerful tracking called ‘server-side tracking’.

Learn more: The Future of Attribution Modeling – Attribution without cookies

Other Articles on Google Analytics Attribution Modelling

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2. How to valuate Display Advertising through Attribution Modelling
3. Shopping Cart Analytics Tutorial
4. 6 Keys to Digital Success in Attribution Modelling
5. How to explain attribution modelling to your clients
6. Default and Custom Attribution Models in Google Analytics
7. Understanding Missing Touchpoints in Attribution Modelling
8. What You Should Know about Historical Data in Web Analytics
9. Model Comparison Report Explained in Google Analytics Attribution
10. Data-Driven Attribution Model in Google Analytics – Tutorial
11. Conversion Lag Report Explained in Google Analytics Attribution
12. Selecting the Best Attribution Model for Inbound Marketing
13. How to do ROI Analysis in Google Analytics
14. Conversion Credit Models Guide – Google Analytics Attribution
15. Introduction to Nonline Analytics – True Multi Channel Analytics
16. Conversion Types Explained in Google Analytics Attribution
17. Attribution Channels Explained in Google Analytics Attribution
18. Differences Between Google Attribution & Multi-Channel Funnel Reports
19. Introduction to TV Attribution in Google Analytics Attribution 360
20. Conversion Credit Distribution for Attribution Models in Google Analytics
21. Conversion Paths Report Explained in Google Analytics Attribution
22. Attribution Model Comparison Tool in Google Analytics
23. Touchpoint Analysis in Google Analytics Attribution Modelling
24. Attributed Conversions & Attributed Revenue Explained in Google Attribution
25. Which Attribution Model to use in Google Analytics?
26. Google Attribution Access and User Permissions – Tutorial
27. Conversion Path Length Report Explained in Google Analytics Attribution
28. How to set up a data-driven attribution model in Google Analytics
29. View-Through Conversion Tracking in Google Analytics
30. Offline Conversion Tracking in Google Analytics – Tutorial
31. How to Create Custom Attribution Model in Google Analytics
32. 8 Google Analytics Conversions Segments You Must Use
33. You are doing Google Analytics all wrong. Here is why
34. How to Use ZMOT to Increase Conversions and Sales Exponentially
35. Connected Properties Explained in Google Analytics Attribution
36. Marketing Mix Modelling or Attribution Modelling. Which one is for you?
37. How is attribution modelling helpful for ecommerce and non-ecommerce websites?
38. Conversion Time & Interaction Time Explained in Google Analytics Attribution
39. How to Allocate Budgets in Multi Channel Marketing
40. Data-Driven Attribution Model Explorer in Google Analytics
41. Google Analytics Attribution Beta and Project Tutorial
42. Google Ads Performance Report Attribution Beta
43. Google Attribution vs Google Analytics Multi-Channel Funnel Reports

Frequently Asked Questions About Google Analytics Attribution Modelling