6 Types of Attribution Models You Can Use for Better Data
6 Types of Attribution Models You Can Use for Better Data
6 Types of Attribution Models You Can Use for Better Data
Do you want to learn more about different types of attribution models?
Learning more about your customer's journey is a critical part of increasing conversions across your site. In some cases, you may want to know where users began from their very first click.
At other times, it might be better to see the last action your customers took that led to a purchase.
At any rate, the most successful marketers understand the differences between the types of attribution models, which is what we'll cover today.
But first, let's get clear on why understanding the customer journey is so crucial for growing your (or your client's) business.
Why Understanding Attribution Models Matters
There are plenty of reasons why understanding attribution models is important.
For one thing, it helps marketers understand the customer journey. This information can provide invaluable insights into how to increase conversions across your site in the future.
Another reason why attribution models are important is that they help marketers know the best time to invest in marketing campaigns.
For example, if you have a high-value customer with lots of lifetime value, it might be wise to invest your money there instead of in new customers or low-value customers.
If you don't have all the relevant information for your sales funnel, then it will be tough for your company to make any changes that could potentially increase conversions in the future.
So this is why understanding attribution models matter and knowing how each of them works can really help!
Now let's look at the 6 different types of attribution models you can start using to track data today.
Types of Attribution Models
There are six main types of attribution models:
First Interaction
Last Interaction
Last Non-Direct Click
Linear
Time-Decay
Position-Based
Let's look at each type in a bit more detail.
1) First Interaction
As the name suggests, the first interaction attribution model assigns conversions to the first instance a user engages with your brand. This is usually from a 3rd-party site like a social media platform or a paid ad.
Advantages: The first interaction attribution model is a good choice if you want to track the time it takes for users to convert across different channels.
This might be helpful, for example, if your company has an in-house CRM and wants to see how long it took people who converted after interacting with ads on Facebook.
Disadvantages: The first interaction model is less accurate because it doesn't take into account where the user came from before converting.
For example, let's say a customer clicked on an ad for your online store and then went to Amazon instead of making their purchase directly through you. If this was your only attribution metric, then you would think that they came from Amazon when in reality, they had clicked on a paid ad.
2) Last Interaction
The last interaction model assigns conversions to the latest instance that a user engaged with your brand.
This is most often accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last interaction attribution model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process.
Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
In this case, it would show that Google was their last interaction before converting instead of the original ad (which is what you'll want to know).
What's more, some people might use a "conversion" as anything from signing up for your email list or downloading an eBook all the way up to making a purchase.
If you're not careful, then the last interaction model can lead to inaccurate data because it doesn't account for what type of conversion took place before someone bought from your company.
3) Last Non-Direct Click Interaction
The last non-direct click interaction model assigns conversions to the latest instance that a user interacted with your brand other than through a paid ad.
This can be accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last non-direct click interaction model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process. Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last non-direct click interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
4) Linear Attribution Model
The linear attribution model splits conversions equally among all the engagements users had with your business.
For example, if a user engaged with your company through an ad and then later on organic search before converting, this attribution model would show 50% for paid ads and 50% for organic.
Advantages: Using the linear attribution model is easy because you don't have to think too hard about how much of each channel contributed to the conversion.
Disadvantages: The biggest drawback of the linear attribution model is that it's not accurate for many businesses because conversions are usually not evenly divided among channels - and, in fact, could be heavily skewed towards a single channel.
This means that if you use this type of attribution model to track your data then you might think that paid ads are only responsible for half of your sales when they may actually be 95%.
5) Time-Decay Attribution
Time-decay attribution is similar to linear attribution, but it takes into account when these conversions took place (rather than splitting them up completely equally).
For example, if a user interacted with your company through an ad and then later on organic search before converting, this attribution model would show 25% for ads and 75% for organic (which was the most recent).
Advantages: One of the advantages of time-decay is that it takes into account when users converted - meaning you can see how many conversions are attributable to each channel from a single user.
This is useful for figuring out which channels are more effective at converting users who first clicked on your brand through paid ads, and then later interacted with you before coming back and engaging with you again.
Disadvantages: The biggest drawback of the time-decay attribution model is that it's a bit more complicated than the linear model (because it takes into account when conversions occur).
6) Position-Based Attribution
The position-based attribution model divides conversions based on where the user was in your conversion funnel when they converted.
For example, if a user came to your site through an ad and then later on organic search before converting, this attribution model would show 50% for ads and 50% for organic.
Advantages: One of the advantages of position-based attribution is that it accounts for when a user was in your conversion funnel - like if they were at a product page or their cart.
This means you can see what channels are better at converting users who engage with you after coming from an ad, and then later on experience something else before converting (like organic).
Disadvantages: The biggest drawback of the position-based attribution model is that it's not as straightforward to calculate because it takes into account where a user was in your conversion funnel when they converted.
Keeping Your Eye on the Data
One of the biggest problems marketers face with attribution models isn’t understanding the theory. Instead, it’s putting that theory into practice.
By that, I mean gathering this data won’t do you any good unless you use it to implement changes in your marketing strategy.
And for that, you should have your conversion data close at hand on a regular basis.
While you can always prepare that data manually, doing so is time-intensive, prone to human error, and expensive (remember, you still need to pay for someone’s time to compile that data).
That’s why I always recommend using a report building tool like Metrics Watch:
I built Metrics Watch to help marketers get the data they need to make the right changes for their (or their client’s) growth.
This tool allows you to build professional marketing reports in a matter of minutes, even if you have no technical skills at all.
That’s because you can use the drag and drop visual builder to create reports and pull data from your favorite sources, such as:
Google Analytics
Google Search Console
Google Ads
Facebook (paid and organic)
Instagram (paid and organic)
LinkedIn (paid and organic)
And more…
With that in mind, you can easily create reports involving your attribution models from Google Analytics.
Then you can have these reports compiled and sent automatically on a daily, weekly, or monthly basis.
But the best part is that these reports aren’t shared through PDF attachments or 3rd-party user dashboards.
The former requires tons of organization on behalf of you or your clients. And the latter requires user management roles like logins and password sharing.
With Metrics Watch, you simply send the data directly to your recipient’s inbox. That means your people get the information they need in a format they’re already familiar with.
And that’s all for today! We hope this article gave you a quick glimpse of the different types of attribution models.
If you enjoyed this post, you’ll definitely want to check out the following resources:
These articles will have even more information that you can use to create smarter data-driven marketing strategies that lead to actual growth.
Want to see Metrics Watch in action for yourself? Click below to start your 100% risk-free Metrics Watch trial today (no credit card required):
Do you want to learn more about different types of attribution models?
Learning more about your customer's journey is a critical part of increasing conversions across your site. In some cases, you may want to know where users began from their very first click.
At other times, it might be better to see the last action your customers took that led to a purchase.
At any rate, the most successful marketers understand the differences between the types of attribution models, which is what we'll cover today.
But first, let's get clear on why understanding the customer journey is so crucial for growing your (or your client's) business.
Why Understanding Attribution Models Matters
There are plenty of reasons why understanding attribution models is important.
For one thing, it helps marketers understand the customer journey. This information can provide invaluable insights into how to increase conversions across your site in the future.
Another reason why attribution models are important is that they help marketers know the best time to invest in marketing campaigns.
For example, if you have a high-value customer with lots of lifetime value, it might be wise to invest your money there instead of in new customers or low-value customers.
If you don't have all the relevant information for your sales funnel, then it will be tough for your company to make any changes that could potentially increase conversions in the future.
So this is why understanding attribution models matter and knowing how each of them works can really help!
Now let's look at the 6 different types of attribution models you can start using to track data today.
Types of Attribution Models
There are six main types of attribution models:
First Interaction
Last Interaction
Last Non-Direct Click
Linear
Time-Decay
Position-Based
Let's look at each type in a bit more detail.
1) First Interaction
As the name suggests, the first interaction attribution model assigns conversions to the first instance a user engages with your brand. This is usually from a 3rd-party site like a social media platform or a paid ad.
Advantages: The first interaction attribution model is a good choice if you want to track the time it takes for users to convert across different channels.
This might be helpful, for example, if your company has an in-house CRM and wants to see how long it took people who converted after interacting with ads on Facebook.
Disadvantages: The first interaction model is less accurate because it doesn't take into account where the user came from before converting.
For example, let's say a customer clicked on an ad for your online store and then went to Amazon instead of making their purchase directly through you. If this was your only attribution metric, then you would think that they came from Amazon when in reality, they had clicked on a paid ad.
2) Last Interaction
The last interaction model assigns conversions to the latest instance that a user engaged with your brand.
This is most often accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last interaction attribution model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process.
Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
In this case, it would show that Google was their last interaction before converting instead of the original ad (which is what you'll want to know).
What's more, some people might use a "conversion" as anything from signing up for your email list or downloading an eBook all the way up to making a purchase.
If you're not careful, then the last interaction model can lead to inaccurate data because it doesn't account for what type of conversion took place before someone bought from your company.
3) Last Non-Direct Click Interaction
The last non-direct click interaction model assigns conversions to the latest instance that a user interacted with your brand other than through a paid ad.
This can be accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last non-direct click interaction model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process. Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last non-direct click interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
4) Linear Attribution Model
The linear attribution model splits conversions equally among all the engagements users had with your business.
For example, if a user engaged with your company through an ad and then later on organic search before converting, this attribution model would show 50% for paid ads and 50% for organic.
Advantages: Using the linear attribution model is easy because you don't have to think too hard about how much of each channel contributed to the conversion.
Disadvantages: The biggest drawback of the linear attribution model is that it's not accurate for many businesses because conversions are usually not evenly divided among channels - and, in fact, could be heavily skewed towards a single channel.
This means that if you use this type of attribution model to track your data then you might think that paid ads are only responsible for half of your sales when they may actually be 95%.
5) Time-Decay Attribution
Time-decay attribution is similar to linear attribution, but it takes into account when these conversions took place (rather than splitting them up completely equally).
For example, if a user interacted with your company through an ad and then later on organic search before converting, this attribution model would show 25% for ads and 75% for organic (which was the most recent).
Advantages: One of the advantages of time-decay is that it takes into account when users converted - meaning you can see how many conversions are attributable to each channel from a single user.
This is useful for figuring out which channels are more effective at converting users who first clicked on your brand through paid ads, and then later interacted with you before coming back and engaging with you again.
Disadvantages: The biggest drawback of the time-decay attribution model is that it's a bit more complicated than the linear model (because it takes into account when conversions occur).
6) Position-Based Attribution
The position-based attribution model divides conversions based on where the user was in your conversion funnel when they converted.
For example, if a user came to your site through an ad and then later on organic search before converting, this attribution model would show 50% for ads and 50% for organic.
Advantages: One of the advantages of position-based attribution is that it accounts for when a user was in your conversion funnel - like if they were at a product page or their cart.
This means you can see what channels are better at converting users who engage with you after coming from an ad, and then later on experience something else before converting (like organic).
Disadvantages: The biggest drawback of the position-based attribution model is that it's not as straightforward to calculate because it takes into account where a user was in your conversion funnel when they converted.
Keeping Your Eye on the Data
One of the biggest problems marketers face with attribution models isn’t understanding the theory. Instead, it’s putting that theory into practice.
By that, I mean gathering this data won’t do you any good unless you use it to implement changes in your marketing strategy.
And for that, you should have your conversion data close at hand on a regular basis.
While you can always prepare that data manually, doing so is time-intensive, prone to human error, and expensive (remember, you still need to pay for someone’s time to compile that data).
That’s why I always recommend using a report building tool like Metrics Watch:
I built Metrics Watch to help marketers get the data they need to make the right changes for their (or their client’s) growth.
This tool allows you to build professional marketing reports in a matter of minutes, even if you have no technical skills at all.
That’s because you can use the drag and drop visual builder to create reports and pull data from your favorite sources, such as:
Google Analytics
Google Search Console
Google Ads
Facebook (paid and organic)
Instagram (paid and organic)
LinkedIn (paid and organic)
And more…
With that in mind, you can easily create reports involving your attribution models from Google Analytics.
Then you can have these reports compiled and sent automatically on a daily, weekly, or monthly basis.
But the best part is that these reports aren’t shared through PDF attachments or 3rd-party user dashboards.
The former requires tons of organization on behalf of you or your clients. And the latter requires user management roles like logins and password sharing.
With Metrics Watch, you simply send the data directly to your recipient’s inbox. That means your people get the information they need in a format they’re already familiar with.
And that’s all for today! We hope this article gave you a quick glimpse of the different types of attribution models.
If you enjoyed this post, you’ll definitely want to check out the following resources:
These articles will have even more information that you can use to create smarter data-driven marketing strategies that lead to actual growth.
Want to see Metrics Watch in action for yourself? Click below to start your 100% risk-free Metrics Watch trial today (no credit card required):
Do you want to learn more about different types of attribution models?
Learning more about your customer's journey is a critical part of increasing conversions across your site. In some cases, you may want to know where users began from their very first click.
At other times, it might be better to see the last action your customers took that led to a purchase.
At any rate, the most successful marketers understand the differences between the types of attribution models, which is what we'll cover today.
But first, let's get clear on why understanding the customer journey is so crucial for growing your (or your client's) business.
Why Understanding Attribution Models Matters
There are plenty of reasons why understanding attribution models is important.
For one thing, it helps marketers understand the customer journey. This information can provide invaluable insights into how to increase conversions across your site in the future.
Another reason why attribution models are important is that they help marketers know the best time to invest in marketing campaigns.
For example, if you have a high-value customer with lots of lifetime value, it might be wise to invest your money there instead of in new customers or low-value customers.
If you don't have all the relevant information for your sales funnel, then it will be tough for your company to make any changes that could potentially increase conversions in the future.
So this is why understanding attribution models matter and knowing how each of them works can really help!
Now let's look at the 6 different types of attribution models you can start using to track data today.
Types of Attribution Models
There are six main types of attribution models:
First Interaction
Last Interaction
Last Non-Direct Click
Linear
Time-Decay
Position-Based
Let's look at each type in a bit more detail.
1) First Interaction
As the name suggests, the first interaction attribution model assigns conversions to the first instance a user engages with your brand. This is usually from a 3rd-party site like a social media platform or a paid ad.
Advantages: The first interaction attribution model is a good choice if you want to track the time it takes for users to convert across different channels.
This might be helpful, for example, if your company has an in-house CRM and wants to see how long it took people who converted after interacting with ads on Facebook.
Disadvantages: The first interaction model is less accurate because it doesn't take into account where the user came from before converting.
For example, let's say a customer clicked on an ad for your online store and then went to Amazon instead of making their purchase directly through you. If this was your only attribution metric, then you would think that they came from Amazon when in reality, they had clicked on a paid ad.
2) Last Interaction
The last interaction model assigns conversions to the latest instance that a user engaged with your brand.
This is most often accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last interaction attribution model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process.
Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
In this case, it would show that Google was their last interaction before converting instead of the original ad (which is what you'll want to know).
What's more, some people might use a "conversion" as anything from signing up for your email list or downloading an eBook all the way up to making a purchase.
If you're not careful, then the last interaction model can lead to inaccurate data because it doesn't account for what type of conversion took place before someone bought from your company.
3) Last Non-Direct Click Interaction
The last non-direct click interaction model assigns conversions to the latest instance that a user interacted with your brand other than through a paid ad.
This can be accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last non-direct click interaction model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process. Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last non-direct click interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
4) Linear Attribution Model
The linear attribution model splits conversions equally among all the engagements users had with your business.
For example, if a user engaged with your company through an ad and then later on organic search before converting, this attribution model would show 50% for paid ads and 50% for organic.
Advantages: Using the linear attribution model is easy because you don't have to think too hard about how much of each channel contributed to the conversion.
Disadvantages: The biggest drawback of the linear attribution model is that it's not accurate for many businesses because conversions are usually not evenly divided among channels - and, in fact, could be heavily skewed towards a single channel.
This means that if you use this type of attribution model to track your data then you might think that paid ads are only responsible for half of your sales when they may actually be 95%.
5) Time-Decay Attribution
Time-decay attribution is similar to linear attribution, but it takes into account when these conversions took place (rather than splitting them up completely equally).
For example, if a user interacted with your company through an ad and then later on organic search before converting, this attribution model would show 25% for ads and 75% for organic (which was the most recent).
Advantages: One of the advantages of time-decay is that it takes into account when users converted - meaning you can see how many conversions are attributable to each channel from a single user.
This is useful for figuring out which channels are more effective at converting users who first clicked on your brand through paid ads, and then later interacted with you before coming back and engaging with you again.
Disadvantages: The biggest drawback of the time-decay attribution model is that it's a bit more complicated than the linear model (because it takes into account when conversions occur).
6) Position-Based Attribution
The position-based attribution model divides conversions based on where the user was in your conversion funnel when they converted.
For example, if a user came to your site through an ad and then later on organic search before converting, this attribution model would show 50% for ads and 50% for organic.
Advantages: One of the advantages of position-based attribution is that it accounts for when a user was in your conversion funnel - like if they were at a product page or their cart.
This means you can see what channels are better at converting users who engage with you after coming from an ad, and then later on experience something else before converting (like organic).
Disadvantages: The biggest drawback of the position-based attribution model is that it's not as straightforward to calculate because it takes into account where a user was in your conversion funnel when they converted.
Keeping Your Eye on the Data
One of the biggest problems marketers face with attribution models isn’t understanding the theory. Instead, it’s putting that theory into practice.
By that, I mean gathering this data won’t do you any good unless you use it to implement changes in your marketing strategy.
And for that, you should have your conversion data close at hand on a regular basis.
While you can always prepare that data manually, doing so is time-intensive, prone to human error, and expensive (remember, you still need to pay for someone’s time to compile that data).
That’s why I always recommend using a report building tool like Metrics Watch:
I built Metrics Watch to help marketers get the data they need to make the right changes for their (or their client’s) growth.
This tool allows you to build professional marketing reports in a matter of minutes, even if you have no technical skills at all.
That’s because you can use the drag and drop visual builder to create reports and pull data from your favorite sources, such as:
Google Analytics
Google Search Console
Google Ads
Facebook (paid and organic)
Instagram (paid and organic)
LinkedIn (paid and organic)
And more…
With that in mind, you can easily create reports involving your attribution models from Google Analytics.
Then you can have these reports compiled and sent automatically on a daily, weekly, or monthly basis.
But the best part is that these reports aren’t shared through PDF attachments or 3rd-party user dashboards.
The former requires tons of organization on behalf of you or your clients. And the latter requires user management roles like logins and password sharing.
With Metrics Watch, you simply send the data directly to your recipient’s inbox. That means your people get the information they need in a format they’re already familiar with.
And that’s all for today! We hope this article gave you a quick glimpse of the different types of attribution models.
If you enjoyed this post, you’ll definitely want to check out the following resources:
These articles will have even more information that you can use to create smarter data-driven marketing strategies that lead to actual growth.
Want to see Metrics Watch in action for yourself? Click below to start your 100% risk-free Metrics Watch trial today (no credit card required):
Do you want to learn more about different types of attribution models?
Learning more about your customer's journey is a critical part of increasing conversions across your site. In some cases, you may want to know where users began from their very first click.
At other times, it might be better to see the last action your customers took that led to a purchase.
At any rate, the most successful marketers understand the differences between the types of attribution models, which is what we'll cover today.
But first, let's get clear on why understanding the customer journey is so crucial for growing your (or your client's) business.
Why Understanding Attribution Models Matters
There are plenty of reasons why understanding attribution models is important.
For one thing, it helps marketers understand the customer journey. This information can provide invaluable insights into how to increase conversions across your site in the future.
Another reason why attribution models are important is that they help marketers know the best time to invest in marketing campaigns.
For example, if you have a high-value customer with lots of lifetime value, it might be wise to invest your money there instead of in new customers or low-value customers.
If you don't have all the relevant information for your sales funnel, then it will be tough for your company to make any changes that could potentially increase conversions in the future.
So this is why understanding attribution models matter and knowing how each of them works can really help!
Now let's look at the 6 different types of attribution models you can start using to track data today.
Types of Attribution Models
There are six main types of attribution models:
First Interaction
Last Interaction
Last Non-Direct Click
Linear
Time-Decay
Position-Based
Let's look at each type in a bit more detail.
1) First Interaction
As the name suggests, the first interaction attribution model assigns conversions to the first instance a user engages with your brand. This is usually from a 3rd-party site like a social media platform or a paid ad.
Advantages: The first interaction attribution model is a good choice if you want to track the time it takes for users to convert across different channels.
This might be helpful, for example, if your company has an in-house CRM and wants to see how long it took people who converted after interacting with ads on Facebook.
Disadvantages: The first interaction model is less accurate because it doesn't take into account where the user came from before converting.
For example, let's say a customer clicked on an ad for your online store and then went to Amazon instead of making their purchase directly through you. If this was your only attribution metric, then you would think that they came from Amazon when in reality, they had clicked on a paid ad.
2) Last Interaction
The last interaction model assigns conversions to the latest instance that a user engaged with your brand.
This is most often accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last interaction attribution model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process.
Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
In this case, it would show that Google was their last interaction before converting instead of the original ad (which is what you'll want to know).
What's more, some people might use a "conversion" as anything from signing up for your email list or downloading an eBook all the way up to making a purchase.
If you're not careful, then the last interaction model can lead to inaccurate data because it doesn't account for what type of conversion took place before someone bought from your company.
3) Last Non-Direct Click Interaction
The last non-direct click interaction model assigns conversions to the latest instance that a user interacted with your brand other than through a paid ad.
This can be accomplished by looking at what site or platform they were on when they converted and determining this was their final click before conversion.
Advantages: One advantage of using the last non-direct click interaction model is that it's great for analyzing the customer journey.
This is helpful, for example, if you want to see where a user's final click before converting was and how long it took them to convert at each point in the process. Another benefit of using this attribution model is that it can be used with all forms of marketing - paid ads, social media campaigns, and in-house CRM.
Disadvantages: The biggest drawback of the last non-direct click interaction model is that it doesn't account for when a user initially interacted with your brand before converting.
This means they may have originally clicked on an ad, but then left or came back to finish their purchase through another channel, like after searching Google and not finding the product they were looking for.
4) Linear Attribution Model
The linear attribution model splits conversions equally among all the engagements users had with your business.
For example, if a user engaged with your company through an ad and then later on organic search before converting, this attribution model would show 50% for paid ads and 50% for organic.
Advantages: Using the linear attribution model is easy because you don't have to think too hard about how much of each channel contributed to the conversion.
Disadvantages: The biggest drawback of the linear attribution model is that it's not accurate for many businesses because conversions are usually not evenly divided among channels - and, in fact, could be heavily skewed towards a single channel.
This means that if you use this type of attribution model to track your data then you might think that paid ads are only responsible for half of your sales when they may actually be 95%.
5) Time-Decay Attribution
Time-decay attribution is similar to linear attribution, but it takes into account when these conversions took place (rather than splitting them up completely equally).
For example, if a user interacted with your company through an ad and then later on organic search before converting, this attribution model would show 25% for ads and 75% for organic (which was the most recent).
Advantages: One of the advantages of time-decay is that it takes into account when users converted - meaning you can see how many conversions are attributable to each channel from a single user.
This is useful for figuring out which channels are more effective at converting users who first clicked on your brand through paid ads, and then later interacted with you before coming back and engaging with you again.
Disadvantages: The biggest drawback of the time-decay attribution model is that it's a bit more complicated than the linear model (because it takes into account when conversions occur).
6) Position-Based Attribution
The position-based attribution model divides conversions based on where the user was in your conversion funnel when they converted.
For example, if a user came to your site through an ad and then later on organic search before converting, this attribution model would show 50% for ads and 50% for organic.
Advantages: One of the advantages of position-based attribution is that it accounts for when a user was in your conversion funnel - like if they were at a product page or their cart.
This means you can see what channels are better at converting users who engage with you after coming from an ad, and then later on experience something else before converting (like organic).
Disadvantages: The biggest drawback of the position-based attribution model is that it's not as straightforward to calculate because it takes into account where a user was in your conversion funnel when they converted.
Keeping Your Eye on the Data
One of the biggest problems marketers face with attribution models isn’t understanding the theory. Instead, it’s putting that theory into practice.
By that, I mean gathering this data won’t do you any good unless you use it to implement changes in your marketing strategy.
And for that, you should have your conversion data close at hand on a regular basis.
While you can always prepare that data manually, doing so is time-intensive, prone to human error, and expensive (remember, you still need to pay for someone’s time to compile that data).
That’s why I always recommend using a report building tool like Metrics Watch:
I built Metrics Watch to help marketers get the data they need to make the right changes for their (or their client’s) growth.
This tool allows you to build professional marketing reports in a matter of minutes, even if you have no technical skills at all.
That’s because you can use the drag and drop visual builder to create reports and pull data from your favorite sources, such as:
Google Analytics
Google Search Console
Google Ads
Facebook (paid and organic)
Instagram (paid and organic)
LinkedIn (paid and organic)
And more…
With that in mind, you can easily create reports involving your attribution models from Google Analytics.
Then you can have these reports compiled and sent automatically on a daily, weekly, or monthly basis.
But the best part is that these reports aren’t shared through PDF attachments or 3rd-party user dashboards.
The former requires tons of organization on behalf of you or your clients. And the latter requires user management roles like logins and password sharing.
With Metrics Watch, you simply send the data directly to your recipient’s inbox. That means your people get the information they need in a format they’re already familiar with.
And that’s all for today! We hope this article gave you a quick glimpse of the different types of attribution models.
If you enjoyed this post, you’ll definitely want to check out the following resources:
These articles will have even more information that you can use to create smarter data-driven marketing strategies that lead to actual growth.
Want to see Metrics Watch in action for yourself? Click below to start your 100% risk-free Metrics Watch trial today (no credit card required):
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