Marketing Attribution 101 | What is Marketing Attribution

Marketing attribution is the process of assigning credit to specific marketing channels or campaigns for the conversion of a sale or other desired action.

In the digital marketing world, attribution is important because it helps marketers understand which channels are most effective at driving conversions, so they can allocate their marketing budget and resources more effectively.

Attribution can help marketers identify areas of their campaigns that may be underperforming, so they can make adjustments to improve their results. By understanding which channels are most effective at driving conversions, marketers can make more informed decisions about where to allocate their resources, which can help them drive more sales and improve the ROI of their marketing efforts.

Also, read: Data driven marketing

Marketing Attribution Models

Attribution modeling is a statistical method used to determine how much credit to assign to different marketing channels and touchpoints in the customer journey for a sale. The goal of attribution modeling is to understand the most effective marketing channels and tactics for investing marketing resources and improving marketing performance measurement accuracy.

There are several different approaches to attribution modeling, including last-click attribution, first-click attribution, linear attribution, and time decay attribution. The choice of attribution model can have a significant impact on the analysis and optimization of marketing campaigns.

Here are 12 Attribution Models that Businesses and Marketers can Consider

1. Single source attribution: This model attributes all credit for a conversion to a single marketing channel.
2. Last click attribution: This model attributes all credit for a conversion to the last marketing channel the customer interacted with before converting.
3. First-click attribution: This model attributes all credit for a conversion to the first marketing channel the customer interacted with.
4. Linear attribution: This model evenly distributes credit for conversion across all the marketing channels a customer interacts with.
5. Time decay attribution: This model attributes more credit for a conversion to marketing channels closer to the conversion.
6. Position-based attribution: This model attributes a set percentage of credit for a conversion to the first and last marketing channels the customer interacted with, and distributes the remaining credit among the other channels the customer interacted with.
7. Data-driven attribution: This model uses machine learning algorithms to analyze customer data and determine how much credit to attribute to each marketing channel based on its relative influence on the conversion.
8. Custom attribution model: This model allows businesses to create their own attribution model based on their specific needs and goals.
9. Multi-touch attribution: This model attributes credit for a conversion to every touchpoint that a customer had with the business, rather than just a single touchpoint.
10. Rule-based attribution: This model allows businesses to define rules to determine how credit for a conversion is attributed to different marketing channels.
11. Last non-direct click attribution: This model attributes credit for a conversion to the last marketing channel the customer interacted with, excluding direct traffic (e.g., traffic from the customer typing in the URL directly or from a bookmark).
12. Last AdWords click attribution: Similar to last click attribution, this model attributes credit for a conversion to the last AdWords ad the customer clicked on before converting.

For few of the most common marketing attribution models, we will go into bit more depth below.

 

What is First-click Attribution Model

As the name implies, first-click attribution assigns the conversion’s credit to the first point of contact or touchpoint a consumer has with a brand.

For example, a customer visits your site by clicking one of the Facebook ads. Then, a couple of days later, the same customer returns to your site as she comes across your Google ad. The customer revisits your sites for the third time listening to your podcast. Then, she directly makes a purchase after some hours.

As per the first-click attribution model, the entire sale/conversion credit goes to the first touchpoint, i.e., the Facebook ad.

• First-click attribution is limited in scope since it only considers the initial point of contact with a consumer.
• First-click attribution’s key advantage is that it’s easy to quantify and analyze. However, it doesn’t provide a comprehensive view of the customer’s journey or the interplay between the many touch points that affect the likelihood of a sale.

What is Last-Click Attribution Model

Last Click attribution assigns credit for a conversion to the customer’s last touchpoint, as opposed to the first-click attribution method.

To continue with the previous example, the brand’s landing page that the customer converted from would receive 100% credit for the sale/conversion.

A significant perk of using last-click attribution is that it records the customer’s whole journey and the interplay between several touch points that led up to the conversion. Measuring it, however, might be trickier since it also keeps tabs on information from various sources.

What is Last Non-Direct Click Attribution

The last Non-Direct Click Attribution model ignores all the direct traffic coming to your website, and 100% of the credit goes to the last channel the customer interacted with before converting.

For example, a customer visits your site by clicking one of the Facebook ads, interacts with your Google ad, listens to your podcast, and makes a purchase. 100% credit for the sale/conversion will go to the podcast, as it is the last channel the customer interacts with before making a sale.

One significant advantage of last non-direct click attribution is that it properly attributes conversion credit to the last touchpoint before the user converted.

However, it disregards other factors, such as direct traffic, that might have affected the conversion, which is a potential conversion source.

What is Multi-touch Attribution

Multi-touch attribution is a method of measuring the effectiveness of different marketing channels by assigning credit for a conversion to all the touchpoints (or interactions) that a customer has with a brand before making a purchase. It is used to understand how different channels and campaigns contribute to the overall customer journey and can help identify the most effective channels in driving conversions.

Continuing with the previous example, if a customer sees an ad on Facebook, clicks on it, interacts with the brand multiple times, and then makes a purchase on the brand’s website, both Facebook and the brand’s landing page would receive credit for the sale.

An improved understanding of the customer’s journey and the interplay between several touch points that lead to a conversion is the primary value proposition of multi-touch attribution. On the other hand, it can be harder to grasp and track than the first-click attribution model as it emphasizes different parts of the customer journey.

Now that we’ve established that “multi-touch” refers to a set of distinct attribution models, let’s take a closer look at different types of Multi-touch Attribution:

1. Linear Attribution Model
2. Time-decay Attribution Model
3. Position-based Attribution Model

What is Linear Attribution Model

The linear attribution model provides equal weight to each interaction/touchpoint along the customer’s path.
Focusing on the example given earlier, the Facebook ad, Google ad, podcast, and your website would share equal credits for a successful sale/conversion.

Linear attribution is advantageous since it is straightforward to analyze and quantify. However, it does not provide a comprehensive view of the customer’s journey or the interplay between various contact points and their impact on conversions.

On the other hand, linear attribution recognizes the importance of all client interactions rather than just the first touchpoint.

What is Time-Decay Attribution Model

Time-decay attribution model gives most of the credit to the touchpoint closest to the time of sale/conversion. This model also assists you in learning how different points in the customer journey contributed to the transaction, much like Linear attribution.

In this particular example, the brand’s website and podcast will get most of the credits as the customer interacts with these channels within a few hours of the conversion. The Google ad would receive fewer credits than the podcast and the website. Since the interaction with the Facebook ad was the first touchpoint, it would receive significantly less credit for the sale/conversion.

In contrast to Linear attribution’s equal emphasis on each touchpoint, time decay largely attributes success to the most recent interaction a customer has had with your brand. This demonstrates which campaigns throughout time contributed to the sale and enables identification of the campaign that ultimately influenced the buyer to make a purchase.

What is Position Based Attribution or U-Shaped Attribution Model

In position-based attribution, the first and last touchpoints are assigned 40% credit each. Meanwhile, the remaining 20% credit is distributed across all other channels equally.

Focusing on our example, the Facebook ad and the brand’s website would each 40% credit for the sale/conversion, while the Google ad and the podcast receive 10% of the credit each.

The position-based attribution models are more complex as they assign value to numerous touch points throughout the customer journey. This eCommerce marketing attribution model has an advantage over other attribution models because it considers all the different customer touchpoints that can lead to a sale/conversion.

How to Choose the Right Attribution Model for your Business

There are several factors to consider when choosing an attribution model for your business. Here are 11 tips to help you choose the right attribution model for your business:

1. Define your goals: What are you trying to achieve with your attribution model? Do you want to understand which channels and campaigns drive the most revenue or conversions? Do you want to optimize your marketing efforts based on the customer journey? Do you want to assess the top-of-the-funnel or bottom-of-the-funnel? Having clear goals will help you choose the right attribution model for your business.
2. Consider the customer journey: How do customers interact with your business? Do they typically make a purchase after a single touchpoint, or do they require multiple interactions before converting? An attribution model that considers the customer journey map can provide a more accurate picture of how different marketing efforts contribute to conversions.
3. Evaluate your data: Do you have access to data from all the channels and touchpoints that customers use to interact with your business? If not, you may need to choose an attribution model that doesn’t require data from every channel.
4. Consider your business model: Different attribution models may be suitable for different business models. For example, if you have a subscription-based business, you may want to use an attribution model that gives credit to all the touchpoints that contributed to a customer’s initial sign-up, rather than just the last touchpoint before the conversion.
5. Think about your marketing mix: Different attribution models may give different weights to different channels or touchpoints. Consider which channels and touchpoints are most important to your business, and choose an attribution model that reflects that.
6. Keep it simple: More complex attribution models may provide more detailed insights, but they may also be harder to set up and interpret. Consider whether a simpler model would be sufficient for your needs.
7. Don’t rely on a single model: No attribution model is perfect, and different models may give different results. Using multiple models and comparing the results is a good idea to get a more complete picture of how your marketing efforts contribute to conversions.
8. Consider the attribution window: Different attribution models use different time frames, or “attribution windows,” to attribute conversions to marketing efforts. For example, a “last touch” model attributes a conversion to the last marketing touchpoint before the conversion occurred, while a “first touch” model attributes the conversion to the first touchpoint. Consider which time frame makes the most sense for your business.
9. Look at industry benchmarks: It can be helpful to see how other industry businesses use attribution models. You can use industry benchmarks as a starting point but remember that every business is different and what works for one company may not work for another.
10. Test and compare: It’s a good idea to test multiple attribution models to see which one works best for your business. You can compare different models’ results to see which provides the most accurate and useful insights.
11. Seek expert advice: If you’re unsure which attribution model is right for your business, consider seeking advice from an expert in the field.

Examples of How Different Attribution Models can be Applied to Specific Scenarios

1. A business that sells clothing online is running a digital marketing campaign to promote its new fall collection. The campaign includes ads on Google, Facebook, and Instagram and a retargeting campaign that displays ads to people who have visited the business’s website but haven’t made a purchase. The business wants to understand which channels and tactics are most effective at driving sales.
   To do this, the business could use an attribution model to attribute sales of the new collection to the various touchpoints in the customer journey. For example, they could use a “linear” attribution model, which attributes equal credit to each touchpoint in the customer journey. This model could show the business that all three channels (Google, Facebook, and Instagram) as well as the retargeting campaign contributed to sales of the new collection.
2. A business that sells home security systems is running a digital marketing campaign to promote their services. The campaign includes ads on Google and Facebook and a direct mail campaign. The business wants to understand which channels and tactics are most effective at driving leads.
   To do this, the business could use an attribution model to attribute leads to the various touchpoints in the customer journey. For example, they could use a “time decay” attribution model, which attributes more credit to touchpoints closer to the conversion. This model could show the business that most leads came from customers who saw an ad on Google, followed by customers who saw an ad on Facebook. It could also show that the direct mail campaign was less effective at driving leads.
3. A business that sells software is running a digital marketing campaign to promote its new product. The campaign includes ads on Google, Facebook, and LinkedIn, as well as a webinar that provides more information about the product. The business wants to understand which channels and tactics are most effective at driving sales.
   To do this, the business could use an attribution model to attribute sales of the new product to the various touchpoints in the customer journey. For example, they could use a “position-based” attribution model, which attributes more credit to the customer journey’s first and last touchpoints and less to touchpoints in the middle. This model could show the business that most sales came from customers who saw an ad on Google or LinkedIn, followed by customers who attended the webinar. It would also show that the ads on Facebook were less effective at driving sales.

Also, read:

1. Customer Lifetime Value
2. Customer Acquisition Cost
3. Average Order Value
4. Conversion Rate Optimization

Using GA4 and Google Adwords for Marketing Attribution

Both Google Analytics 4 (GA4) and Google Ads offer attribution modeling features that allow you to attribute conversions to the touchpoints in the customer journey.

GA4 is a web analytics tool that provides insights into how people use your website and how it contributes to your business goals. It includes an attribution modeling feature that allows you to attribute conversions to different touchpoints in the customer journey. You can use GA4 to understand the role that different channels, campaigns, and tactics play in driving conversions, and use this information to optimize your marketing efforts.

Google Ads is a pay-per-click (PPC) advertising platform that allows you to create and run ads on Google search and other websites. It also includes an attribution modeling feature that allows you to attribute conversions to different touchpoints in the customer journey. You can use Google Ads to understand which channels, campaigns, and tactics are most effective at driving conversions, and use this information to optimize your PPC efforts.

In general, GA4 is a good choice if you want to understand the overall performance of your website and marketing efforts, while Google Ads is a good choice if you want to focus specifically on PPC campaigns. However, both tools can be useful for attribution modeling, and you may want to consider using both to get a complete picture of your marketing efforts.

Setup Attribution Modeling in GA4

Here’s a step-by-step guide to setting up attribution modeling in GA4:

1. Sign in to your GA4 account and click on the “Conversions” tab in the left-hand menu.
2. On the “Conversions” page, click on the “Attribution” tab.
3. On the “Attribution” page, click the “Create model” button.
4. On the “Create model” page, choose the type of model you want to create. GA4 offers several types of attribution models, including Last Interaction, First Interaction, Linear, Time Decay, and Position Based. Read the descriptions of each model to choose the one that best fits your goals.
5. Once you have chosen a model, give your model a name and click the “Create” button.
6. On the “Attribution” page, you can view and compare the results of different attribution models. Use this information to inform your PPC strategy and optimize your campaigns.

Attribution Modeling for PPC

Attribution modeling is particularly important for PPC campaigns because budget depletion is a major concern with any paid marketing strategy.

Most PPC platforms, such as Google Ads, have built-in attribution modeling tools that allow you to set up and apply an attribution model to your campaigns. Follow the information given earlier about the steps that are needed to implement marketing attribution properly.

Few Best Practices Include

• Use a multi-touch attribution model
• Consider the customer journey: A customer may have first become aware of a product through a display ad, but then research the product more through organic search before ultimately converting through a paid search ad. By considering the customer journey, you can better understand the role that different touchpoints play in the conversion process and make more informed decisions about how to allocate your advertising budget.
• Use UTM parameters: UTM parameters are tags that can be added to the end of a URL to track the source, medium, and campaign of a referral. By using UTM parameters, you can track the performance of different campaigns and ads and attribute conversions to specific touchpoints.
• Test and refine

Setup Attribution Modeling in Google Ads

1. Sign in to your Google Ads account and click the “Tools” icon in the top right corner.
2. In the “Measurement” section, click on “Attribution.”
3. On the “Attribution” page, click on the “Models” tab.
4. Click the “Create model” button.
5. On the “Create model” page, choose the type of model you want to create. Google Ads offers several types of attribution models, including Last Click, First Click, Linear, Time Decay, and Position Based. Read the descriptions of each model to choose the one that best fits your goals.
6. Once you have chosen a model, give your model a name and click the “Create” button.
7. On the “Models” page, you can view and compare the results of different attribution models. Use this information to inform your PPC strategy and optimize your campaigns.

Future of Marketing Attribution

With the updates to cookie policies and increased privacy concerns, it is becoming more challenging for marketers to attribute conversions to specific channels and tactics accurately.

One solution that has gained popularity in recent years is using first-party data. This refers to data collected directly from the brand’s properties, such as its website or mobile app. By collecting and analyzing first-party data, brands can get a more accurate picture of their marketing efforts driving conversions.

Some of the latest developments and trends in the field of marketing attribution include the following:

1. Adoption of machine learning and artificial intelligence (AI) technologies to improve attribution accuracy and efficiency.
2. Utilization of real-time data to obtain more precise insights into consumer behavior.
3. Increased focus on cross-channel attribution for a more comprehensive view of the customer journey.
4. Use of granular data to gain insights into consumer preferences and create more personalized campaigns.

Predictions for the future of marketing attribution include the continued growth of AI and machine learning, the emergence of more sophisticated attribution models, and the increased use of customer data to create increasingly targeted campaigns.

The potential impact of these developments on businesses and marketers is that they will be able to create more effective and efficient campaigns that are tailored to individual customers, resulting in improved ROI.

Also read:

1. Customer Segmentation

Challenges in Marketing Attribution

Several data and analytics challenges can arise when it comes to proper marketing attribution. These include:

1. Lack of data: To properly attribute marketing efforts, it is necessary to have access to a wide range of data from different sources. However, this data may not always be available, or it may be incomplete or inaccurate.
2. Data silos: Another challenge is that data may be siloed within different departments or systems, making it difficult to get a comprehensive view of the customer journey and attribute marketing efforts accordingly.
3. Complex customer journeys: Customers often interact with brands in complex ways, using a variety of channels and touchpoints before making a purchase. Attributing the correct marketing efforts to a particular sale or desired outcome can make it difficult.
4. Difficulty in measuring offline marketing: It can be challenging to measure the effectiveness of offline marketing efforts, such as TV or print ads, and attribute them to specific outcomes.
5. Attribution modeling: Attribution modeling determines how much credit to assign to each touchpoint in the customer journey. There are many different attribution models to choose from, each with its own strengths and limitations. It can be difficult to determine which model is the most appropriate for a particular situation.

To overcome these challenges, it is important to have a robust analytics infrastructure in place and to plan and execute marketing attribution efforts carefully. This may involve using advanced technologies such as machine learning and AI to automate and optimize attribution models and working with cross-functional teams to ensure that data is shared and used effectively.

 

Why do Point Solutions for Marketing Attribution fail

Point solutions for marketing attribution are specialized tools designed to track and attribute the performance of a specific channel or tactic, such as email marketing or social media advertising. While these tools can be useful for understanding the performance of individual channels, they can fail to provide a comprehensive view of the customer journey and the overall effectiveness of a company’s marketing efforts.

One reason that point solutions for marketing attribution can fail is that they are not integrated with the rest of the marketing stack. This means that they may not have access to data from other channels or touchpoints, making it difficult to get a complete picture of the customer journey.

Another reason is that point solutions often use proprietary algorithms and data models for attribution, which can be difficult to compare and integrate with other tools and systems. This can make it challenging for businesses to get a consistent and accurate view of their marketing performance.

Finally, point solutions for marketing attribution may not be able to handle the complexity and scale of modern marketing campaigns, which often involve multiple channels and touchpoints across different devices and platforms. In these cases, a more comprehensive and integrated approach to marketing attribution may be necessary.

Conclusion

Marketing attribution is particularly important in times of economic downturn because it allows businesses to allocate their marketing resources more efficiently and effectively. By accurately attributing the success of their marketing efforts to specific channels and tactics, businesses can identify which strategies are working well and allocate their budgets accordingly.

Proper implementation of Google Analytics 4 (GA4) and a data warehouse can be the foundation for effective marketing attribution. GA4 provides a wide range of tools and features for tracking and analyzing customer behavior, and a data warehouse allows businesses to store, manage, and analyze large amounts of data from multiple sources.

Saras Analytics offers services for GA4 implementation, data warehouse setup, and end-to-end automated reporting dashboards, which can help businesses attribute successes and allocate resources to their most profitable cohorts.