Mastering cross-channel attribution is something that most marketers strive for to fully understand and measure the impact of their marketing campaigns. However, it’s not always that simple, especially when you consider the wide range of channels, campaigns and tactics at play.
In this guide to cross-channel attribution, we explore the different types of attribution models and how to choose the right cross-channel attribution model for you, with practical examples throughout.
- Cross-channel marketing campaigns are the use of a wide variety of marketing channels to reach your target audience and promote products and services.
- Learn about the 3 main types of cross-channel attribution, which include multi-touch attribution (MTA), media mix modeling (MMM) and incrementality measurement.
- Each business has different goals and customer journeys, which are key things to consider when selecting the right attribution model for your business.
- Enhance the accuracy of your attribution efforts and benefit from data-backed decision-making by following 5 best practices for cross-channel attribution.
- Understand the challenges that surround cross-channel attribution models and how you might be able to overcome them.
What is Cross-Channel Marketing?
Cross-channel marketing is the practice of using different marketing channels to connect with audiences and promote products and services. The ability to use multiple marketing channels has always been a possibility, but now more than ever there are many ways to reach and engage with consumers online.
A good analogy to help visualize cross-channel marketing campaigns is to think of your overall marketing strategy as a puzzle with each piece of the puzzle representing a different channel.
Whether it’s organic social such as TikTok, Facebook, Instagram, LinkedIn and Pinterest, paid social, paid search, shopping, affiliates, SEO / organic search, email marketing, display or outreach, there are so many channels to choose from.
Marketers, more often than not, use a mix of marketing channels to reach a variety of audience segments all at different stages in their customer journey. As well as that, using multiple channels increases the ability to reach more people and creates a smoother experience for your customers.
Why should I use cross-channel attribution?
It’s important to consider using cross-channel attribution because of how users move between online platforms seamlessly. For example, a user might first click on a paid search ad. Then, they might get served paid social ads over the course of a week and finally, they click on a shopping ad and make a purchase. Or, perhaps they subscribe to a newsletter and later make a purchase after receiving a special offer in an email.
Cross-channel attribution takes into account all channels and touchpoints when measuring performance and assigning attribution, providing a better understanding of a user’s journey and how each channel impacts performance.
Getting this level of insight allows you to spend your marketing budget more wisely, focusing on the channels that bring the best results. It’s also becoming increasingly clear that channels don’t work in silos, so it’s important to distribute credit where credit is due.
What's the difference between Cross-channel Marketing and Multi-Channel Marketing?
People often use ‘cross-channel’ and ‘multi-channel’ marketing interchangeably, however, there is a distinction between the two.
Multi-channel marketing means being present on different platforms, but they often work independently. For example, when different teams in a marketing department execute campaigns, all with the same objective, but with little communication and cohesion between each channel.
Whereas cross-channel marketing, which is also known as omnichannel marketing, is customer-centric in nature and about integrating these channels to enhance customer experience. The customer journey should marry up across each platform and therefore, making sure the key message and tone of voice are consistent and that special offers are also consistent is vital.
The key to cross-channel marketing is to make sure all your channels work together, telling a consistent story instead of operating in isolation, so as to maximize the potential of each channel.
What is multi-channel attribution?
Multi-channel attribution assigns value to the multiple touchpoints that a customer interacts with before converting. As with multi-channel marketing, multi-channel attribution focuses on measuring the effectiveness of individual channels independently, whereas cross-channel attribution takes into account how different channels work together and influence one another in the customer journey.
How do you use cross-channel attribution?
There are several things marketers must do before they can start using cross-channel and multi-channel attribution models. Thankfully, with the help of technology and namely a reliable cross-channel analytics platform, it’s now simple to get started.
The first step is to track your website data, in particular website visitors. As well as that, track as many possible actions a user can take on your website, from scrolling, clicking on content, watching videos, to subscribing to newsletters, filling out forms and purchasing.
In GA4, actions a user can take on your website are known as ‘events’ and certain events that are the most valuable, can be marked as conversions.
2. Store data in one place
This data will also need to be stored in a single location, which is fundamental to cross-channel attribution. Each individual platform will likely provide you with their own analytics, however, they may all use different attribution models and have different definitions of metrics.
Pulling all data into one single platform and using that as the main source of truth will aid cross-channel attribution.
3. Sales and marketing tools
Finally, consider feeding your data into other sales and marketing tools, such as CRM tools and sales platforms. This can also help to connect actual sales data with user behavior on your website and it’s especially important in cases where offline channels are being used and when sales can happen offline.
Starting with these 3 techniques is the best way to use cross-channel attribution. It is important to note that there isn’t a perfect solution that’s going to provide 100% accuracy when it comes to attribution. This is a wider issue in digital marketing and boils down to challenges surrounding privacy, storing data, offline interactions and lack of communication between different channels.
Types of cross-channel attribution models + examples
The main cross-channel attribution models to consider adopting for the best attribution of your marketing efforts are the following: multi-touch attribution (MTA), media mix modeling (MMM) and incrementality measurement. Let’s explore each of these in detail and look at examples of their use.
Multi-touch attribution (MTA)
Multi-touch attribution, also known as MTA or a multi-channel attribution model, is a model that assigns a certain amount of value to each touchpoint in the customer journey. Unlike traditional single-touch models, MTA recognizes that customers often interact with multiple channels before making a decision and converting. Therefore, MTA aims to distribute credit among these touchpoints based on their influence on the conversion.
The move away from single-touch models like last-click and first-click attribution has been a large focus for attribution over the past 5 years.
The linear model is an example of multi-touch attribution, where equal credit is given to each touchpoint in the customer journey. This means that if a customer interacts with your organic social media, paid social, receives an email, and clicks on a shopping ad before visiting your website to make a purchase, each touchpoint gets an equal share of the attribution:
- Organic social - 25% credit
- Paid social - 25% credit
- Email - 25% credit
- Google Shopping - 25% credit
Another multi-touch attribution model that marketers could consider adopting is the ‘time decay’ model, which gives more credit to interactions that happened closer in time to a key event you are tracking, such as a purchase. Using the same example above, the time-decay model would assign credit in the following way:
- Organic social - 10% credit (first interaction)
- Paid social - 15% credit
- Email - 25% credit
- Google Shopping - 50% credit (last interaction before purchase)
Multi-touch attribution models aim to take into account more than one channel and instead consider the entire marketing mix a user is exposed to before taking action. This makes them more sophisticated than single-touch attribution models.
Media mix modeling (MMM)
Media mix modeling is in essence a cross-channel data-driven attribution model that assesses the impact of various marketing channels on overall performance. It goes one step further than simply splitting credit amongst channels based on a one-size-fits-all percentage, as we’ve discovered with multi-touch attribution. It involves analyzing historical data to understand the impact each channel has had on generating conversions.
For example, if a company runs display ads, social media, and affiliate campaigns simultaneously, media mix modeling helps determine the relative effectiveness of each channel. If data suggests that affiliates have a more significant impact on conversions, more attribution can be assigned to affiliates to reflect this.
Using data is central to this and it means that each conversion action can be analyzed individually before attribution is assigned. This also means that the emphasis should be on collecting as much data as possible and ensuring your data is high quality and accurate.
The main benefit of media mix modeling is the insight that can be gained, leading to optimizing the marketing strategy and budget of each channel. This is possible thanks to deeper insight into how each channel performs. As an example of this, the following table shows how the attribution of a linear model vs a data-driven model will impact decision-making:
Organic social media - 33.33%
Email marketing - 33.33%
Google shopping - 33.33%
Organic social media - 10%
Email marketing - 20%
Google shopping - 70%
Invest equally in all three marketing channels
Invest more heavily in Google Shopping, however still invest in organic social and email marketing
Finally, incrementality measurement is an alternative approach to cross-channel attribution that focuses on measuring the incremental impact of a specific marketing activity. Many native platforms offer this in some form, usually as a/b testing, and this is exactly what incrementality measurement is and it can be done on a single-channel basis or across multiple channels.
To use incrementality measurement, you must compare data between a group exposed to a particular marketing campaign with a control group that hasn’t been exposed. This way you can measure the impact of the marketing campaign on the group that was exposed to it.
For instance, if an e-commerce company launches a promotional email campaign, incrementality measurement would involve comparing the purchasing behavior of those who received the email against those who did not. This helps the company attribute the overall impact of the email campaign to how it changed user behavior.
This method exists because of the limitations of cross-channel attribution and the difficulty in achieving attribution that’s 100% accurate. Rather than trying to make sense of everything all at once, incrementality measurement is about taking a step back and reviewing one thing at a time in order to measure how it performs.
How do you choose the right cross-channel attribution model?
Choosing the right cross-channel attribution model is crucial for accurately understanding the impact of your marketing efforts, however, it’s not always an easy question to answer. There isn’t necessarily a right or wrong answer either.
Each business has different goals and customer journeys, along with using its unique set of platforms and running specific campaigns. This is what first needs to be considered when choosing the right cross-channel attribution model. It must align with your goals and data.
If your customers typically interact with lots of different touch points before converting, a multi-touch attribution model is the most suitable approach, because it’ll allow you to distribute credit across the customer journey. On the other hand, to get even more insight into the overall contribution of each marketing channel, media mix modeling could be an even more optimal fit providing you collect a sufficient amount of customer data.
Incrementality measurement is valuable when you want to isolate and measure the specific impact of a particular marketing campaign and this model can be used alongside other models. It doesn’t have to be a stand-alone model.
This brings us to another key consideration and that’s to be flexible and open to experimentation. Having a view of different models and refining your marketing by taking into account each model may be the most sensible way to handle attribution. The main thing is to interpret the data you have in the best way possible to inform your strategy and results.
5 Cross-Channel Attribution Best Practices
Follow these 5 cross-channel attribution best practices to enhance the accuracy of your attribution efforts and to achieve informed decision-making.
1. Clearly define goals
The first best practice is to clearly outline your marketing goals and KPIs before implementing a cross-channel attribution model. Having well-defined objectives helps align your attribution strategy with your overall business objectives and it means you can build your attribution around the goals that matter the most.
For example, let’s say your primary goal is to generate online sales and your secondary goals are to grow website traffic and the email database. With these in mind, your cross-channel attribution strategy should be able to measure these goals from each marketing channel and provide you with insight into how they perform.
2. Understand customer journey
It’s important to have a deep understanding of your customer journey to identify key touchpoints and interactions that may take place. Whether it’s ongoing email campaigns, website content, SMS, or paid advertising, try to map out the customer journey so the path to purchase is clear.
Recognizing all of the channels that users engage with before converting will enable you to tailor your attribution model to reflect this.
3. Use a mix of attribution models
Combine different attribution models to get a more comprehensive view of your marketing effectiveness. As mentioned previously, it’s good practice to experiment with different models and interpret data in different ways, which is a best practice.
For example, if it’s not immediately clear which model is the best fit or if the customer journey is complex, review performance using multi-touch attribution models alongside media mix modeling and compare. Take into account multiple scenarios to build a clearer picture of performance.
4. Regularly review your attribution
Periodically review and adjust your attribution model based on changes in customer behavior, marketing channels, and industry trends. This could be using the data in your cross-channel marketing dashboard, or by reviewing all platforms themselves and it’s particularly important now that the tracking and attribution landscape is changing so rapidly.
There’s no longer a need to settle for one attribution model and stick to it in the long run. Instead, regular audits ensure that your model remains relevant and aligned with evolving business needs.
5. Integrate data sources
Last but certainly not least, integrate data from different sources to create a unified and comprehensive dataset. This could include CRM data, cross-channel analytics, and other relevant sources to enhance the accuracy and depth of your cross-channel attribution insights.
In cases where you want to use media mix modeling, this is especially important since the need for robust data is the key.
Ecommerce advertisers should consider integrating a shopping feed ad analytics platform as well, to complement other ad analytics platforms. Feed Analytics has been designed to uncover more insight from data, which can then lead to more accurate attribution as well as better informed decision making and optimization.
Here are the main ways Feed Analytics can enhance data analysis and attribution:
- Compare ad platform data with GA4 data, amongst other analytics platforms, to get a clearer picture of performance.
- Accurately allocate all revenue from your shopping ad campaigns, which will then lead to a better understanding of ROAS from your marketing channels. The screenshot below demonstrates how Feed Analytics accurately reports on ROAS thanks to its ability to categorize revenue and clean data.
- Anticipate the point of diminishing returns to inform key decision making around how much budget should be spent on various channels to maintain your target ROAS or profit, or how much more volume could be generated and at what cost to ROAS.
- More insight from data analysis and attribution leads to optimization of your marketing budget across your media mix. This is demonstrated below and this example shows how for one business, they should consider increasing Facebook spend and reducing Google Ads spend in order to increase ROAS by 13.5%.
What are the challenges and limitations of cross-channel attribution models?
Some of the challenges that surround cross-channel attribution models have already been referenced in this article. They are similar to the challenges faced in managing multiple e-commerce channels in general.
Here are some of the main challenges that marketers are faced with:
Diverse mix of channels
One of the main challenges in implementing a cross-channel attribution model is the sheer diversity of marketing channels and touchpoints available. There are simply so many channels used by businesses, and users are exposed to brands in so many ways, often daily, making it hard to fully understand the entire customer journey, especially when you factor in each channel having its own set of metrics and data. The lack of communication between each channel, for example between Meta and Google, hinders this even more.
Data and privacy
Another hurdle lies in the issues surrounding data capture and user privacy. There’s a need for companies to collect and analyze data to refine their attribution models, however, there's also an increasing need to comply with user privacy and data laws. Being responsible with data and collecting it in accordance with compliance, such as GDPR, means the accuracy and completeness of data are often impacted.
Lack of one single tool
The absence of a single, coherent tool that seamlessly integrates cross-channel attribution exacerbates the challenges faced by marketers. While there are various analytics and attribution tools available, there’s a real lack of a universally accepted standard tool that can effectively handle data from all sources.
These are the main reasons why attribution platforms and models are flawed, which explains the need to experiment with different models and for companies to use the data and insight they have at hand when it comes to attribution.
How to analyze the success of a cross-channel attribution?
The best way to analyze the success of cross-channel attribution is firstly by the level of insight and informed decision-making that directly results from the attribution model.
If an attribution model can provide you with clear insights that are logical and make sense of performance, then it’s a success. If the insights are not very clear, they come with many caveats or they contradict other data points, then perhaps attribution could do with some work. Practically speaking, if Google Ads claims all conversions, however, your cross-channel attribution model assigns very little to Google Ads, then there may be an issue with the data that’s being captured on either side that would need to be resolved.
Another way to analyze the success of cross-channel attribution is its ability to mitigate all of the challenges and limitations that are associated with attribution: the diverse mix of channels, data and privacy and the lack of one single tool. If you feel your attribution can overcome these issues, then that’s an indication that it’s a success.
Although it feels like an uphill battle at times, mastering cross-channel attribution is more crucial than ever. It isn’t just about knowing how a diverse mix of channels and touchpoints contribute to performance, it’s how that level of insight can inform your marketing strategy and help you to allocate your budget most effectively.
With so many marketing channels to choose from, it can be hard to know what the best channels are for your e-commerce business, which is another thing that attribution can help with.
For those interested in e-commerce marketing, discover our Multichannel Marketing Report 2023 to understand the strategies employed by fellow retailers in your sector to gauge your position and uncover opportunities for refining your advertising approach.