How to Build a Custom Marketing Attribution Model
by Frankie Karrer
6 Min Read
8 Min Read
With digital ad spend predicted to surpass $600 billion by the end of 2023, accurately measuring your results is more important than ever. You’re likely allocating your marketing dollars across multiple channels to create various touchpoints for your potential customers.
Cross-device attribution helps you assess how potential customers are seeing your ads so you can break up your budget accordingly. It’s important to understand cross-device attribution (and its limitations) to better measure your marketing performance and drive meaningful results.
Cross-device attribution is a process used in marketing analytics to track and attribute customer interactions and conversions to the appropriate marketing touchpoints across multiple devices. This allows marketers to understand how different devices (such as smartphones, tablets, and desktop computers) contribute to the customer journey and ultimately lead to conversions or sales.
Marketing’s “Rule of 7” indicates that a potential customer needs to see your ads seven times before they buy. The actual number may be more or less, but the fact is that potential customers should be seeing your ads on multiple devices.
They may come across a sponsored post while scrolling social media on their phone and then see a Connected TV (CTV) ad when streaming their favorite show. Cross-device attribution helps you determine which touchpoint was responsible for the conversion.
Your customers are unlikely to interact with your brand on just a single device. It’s tempting to give credit for every pay-per-click search to someone on a laptop or desktop computer or attribute every sponsored social media post to a mobile device. But doing so will over-simplify the customer journey and likely leave you with unanswered questions (not to mention inaccurate data).
Single-device attribution can’t give you the full picture of how your customers are finding you online. You may be neglecting some of your most successful platforms because you’re not aware of how users of these devices are converting.
While cross-device attribution offers a more accurate picture of how your customers are finding you online, it still has its challenges.
First, a user needs to be logged into their device or their various platforms for you to track them accurately.
Additionally, some customers may interact with your brand using multiple devices throughout their buyer’s journey. This is more common when a potential customer is making a big buying decision.
For example, a person booking a vacation may search for hotels using different apps on their phone, and then actually book the room from their work computer. In this case, you could attribute the sale to either device, but you might not be crediting the right one.
Even with its challenges, there are plenty more benefits to using a cross-device attribution model over the single-device option.
Cross-device attribution allows you to better analyze your marketing efforts to see which are most successful. It also gives you a clearer picture of a potential customer’s conversion path, including how they first found you and what factors may have motivated the sale.
You can use cross-device attribution data to optimize your entire performance marketing strategy. You can target audiences based on where they are most likely to find you, setting realistic budgets and optimizing your ads for different types of devices.
Cross-device attribution also helps you avoid placing repetitive ads on multiple platforms. You can create memorable ads for each touchpoint, ads that complement one another without being redundant. This helps avoid the ad fatigue and ad blindness that can sabotage your ability to convert a potential customer.
Follow these steps to apply cross-device attribution to your marketing efforts and get a better sense of your customers’ journeys.
First, you need to collect data on your various marketing campaigns. You can use tags, data management platforms, marketing automation systems, analytics, and other tools to see how and where people are finding you. You might look at different platforms and compare email addresses or IP addresses to see who might be logging into multiple devices.
Data management platforms and other types of marketing software make it easier to collect this information. These platforms collect data from online and offline sources, allowing you to track and segment your audience.
After collecting data, you can start modeling how people are interacting with your brand through multiple devices. You should get a reasonable sense of which platforms are serving as the first touchpoint and which are serving as the last.
During the data collection phase, you might be able to see which members of your email list or which social media followers are logging in with other devices to navigate to your website. Making these connections will help you design ads that speak to customers based on where they are in their journey.
Once you have your data and you’ve identified potential patterns with device usage, you can choose which attribution models are most applicable. These models represent ways one can credit various devices for a sale. Pick the model that best matches your marketing goals and overall business strategy.
The following cross-device attribution models are used most often for measuring the return on investment (ROI) of ad campaigns based on the device a customer used to find your business. Each model organizes the data you’ve collected about whether, where, and when a person is using multiple devices.
The deterministic attribution model involves matching users across platforms by using unique identifiers. Because many people use their email addresses to log into various devices and apps, email addresses are a common unique identifier, but you may also use different types of data, such as user IDs or IP addresses.
Deterministic attribution assigns full credit for a conversion to one touchpoint, such as the first or the last interaction with your brand. This model is simple to execute, but it doesn’t consider the full customer journey when giving credit for a conversion. It also doesn’t consider users who aren’t logged in on their devices but are still using multiple devices to interact with your brand. If a potential customer is using a web browser anonymously, you wouldn’t be able to match that session data with the same person when they see your social media ads on their phone.
You might use this attribution model when you don’t have much customer data or when you want to measure specific key performance indicators such as click-through rates or conversions.
Probabilistic matching is more complex than deterministic matching. Instead of looking at data such as email addresses to match users across devices, you would compare multiple field values and use an algorithm to determine the probability that a user is logged in on multiple devices.
Probabilistic attribution assumes that people use multiple touchpoints across devices to find you online. This model is similar to multi-touch attribution models, in that you allocate credit for a sale or conversion to multiple devices. While this method is more accurate, it also requires more data and can be more difficult to execute.
The hybrid attribution model allows you to combine elements of both models to get a clear picture of a customer’s overall journey. You get concrete data that comes from deterministic matching while filling in the gaps with probabilistic matching. With this attribution model, you assign credit based on user actions.
For example, if you’re running a SaaS company, you may use deterministic matching to learn that a user clicked on your LinkedIn ad and later visited your website from their phone to download your app. However, they might indicate that they heard about your brand on a podcast when they sign up.
With the hybrid model, you’d allocate equal credit to the LinkedIn ad and the podcast ad. This attribution model offers more accurate information than the deterministic model, and it’s easier to use than the probabilistic model.
Connected TV is a relatively new digital ad channel in which you reach potential customers on smart TVs or through over-the-top ads on mobile streaming platforms. CTV platforms allow you to reach millions of viewers, and unlike with traditional television advertising, you can target your CTV ads to potential customers who fall within your ideal customer demographics (rather than just blasting your ad wide and hoping the right people see it).
However, since customers don’t click through a TV ad, it’s often been difficult to measure the ROI on these ad campaigns. Although you need to account for these ads as part of your customer’s journey, you might not have the full picture.
MNTN has created a solution to this problem with Verified Visits. This technology allows you to validate organic site visits that are driven by your ads. It works as follows:
Verified Visits gives CTV advertisers more accurate representation of how their CTV ads are driving traffic to their site, without double-counting your other efforts.
Start tracking your CTV ROI now. Schedule a demo.
A customer interacts with your brand in multiple ways across various devices. Rarely does a person make a decision to buy something after viewing just one ad. Your customers are likely seeing your ads in different places and interacting with them in myriad ways.
Cross-device attribution gives you the ability to record these touchpoints and give each one proper credit. This attribution model highlights the customer journey, allowing you to better understand all the ways potential customers find you. With it, you can better allocate your ad spend, creating ads that resonate with customers based on where they are in their journey.
Tell stories that drive customers from one touchpoint to another or mix up your ad campaigns to keep viewers interested. Most of all, use this valuable data to launch successful campaigns.
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