The CMO’s Guide to Multi-Channel Attribution in 2025
The customer journey is more complex and fragmented than ever before. Prospects interact with your brand across many channels, from social media and search engines to email, display ads, and even offline touchpoints.
For a Chief Marketing Officer (CMO), accurately understanding which of these interactions truly influence a sale or conversion could be the catalyst to making some big changes. The way to understand the value of digital interactions is through multi-channel attribution.
98% of marketers consider attribution important for the success of their overall marketing strategy, and for good reason. It can help you know what’s working, why, and how well.
In this blog, we’ll break down how multi-channel attribution models work, what’s changed, and how CMOs can use this data to make smarter, faster marketing decisions.
What is Multi-Channel Attribution?
Multi-channel attribution is the process of identifying and assigning value to every touchpoint a customer interacts with before converting. Whether it’s a LinkedIn ad, a Google Search click, a product demo, or a webinar, each interaction plays a role in the decision-making process.
Unlike single-touch models that give all credit to either the first or last interaction, multi-channel attribution gives you a more complete picture. It helps answer a critical question: Which combination of channels actually drove this conversion, and in what order?
For CMOs, this isn’t just about data. It’s about making smarter budget decisions, aligning teams around what actually moves the needle, and turning fragmented customer paths into measurable journeys.
Benefits of Multi-Channel Attribution
Multi-channel attribution provides a detailed view of how various marketing channels contribute to conversions. For a CMO, this information can be a godsend. How?
For startups, multi-channel attribution offers accurate insights into which marketing efforts are most effective. Empowered with this knowledge, as a CMO, you can allocate budgets more efficiently and focus on channels that drive the most value.
This is good news, considering marketing budgets are tightening across many organizations. According to the 2024 Gartner CMO Spend Survey, the marketing budget dropped 15% from the previous year (7.7% of total company revenue).
So, CMOs are essentially being asked to do more with less. That’s where multi-channel attribution comes in handy. It can significantly improve return on investment (ROI).
By identifying the channels that contribute most to conversions, marketing leadership can invest more in high-performing areas and reduce spending on less effective ones.
“Without understanding how campaigns influence each other, you might shut down effective campaigns or over-invest in ones that don’t drive conversions.” Ryan Koonce, CEO of Attribution
Multi-channel attribution helps you understand the true contribution of each channel, so you can allocate resources more effectively and double down on what’s actually driving results.
For example, a customer might first discover a product through a social media ad, later read a blog post about it, and finally make a purchase after receiving an email promotion. Multi-channel attribution models analyze these interactions to determine how much each channel contributed to the final conversion.
Simply put, multi-channel attribution allows for data-driven decisions that improve overall marketing performance while optimizing resource usage.
Understanding the benefits is just the start. To make attribution work at scale, CMOs must pick the model that aligns with their strategy and customer journey.
Types of Multi-Channel Attribution Models
Different multi-channel attribution models differ by how the credit for conversion is attributed across the customer journey and for various touchpoints.
Let’s discuss the most commonly used models:
1. Linear Attribution Model
The linear attribution model assigns equal credit to all touchpoints in the customer journey. This approach is beneficial when each interaction is considered equally influential in driving conversions.
For instance, if a customer interacts with a social media ad, reads a blog post, and clicks on an email link before converting, each channel receives equal credit. This model is straightforward and provides a balanced view of marketing efforts, but it’s not the most detailed.
2. Time Decay Attribution Model
The time decay model gives more credit to touchpoints that occur closer to the conversion event. This model is based on the idea that recent interactions are more influential in the decision-making process.
For example, if a customer initially discovers a product through a search ad but purchases after receiving a promotional email, the email receives more credit.
This model is particularly useful for campaigns with longer sales cycles, which is typically the case with B2B marketing. In one survey, 41% of marketers said the time decay or last-touch method was their go-to model for attribution.
Pro Tip: Collaborate with a B2B marketing agency to select the best attribution model and streamline the process with the help of professionals.
3. U-Shaped Attribution Model (First and Last Interaction)
The U-shaped model assigns significant credit to the first and last touchpoints, typically 40% each, with the remaining 20% distributed among the middle interactions.
If a customer first interacts with a Facebook ad, watches a video on the brand’s YouTube, and then finally buys through a Google ad, 40% of the credit would go to the Facebook ad, 40% to the Google ad, and the remaining 20% to YouTube.
This model emphasizes the importance of initial engagement and final conversion actions. It's suitable for understanding how early awareness and closing tactics contribute to conversions.
4. W-shaped Attribution Model
The W-shaped model extends the U-shaped approach by assigning credit to three key touchpoints: the first interaction, lead conversion, and final conversion.
Each of these receives 30% credit, with the remaining 10% distributed among other interactions.
This model is best for B2B digital marketing and can help marketers understand the impact of lead generation and nurturing activities.
5. Custom Attribution Model
Custom attribution models let marketers tailor credit distribution based on specific business needs and customer behaviors. These models can be designed to align with unique sales processes, marketing strategies, and customer journey complexities.
Implementing a custom model requires a deep understanding of customer interactions and often involves advanced analytics tools.
How to Get Started with Multi-Channel Attribution?
For CMOs managing complex digital marketing strategies, implementing multi-channel attribution is a great way to assess the impact of various marketing channels on conversion events.
Here’s how you can go about it:
1. Define Attribution Goals
Before we get into the how of multi-channel attribution, it’s important to address the why. You must first define what you want to achieve with this endeavour.
Do you want to increase conversion rates?
Have you been asked to increase the ROI on marketing expenditure?
Or do you want to gain insights into the entire customer journey?
The answers to these questions will clarify the goals, which, in turn, will guide the selection of appropriate attribution models and tools.
Also, identify the key performance indicators (KPIs) you want to zero in on through multi-channel attribution.
2. Pick the Right Attribution Model
The next step is identifying which attribution model aligns with your marketing objectives and customer behavior patterns.
The best model depends on your business model (e-commerce, SaaS, B2B, etc.), customer journey complexity, and marketing goals.
| MCA Model | When to Use? |
|---|---|
| Linear | Useful for providing a general overview of all touchpoints involved. It can be used to distinguish channels with any impact and channels with no impact at all. |
| Time Decay | Best suited for B2B marketing with longer sales cycles. |
| U-Shaped | Best for instances where awareness and conversion stages are distinct. Also useful for long sales cycles. |
| W-Shaped | Valuable for businesses with distinct stages in their sales funnel, particularly when lead qualification is a key milestone or where large purchases are involved. |
| Custom | Best if standard models don’t provide an accurate reflection of marketing efforts, or in the case of a long, complex customer journey. |
Besides the above choices, you can also explore data-driven attribution models, which use algorithms and machine learning to assign credit based on the actual contribution of each touchpoint to a conversion.
These algorithmic models can offer a more dynamic and accurate view but require higher data volume and more advanced analytical capabilities.
For instance, Google recommends at least 200 conversions and 2,000 ad interactions within 30 days for an accurate data-driven attribution analysis.
3. Track Data Across All Marketing Channels
Work with your marketing team to implement tracking mechanisms to collect data from all digital marketing channels you currently use. This may include email marketing, social media posts, search ads, and display ads.
You essentially need to track both ad and conversion data based on the platforms you’re using for paid and organic marketing. As you can probably guess, you’d be tracking a lot of data.
Use UTM parameters and tracking pixels to track which channel the visitors to your website are originating from.
Similarly, track data on interaction with your website through JavaScript tracking scripts. Have your development team add this code to the website to capture data on user interactions (this will be useful in the next step).
4. Connect Every Touchpoint Across the Buyer’s Journey
Keep in mind that multi-channel attribution is also about understanding the customer journey. Integrate data from different channels to create a unified view of the customer journey. This involves mapping each customer touchpoint, from initial awareness campaigns to final conversion events.
Sure, there will be overlaps in interactions on different channels, but you’d still be getting meaningful insights into how your audience reacts with different channels and what pathways it takes to buy or convert.
At this stage, you should also incorporate offline interactions. They’re just as important as online interactions, if not more.
A 2022 survey found that 61% of consumer-brand interactions were online, while the remaining were offline. If you’re not considering the latter, you’re missing out on opportunities.
Where data on offline interactions is available, make sure it’s incorporated to map the customer journey.
5. Store Data in a Single Platform
In multi-channel attribution, collecting data from different tools and integrating is actually a pain point. According to the State of Attribution Survey 2024, 80% of marketers were dissatisfied with their ability to get results from different tools.
A better approach is to consolidate all collected data into a centralized platform. This can be your customer relationship management (CRM) platform, but a customer data platform (CDP) is a better choice. The former is useful for tracking customer interactions from different channels, but CDP consolidates customer data from the entire tech stack.
You may be able to use built-in integrations of your CRM or CDP to import data from different channels, for example, email marketing tools and social media accounts. Where such integrations aren’t readily available, invest in the development of APIs to make data import seamless.
6. Use the Right Tools for Analysis
Invest in analytics tools that support multi-channel attribution analysis.
Platforms like Ruler Analytics and Attribution offer dedicated analytics solutions for marketing attribution. These tools provide granular insights into marketing performance and can help you make data-driven decisions to optimize campaigns and improve return on ad spend (ROAS).
Attribution data from such platforms can be fed into an even more powerful, niche analytics platform to track key metrics.
Pro tip: When integrating data from various sources, ensure that all tracking parameters are standardized across channels. Consistent use of UTM parameters and naming conventions ensures accurate attribution and simplifies the analysis process.
P.S. Explore our CMO’s Guide to Campaign Management and Execution to get the blueprint for everything from strategy to analysis for your marketing campaigns.
What Are the Challenges and Drawbacks of Multi-Channel Attribution?
Remember: even the most accurate model won’t deliver results if your team can’t overcome the practical challenges of attribution.
1. Offline Interactions Still Go Untracked
Offline interactions are also important for attribution, but they’re notoriously hard to track.
In-store visits or phone calls often go unrecorded in digital attribution models, leaving an incomplete picture of the customer journey. For instance, a customer might research a product online but make the final purchase in-store, a conversion that may not be accurately attributed to the initial digital marketing efforts.
This may not necessarily be an obstacle for every business. For some businesses, the majority of customer interactions may be entirely online.
That said, if your business has brick-and-mortar locations or spends heavily on non-digital marketing (for example, out-of-home advertising), you may use marketing mix modeling (MMM) to get the full picture of your marketing efforts.
2. Privacy Laws Are Disrupting Attribution
Data privacy regulations, such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA), have imposed stricter guidelines on data collection and user tracking.
These regulations restrict the use of third-party cookies and require explicit user consent. That makes it more challenging to gather comprehensive data on customer behavior.
Similarly, the introduction of tracking opt-out with iOS 14 has made it difficult to collect interaction data with apps. For context, the opt-in rate was just 15% in three weeks after the roll-out of iOS 14.5 in 2021. And it’s reasonable to expect that rate has only gone down since.
Google had also announced that it would phase out third-party cookies, which many marketers rely on for tracking and attribution. However, it reversed its decision and instead allows users to choose whether they want such cookies.
All these challenges mean that marketers must rely more on first-party data and ensure compliance with these regulations to maintain effective attribution models.
3. Multi-Device User Behavior Complicates Attribution
Consumers often interact with brands across multiple devices. For example, they might start a search on a smartphone, continue on a tablet, and complete a purchase on a desktop. This fragmented behavior makes it difficult to unify user data and accurately attribute conversions to specific marketing efforts.
Without a cohesive view of the customer journey, attribution models may misrepresent the effectiveness of certain channels.
The right tools can address this challenge. For instance, Adobe Analytics can consolidate data from different devices and create a person-centric view instead of a device-centric one.
Multi-Channel Attribution vs. Marketing Mix Modeling (MMM)
Multi-channel attribution focuses on tracking individual user interactions across digital touchpoints to assign credit for conversions. In contrast, marketing mix modeling (MMM) takes a high-level, statistical approach by analyzing historical data, both online and offline—to measure the impact of marketing efforts over time.
Attribution is granular and real-time; MMM is broad and retrospective.
While multi-channel attribution zooms in on the user’s digital journey, it's best suited for optimizing specific campaigns and understanding the role of each touchpoint. It allows for real-time, user-level analysis that helps maximize ROI. However, it struggles with tracking offline interactions and is increasingly impacted by privacy regulations that limit user-level data collection.
MMM, on the other hand, provides a top-down view of overall marketing performance. It’s useful for evaluating the combined impact of multiple activities, especially across offline channels, and understanding broader trends over time.
Key strengths of MMM include:
Aggregate data analysis: MMM uses statistical models to assess the impact of marketing activities over time, considering factors like seasonality and market trends.
Inclusion of offline channels: MMM can incorporate data from traditional media, in-store promotions, and other offline activities, providing a comprehensive view of marketing effectiveness.
Much like with multi-channel attribution, CMOs can turn to MMM to make strategic decisions about budget allocation and campaign planning.
Despite its advantages, MMM requires extensive data collection and can be time-consuming to implement. It also lacks the granularity of MCA, making it less suitable for optimizing individual campaigns.
When to Use MCA or MMM?
The choice between MCA and MMM depends on the specific goals and resources of your organization.
Use MCA when: You need detailed insights into digital campaigns, require real-time data for quick decision-making, and focus primarily on online channels.
Use MMM when: You want to understand the overall impact of marketing activities, including offline channels, and are planning long-term strategic initiatives.
In many cases, a hybrid approach that combines the strengths of both methodologies can provide the most comprehensive insights.
Begin the Work!
The power of multi-channel attribution lies in its ability to provide clarity in a chaotic world of marketing. With value accurately attributed across the customer journey, CMOs can move beyond guesswork.
Use the attribution model that makes the most sense for your business model and marketing strategy. Invest in sophisticated technologies to automate data collection and processing.
Then, use the findings from attribution to maximize ROI, which is what stakeholders want from CMOs at the end of the day.
Expanding your leadership bench or advising on key hires for regional teams? Explore our list of Top CMO Executive Search Firms for Hiring Marketing Leaders to ensure you’re bringing in executives who understand data-driven strategy from day one.
FAQs
What’s the difference between multi-channel and omnichannel?
Multi-channel marketing involves engaging customers through multiple, separate channels, such as email, social media, and paid search, without necessarily integrating the experiences across these platforms.
Omnichannel marketing, on the other hand, focuses on providing a seamless and integrated customer experience across all channels. Customer interactions are often connected and create a cohesive journey, whether the customer is shopping online from a desktop or mobile device, by telephone, or in a brick-and-mortar store.
Is it worth investing in custom attribution models?
Yes, investing in custom attribution models can be worth it for businesses with complex marketing strategies and diverse customer touchpoints. Custom models allow marketers to assign conversion credit more accurately based on their unique customer journeys and business goals. This tailored approach provides more precise insights into marketing performance and better-informed budget allocation decisions that regular models may not offer.
How can I improve attribution accuracy without third-party cookies?
Shift to first-party data by tracking user behavior through your own platforms, like website analytics, CRM, and surveys. Moreover, use cookieless attribution methods such as unique identifiers and server-side tracking to maintain visibility. Tools like Google Analytics 4 are built to support these approaches in a privacy-first environment.
What’s the best model for omnichannel retail?
A position-based attribution model, like the U-shaped or W-shaped, is usually the best fit for omnichannel retail. These models give more credit to the first and last touchpoints while still accounting for the interactions in between. That’s ideal for retail journeys that span both online and offline channels, where initial discovery and final conversion matter, but so do the steps along the way.
Can attribution models measure brand awareness?
Yes, attribution models can be used to measure the impact of brand awareness efforts, although this can be more challenging than tracking direct conversions. Multi-touch attribution models, for example, assign partial credit to early-stage interactions, such as a display ad or a social media post, which are often associated with brand awareness.
When should I use multi-channel attribution?
You can use multi-channel attribution when you want to understand how different marketing channels contribute to conversions and overall marketing performance. It’s particularly useful in scenarios where customers engage with your brand through multiple touchpoints before purchasing.
You can get powerful insights about each channel and identify which ones are most impactful. Also, you learn how your audience behaves throughout their journey.