Attribution Deep Dive Series Part Four: The Pros and Cons of Multi-Touch Attribution
Jun 30, 2025
Jason Stewart
Head of Content
More data was supposed to mean deeper insights, but bad or missing data broke that expectation.
No single attribution model can fully capture the complexity of a B2B buyer’s journey. First-touch and last-touch models each tell a narrow story … about where engagement began or about where it ended. But what about everything in between? That’s the space multi-touch attribution (MTA) tries to fill.
In a world of fragmented buying committees, long sales cycles, and dozens of touchpoints per deal, MTA aims to reveal the cumulative influence of marketing. It’s not a silver bullet, but it remains a central part of many B2B marketers’ attribution strategies. Used thoughtfully, MTA can drive smarter decisions. Used carelessly, it can lead teams astray. Especially if there are problems with the data.
What is Multi-Touch Attribution?
Multi-touch attribution assigns partial credit to multiple tracked interactions across a buyer’s journey. Rather than pointing to one campaign or asset as the sole reason for a deal, MTA seeks to capture a fuller picture of influence.
Sound complicated? It can be. So complicated that there are multiple different versions of multi-touch attribution. The most common variants include:
Linear Attribution
Gives equal credit to every tracked touchpoint. For example, if a deal involves five tracked interactions, each gets 20% of the credit. It’s straightforward and works well for teams that value consistency and breadth over pinpoint causality.
Time Decay Attribution
Weighs the most recent touchpoints more heavily, assigning more credit to touchpoints closer to the conversion. For instance, in a five-touch journey, the last touch might get 40%, the one before that 30%, the next 15%, and the earliest two just 10% and 5%.Older interactions still matter, but their influence fades with time. This is useful in models where recency is a strong signal of intent.
U-Shaped Attribution
Prioritizes the first and last touchpoints. For instance, a U-shaped model might assign 40% each to the first and last touch, and 20% shared by the middle.
W-Shaped Attribution
Emphasizes three key moments: the first touch, the lead ascension (or what some may call the “MQL moment”), and the opportunity creation or conversion. A typical W-shaped model might assign 30% credit to each of these three events and divide the remaining 10% among other touches.
All the varied multi-touch attribution offer different perspectives, but they share one fundamental goal: to measure the shared influence of multiple marketing efforts across the buyer's journey.
Pros of Multi-Touch Attribution
Balanced View of the Full Journey
MTA acknowledges that buying decisions are rarely made in one moment. By distributing credit across interactions, it supports a more realistic view of the influence you can track. This is especially helpful for B2B marketers working in long sales cycles where early- and mid-funnel engagement plays a significant role.
Better Investment Visibility
It’s typically much easier to acquire new leads than it is to advance them towards opportunity. Where single-touch models often reward obvious conversion events, MTA sheds light on those mid-funnel supporting tactics that help move prospects forward. Retargeting, branded search, nurture emails, and awareness campaigns gain visibility they might otherwise lack.
Supports Cross Functional Collaboration
When attribution includes sales development touches or product-led experiences, teams are more likely to align on performance reporting. MTA can surface patterns of collaboration and reduce the political friction caused by “who sourced it” arguments.
Strategic Budgeting Insight
By showing how multiple programs interact across a funnel, MTA enables more thoughtful budget allocation. It gives credibility to strategies that invest across stages, not just in lead capture. For example, if you have an abundance of “stalled” leads, MTA insights into the highest performing mid-funnel tactics can help get them unstuck.
Works Well With Account-Based Models
Multi-touch becomes even more relevant when analyzing buying group behavior. If five contacts at the same account interact with different content, MTA can recognize those combined signals as contributing influence.
It's typically much easier to acquire new leads than it is to advance them towards opportunity. Where single-touch models often reward obvious conversion events, MTA sheds light on those mid-funnel supporting tactics that help move prospects forward.
Cons of Multi-Touch Attribution
It's All in the Past
MTA is inherently reactive, not proactive. It is a rear view mirror that might offer prescriptive advice about what worked in the past and at which stage of the journey, but falls short of the predictive capability marketers need when faced with the if I gave you $10K what would happen? question.
Implementation Complexity
MTA requires consistent and complete data across all touchpoints. Without robust tracking, CRM hygiene, and process discipline, the output becomes unreliable. Most companies lack the resources, data infrastructure, or cross-functional alignment to implement it effectively. Check out the “side note” below on data quality.
False Precision
The ability to assign percentages can give a false sense of certainty. If a nurture email is credited with 15% of a deal, is that a reflection of true influence or just proximity to conversion? Assigning credit based on position or time without qualitative context can lead to misguided conclusions. Marketers should treat these numbers as directional, not definitive.
Can Obscure Key Moments
In some cases, MTA downplays the touchpoints that actually drive conversion. A model might assign 5% credit to a high-intent demo request (depending on when it occurred), while spreading credit equally across lower-value emails. This risks distorting strategic priorities.
Stakeholder Misunderstanding
MTA models can quickly become a black box. If marketers can’t clearly explain why one touch was credited over another (or how the model works) stakeholders begin to question the output. Trust erodes, especially when sales, finance, or leadership see attribution reports that conflict with their understanding of how a deal was won. As discussed in the first post in this series, attribution’s biggest challenge is not always technical. It’s interpersonal.
Requires Continuous Maintenance
MTA is not a “set it and forget it” system. As campaigns evolve and systems change, attribution logic needs to be recalibrated. Without regular oversight, MTA can drift into irrelevance or misrepresent the buyer journey entirely.
Attribution's biggest challenge is not always technical. It's interpersonal.
Side Note: The Impact of Systems and Data Quality on Attribution
Multi-touch attribution puts serious pressure on data hygiene. When systems don’t talk to each other (or when key data is missing), MTA’s complexity becomes a liability.
Consider incorporating regularly scheduled activities that enhance the accuracy and relevancy of the database. Issues like duplicate records, untracked interactions, inconsistent timestamps, and siloed systems can quickly degrade trust in results. And if there is more than “one source of truth”? Different groups will default to the reporting numbers that make them look the most successful, which further breaks down trust across GTM.
Many organizations lack the data science resources to diagnose or correct these issues. Marketing often relies on IT, RevOps, or external tools, which can deprioritize attribution amid broader analytics initiatives. I have seen marketing data sent to the bottom of the queue in many data science initiatives.
How Most Organizations Use MTA Today
Despite its appeal, MTA isn’t always used in isolation. Often, MTA lives inside marketing dashboards, slides, or reporting tools. Even worse? It often lives inside spreadsheets with data pulled manually from multiple systems.
Many companies adopt hybrid approaches, pairing MTA with last-touch or first-touch models depending on the campaign objective:
First-touch to understand acquisition sources
Last-touch to identify conversion catalysts
MTA to analyze influence and guide mid-funnel investment
For example, a team might use MTA to evaluate evergreen nurture tracks or brand efforts, but lean on last-touch for short-term conversion campaigns.
Others deploy MTA in post-mortem analysis rather than as a real-time measurement tool, using it to inform strategic planning rather than day-to-day optimization. This reduces the risk of over-reliance while still benefiting from its insights.
In many cases companies use MTA selectively, tracking it internally and solely for marketing use. They still rely on simpler models for executive reporting due to the clarity and consistency those models offer.
Multi-Touch Attribution by Persona: HOT / WARM / COLD
Each GTM persona evaluates attribution through their own lens. Their sentiment toward multi-touch attribution reflects their priorities, accountability, and trust in data complexity.
Stakeholder | Disposition to | Rationale |
Chief Marketing Officer (CMO) | HOT | CMOs champion MTA because it gives visibility to efforts often under-credited by simpler models … brand campaigns, nurture journeys, and content programs. It helps justify full-funnel investments and supports strategic planning across channels. |
Chief Revenue Officer / Head of Sales | COLD | Sales leaders often prefer direct, cause-and-effect attribution. MTA’s partial credit system feels vague and disconnected from actual pipeline outcomes. CROs want clarity on conversion-driving actions, not a list of incremental influences. |
Chief Financial Officer (CFO) | COLD | CFOs scrutinize any model that appears subjective. Without a clear understanding of what “influence” means or how it's calculated, MTA feels opaque. Finance teams are more likely to trust models tied directly to cost and revenue data. |
Marketing Operations / Analytics Lead | HOT | Ops leaders value MTA for its breadth and flexibility. It offers cross-channel performance insights and enables more precise reporting. While they understand its limitations, they also know how to interpret its nuances. |
Sales Ops / Revenue Ops Lead | WARM | RevOps teams may find value in MTA if it aligns with broader forecasting efforts. They want attribution to inform pipeline velocity and lead qualification, so if MTA supports those use cases their interest rises. |
Demand Gen / Campaign Managers | WARM | For campaign owners, MTA offers a more complete feedback loop. It helps show the impact of mid-funnel content and multi-touch nurtures. Still, many pair it with last-touch to keep an eye on what ultimately converts. |
The chart above illustrates the typical disposition of each role, not a universal truth. Where sentiment cools, complexity and lack of transparency often play a role. Where it runs hot, the model tends to support the functional objectives of the persona.
The Real Risk: Mistaking Complexity for Accuracy
It’s easy to assume that more complex attribution means more accurate attribution. But MTA’s sophistication can obscure its limitations … and like any attribution model, MTA influences behavior. When teams optimize for what’s measured, they can unintentionally bias efforts toward touches that register in the model (even if those touches don’t matter to the buyer).
Take a nurture stream, for example. If prospects open five nurture emails before requesting a demo, a linear MTA model might give those emails 80% of the credit. That sounds impressive, but was it really the emails that drove the decision? Or were those touches simply along for the ride? Or was there one specific email that tipped the scale?
Or consider a team that knows paid social ads often serve as the last interaction before opportunity. They might over-invest in retargeting, hoping to catch the “last click,” while undervaluing higher-funnel programs that actually built interest.
Without deeper analysis, teams may overvalue repetitive, low-effort touches like display ads or automated emails. Budget decisions follow the model. Over time, this creates a cycle of misaligned incentives. Marketing looks successful in attribution reports, but pipeline quality erodes.
Recommendations for Teams Using Multi-Touch Attribution
Start with Linear Models
Linear attribution is a good entry point. Low complexity, easier adoption, and useful directional signals.
Pair with Simpler Models
Don’t rely on MTA alone. Use first-touch for acquisition analysis and last-touch for conversion insight. MTA should be a complement, not a replacement.
Define Touchpoints
Clearly Agree on what counts as a touch. Is a chatbot viewed? Is a retargeting impression a “real” interaction? Document your rules upfront.
Align Cross-Functionality
MTA requires shared understanding. If sales, marketing, and finance don’t agree on the model, it won’t drive decisions.
Maintain and Audit Frequently
Set a quarterly schedule for attribution audits. Ensure platform integrations are working, lead timestamps are accurate, and channel definitions remain relevant.
What's Next for Multi-Touch Attribution
As marketers grow more sophisticated in their reporting strategies, MTA will evolve but won’t disappear.
Increasing Adoption of Custom and Weighted Models
Some organizations will move beyond standard models like linear or W-shaped to create custom weighting structures. These might assign greater value to in-person events, high-intent pages, or buyer-specific actions. While more tailored, these models still require rigorous data collection and clear stakeholder education.
Real-Time Optimization
MTA has long been reactive. But the next frontier is real-time influence mapping, using historical MTA patterns to proactively adjust spend or engagement in-flight. For example, a platform might detect that buyers who view a case study and attend a webinar within a week are more likely to convert. Marketing could then automate that sequence for similar accounts.
Real-time application demands more than analytics. It requires orchestration tools, clean data, and alignment between systems. Companies that invest in this capability will likely work with partners or platforms designed specifically for full-funnel attribution and optimization.
Smarter Predictive Models (without the buzzwords)
AI can help identify patterns across journeys, flag statistically significant touchpoints, and forecast outcomes. But it isn’t a magic fix. These models need well-tagged data and clear governance. Instead of focusing on technical terms like “Markov chains” or “Shapley values,” marketers should focus on building systems that feed cleaner, more consistent data into simpler predictive models. And please don’t ask me to define “Markov chain.”
Better Communication Across GTM
To succeed with MTA, marketers must explain not just the “what,” but the “why.” This means documenting the logic behind models, flagging limitations, and proactively addressing discrepancies between marketing attribution and sales-reported results.
Companies that achieve this will build trust, transforming attribution from a marketing-only tool into a shared lens for performance evaluation.
Multi-Touch Attribution: A Useful Tool, Not a Universal Answer
Multi-touch attribution isn’t perfect. But in a B2B environment where influence spans people, platforms, and time, it’s one of the better tools we have.
The goal isn’t mathematical truth. It’s credibility. MTA should offer directional guidance, support full-funnel thinking, and elevate conversations between teams.
Done well, it brings clarity. Done poorly, it muddies the waters.
Multi-touch attribution is a lens, not a verdict. Its value depends on what you ask it to do, and what you know it can’t.
Regardless of model, attribution should move with the market. It should evolve with buyer behavior, handle incomplete data with intelligence, and support smarter marketing … not just prettier reports.
Previous Post: The Pros and Cons of Last Touch Attribution
About Channel99
Channel99 offers an AI-driven B2B performance marketing platform designed to optimize marketing investments and enhance campaign effectiveness. The platform addresses challenges in attribution and data transparency by providing advanced tools such as predictive attribution models, superior account identification, and a universal verification pixel that uncovers the true sources of "Direct" web traffic. Features include view-through analytics, campaign and vendor scoring, and audience verification, all aimed at delivering measurable improvements in ROI and pipeline growth. Channel99 integrates seamlessly with CRM systems and media platforms, enabling marketers to make data-informed decisions and achieve greater financial efficiency in their marketing strategies.