Attribution usually comes up when someone asks a very reasonable question: What’s actually driving revenue?

It sounds simple. It almost never is.

If you’ve worked in RevOps for any length of time, you’ve probably been in this exact situation. A dashboard goes up in a meeting. Someone points to an attribution report. Someone else questions the assumptions behind it. Ten minutes later, you’re no longer talking about decisions—you’re talking about whether the numbers should be trusted at all.

Attribution isn’t useless. But it’s easy to expect more from it than it can realistically deliver. The goal here isn’t to argue for a specific model. It’s to explain what attribution is generally trying to do, where it can be helpful, and where it tends to break down in real operating environments.

What Attribution Is Actually Trying to Answer

At its core, attribution is an attempt to connect activity to outcomes. Teams want to understand which channels, campaigns, or interactions tend to show up when deals close, and whether that insight should influence where time and budget go next.

Salesforce describes attribution as a way to understand how marketing efforts contribute to pipeline and revenue (Salesforce on marketing attribution). That framing is accurate, but it’s incomplete. Attribution can point you in a direction. It can’t explain everything that led someone to buy.

How the Common Attribution Models Actually Behave

First-touch attribution gives all the credit to the first recorded interaction. It’s often useful for understanding how prospects initially find you and which channels are opening the door. Where it falls short—especially in B2B—is that it ignores everything that happens after that first moment. In long sales cycles, first-touch usually explains discovery, not decision-making.

Last-touch attribution swings the pendulum in the other direction. It assigns credit to the final interaction before conversion, which can be helpful when you’re trying to understand what helps deals cross the finish line. The problem is that it erases all of the earlier work that built momentum. Salesforce itself has acknowledged that last-touch models tend to oversimplify real buyer journeys.

Multi-touch attribution tries to solve for both of these gaps by spreading credit across multiple interactions. In theory, this feels closer to reality. In practice, it often introduces a new challenge: complexity. Multi-touch models are harder to explain, harder to maintain, and often assume that every tracked interaction is equally meaningful. Tools like Agentforce Marketing (formerly Marketing Cloud) make it easier to capture engagement across channels, but more data doesn’t automatically lead to better understanding.

Where Attribution Starts to Break Down

Most attribution problems aren’t caused by the model itself. They show up because of the environment the model is dropped into.

Data is rarely as clean or connected as teams assume. CRM data, marketing engagement, and customer activity often live in different systems with different definitions. Salesforce emphasizes the importance of unified customer data, and platforms like Data 360 (formerly Data Cloud) help centralize that information—but only if lifecycle stages and ownership are clearly defined.

Buying journeys add another layer of complexity. Most B2B deals involve multiple stakeholders, offline conversations, and long gaps between measurable touches. A lot of real influence never shows up in an attribution report, no matter how sophisticated the model is.

Attribution also tends to fall apart when it’s treated like a scorecard. When teams are evaluated or compensated directly on attributed revenue, behavior shifts. People optimize for the model instead of the outcome, short-term wins get prioritized, and trust in the data starts to erode. Attribution works better as context than as a verdict.

How RevOps Teams Use Attribution Without Letting It Run the Show

The RevOps teams that get the most value from attribution tend to keep it in its place. They use it to spot patterns, not to assign credit with precision. They sanity-check what the data is saying with input from sales. And they revisit their approach as the business, market, or go-to-market motion changes.

Pipeline context from Agentforce Sales often helps ground attribution in reality. Post-sale signals from Agentforce Service can surface value that attribution models never capture.

A More Useful Way to Think About Attribution

Instead of asking which attribution model is “right,” it’s usually more productive to ask what decision you’re trying to make and how this data will actually be used. Attribution is most effective when it informs conversation, not when it’s treated as the final answer. At Revenue Ops, we see the strongest results when attribution is kept simple, transparent, and clearly positioned as directional rather than definitive.

Attribution doesn’t fail because it’s useless. It fails when it’s expected to explain things it can’t fully see. For RevOps professionals, the real value isn’t perfect attribution. It’s better context. When attribution helps teams have clearer conversations instead of ending them, it’s doing its job.