Most GTM teams rely on pipeline, conversion rates, and revenue tracking, but these GTM metrics fail to explain why revenue grows or stalls. Traditional reporting shows correlation, not causation, leading to unreliable forecasts and wasted marketing spend. Causal AI for marketing analytics shows what is happening and why, and how to improve GTM forecasts.
A lot of GTM teams struggle with reporting mistakes because their dashboards don’t explain why revenue grows or stalls. Most traditional GTM metrics fail to show what’s actually driving revenue or how to predict future growth accurately.
Todd Mumford recently pointed this out on LinkedIn, listing friction metrics that usually get ignored.
Friction metrics reveal where GTM efforts break down and explain why we keep missing our targets. They rarely show up at quarterly reviews because they are rarely tracked consistently.
“The marketers who will outperform are brave enough to measure what hurts.”
Todd Mumford
For Todd’s complete list of metrics, see his post on LinkedIn.
Most GTM reporting focuses on what happened, not why.
You get revenue numbers, conversion rates, and pipeline figures, but these only tell part of the story. Here’s what’s missing:
You might see an increase in web traffic alongside revenue growth and assume one drove the other. But without causal analysis, you don’t know why revenue increased. Maybe it was a pricing change, a competitor going under, or an unrelated market trend.
According to a Wharton study, 57% of marketers misinterpret correlation as causation, leading to bad investments and wasted budget.
Think of it this way:
Many RevOps teams rely on pipeline coverage. For example, “We have 3x our quota in pipeline, so we’ll be fine.”
But without understanding which opportunities are likely to close and why, these forecasts are unreliable.
Google’s research confirms that traditional Media Mix Models (MMM) often inflate ROI estimates because “MMM typically produces correlational, not causal results.” That results in improper budgeting and misleading insights.
Marketing isn’t linear. Deals don’t move through funnels nor in a straight line.
Buyers come and go as they please revisiting touchpoints, getting stalled by procurement, and engaging multiple channels. But most GTM teams don’t capture these behaviors.
For example, 60-70% of B2B marketing content goes unused by Sales, according to Forrester. If you’re not tracking which content is influencing deals, you’re burning money.
To answer what’s happening, why it’s happening, and how to predict growth, GTM teams need to track metrics that explain real-world outcomes.
Traditional analytics can tell us what happened—revenue increased 20% last quarter. But It’s a mistake to assume that just because two things happen at the same time, one must have caused the other.
Causal analytics help us understand why—a specific campaign, a competitor going out of business, or an economic or geopolitical shift. Causal AI separates real cause-and-effect relationships from coincidences. It filters out random noise and external factors to show what’s really driving growth.
When done right, Marketing is an exponential multiplier of Sales effectiveness and efficiency.
The problem with traditional GTM metrics isn’t that they’re wrong—it’s that they’re incomplete.
If you’re only tracking pipeline and conversion rates, you’re missing the friction points, the real decision drivers, and the hidden inefficiencies that stall growth.
Causal AI can improve effectiveness, helping you fix GTM reporting mistakes, forecast revenue accurately, shorten sales cycles, and optimize your marketing spend.
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This article is AC-A and published on LinkedIn. Join the conversation!
