By Ali Orlando Wert, Director of Content at Databox
Every marketing leader has had this moment: the executive team drops next year’s revenue target, and you’re left figuring out how to reverse-engineer it into pipeline. You do the math. You map the funnel. You guess. Then you tweak. And tweak again.
And somewhere in the back of your mind, you’re wondering… Does any of this actually add up?
According to Bec Henrich, Head of Marketing at Traction Complete, this is exactly where most forecasting goes off the rails. “When you don’t look at the pieces of the puzzle that add up to whether something is attainable,” she says, “you’re shooting in the dark.”
Wishful Thinking ≠ Strategy
That disconnect — between targets and what the funnel can realistically support — is what Bec calls a “wishful thinking forecast.” It’s what happens when teams work backwards from arbitrary revenue goals, rather than forward from what the funnel data is telling them.
Instead of guesstimating your way through forecast season, Bec recommends a shift: treat forecasting as a modeling exercise.
“You can’t create math for everything,” she said in a recent episode of Metrics & Chill, “but you want to bring some predictability to what you can contribute as a team to the business.”
Start Where the Data Is: Leads to Opportunities
Bec’s modeling approach doesn’t require a PhD in data science—or a perfect historical dataset. She starts with what you can control and measure: the activities you’re running, the leads they generate, and how those leads historically convert to opportunities.
From there, the model builds:
- Lead estimates by channel or tactic
- Lead-to-opportunity conversion rates
- Average deal size
- Close rates
Even a basic model using these metrics can help you pressure-test revenue goals before you commit to them. “What you’re looking at is just a bunch of numbers,” Bec said. “Can you actually do what you’re putting in the model?”
Why Clean Data Is a Dealbreaker
Here’s the catch: none of this modeling works without clean data. “You don’t want to create your model based on rubbish input,” Bec said.
That means getting your CRM in shape before you start. At Traction Complete, the team uses its own suite of tools to help ensure their Salesforce instance is clean, standardized, and structured for forecasting.
Duplicate records? Inconsistent lead sources? Dormant opportunities that never got closed out? Those are the pitfalls that turn smart modeling into a messy guessing game.
Don’t Miss the Seasonality Factor
Another silent killer of accurate forecasting? Seasonality. It’s easy to evenly divide annual targets into quarterly chunks — but that’s rarely how your funnel behaves.
Bec’s advice: factor in the peaks (like event-driven quarters) and the inevitable lulls (like Q4 holidays). One of her models allowed her to shift quarterly goals to reflect a spike in pipeline expected from Dreamforce.
Without that adjustment, her team might have overperformed in one quarter and underdelivered in the next — with zero reflection of reality.
Forecasting as a Team Sport
Perhaps the most underrated part of forecast modeling? It’s not a solo act.
“For this to be successful,” Bec said, “we as marketers rely on the sales team.” Sales leaders need to validate the average deal size and close rate assumptions. RevOps needs to confirm the integrity of the CRM data. Everyone needs to agree on how each function contributes to the overall number.
When it works, the forecast isn’t just a number—it’s a plan the entire go-to-market team believes in.
Try It for Yourself
Bec shared a public version of the simple spreadsheet model she uses. You can plug in your own data and see exactly how changes in activity inputs affect your pipeline and revenue forecast. No complex software needed.
It’s a smart reminder that forecasting isn’t about gut feel — it’s about structure, alignment, and trust in your data.
Want to build forecasts your team actually believes in?
- Check out the full Metrics & Chill episode
- Try Bec’s free modeling template
- Learn how to predict performance with metric forecasts in Databox
Author bio
Ali Orlando Wert is the Director of Content at Databox, where she leads brand, content, and media. She’s passionate about helping RevOps and marketing leaders use data to tell stories and drive strategy.
