Imagine planning your next vacation.
In a deterministic world, you assume everything will go exactly as planned: no flight delays, perfect weather, no traffic to the hotel, and your kids love every minute of the itinerary.
In a probabilistic world, you acknowledge some uncertainty. You build in buffer time, check weather forecasts, maybe even buy travel insurance. You don’t assume it’ll all go wrong, but you don’t assume it’ll go perfectly either.
B2B marketing planning has been stuck in vacation mode #1: rigid, linear, and wildly optimistic.
And just like real-world travel, it rarely goes exactly according to plan. But unlike travel, most marketing plans don’t adjust, they just fail.
Let’s talk about why deterministic models like the SiriusDecisions waterfall feel broken and what probabilistic planning looks like instead.
Quick Definitions: Deterministic vs Probabilistic Planning
- Deterministic planning assumes fixed inputs will lead to fixed outcomes. If we spend X, we’ll get Y. It treats marketing like an equation where everything is knowable and controllable.
- Probabilistic planning recognizes uncertainty. It forecasts likely outcomes within a range, based on historical patterns and variability. It treats marketing like an investment portfolio built for confidence, not perfection.
The Real Problem With Deterministic Planning
Why marketing “funnels” like SiriusDecisions often break under pressure
1. Structural Flaws in the Model
- Fixed Conversion Rates: The model assumes conversion rates hold steady as volume scales. But double the leads rarely means double the pipeline since performance often drops with volume.
- Linear Stage Relationships: It presumes MQLs naturally turn into SQLs, then deals. But that relationship is often weak or missing entirely.
- Ignores External Factors: Once leads are “in funnel,” the model assumes no external disruption. But seasonality, budget shifts, or team changes can alter outcomes mid-funnel.
- Zero Time Lag: Funnel math often assumes immediate cause and effect. In reality, there’s a lag between spend and results, especially for upper-funnel activities.
2. Behavioral Consequences It Creates
- The Gumball Machine Mentality: Marketing becomes seen as an input/output machine. Put dollars in, get leads out. This erodes executive trust when results don’t match projections.
- Tactical Over Strategic: Because attribution favors short-term, trackable channels, marketers over-index on performance marketing and underinvest in brand and earlier-stage influence.
- Credit Battles: Specific rules for attribution and sourcing make the model feel fair, but they often ignite internal battles over who “created” the pipeline. These are a colossal waste of time.
- False Confidence: When marketing hits its funnel numbers but the business misses revenue goals, the model breaks and execs lose confidence in marketing.
🔁 3. Operational Fragility
- ‘What-Ifs’ are slow: When the environment shifts, like a budget cut, changes in the sales team, or a slowdown in deal velocity, marketers are forced back into their spreadsheets, manually tweaking assumptions. The problem? Those assumptions are now outdated or replaced with quick guesses, making the new plan just as fragile as the first.
- Disconnected from Reality: Most B2B buyers already have 1–3 preferred vendors before they ever fill out a form. But deterministic funnel math makes us chase leads as if awareness equals intent.
What Probabilistic Planning Looks Like
So if deterministic planning causes so many problems, what’s the alternative?
Probabilistic planning embraces uncertainty. Instead of asking “what’s the number?”, it asks “what’s the range and what are the odds?”
This doesn’t mean vague, hand-wavy plans. It means:
- Forecasting outcomes with confidence intervals
- Building models that adjust based on new data
- Using historical patterns to simulate what-if scenarios
- Accounting for variability in sales velocity, lead quality, and execution capacity
Marketers aren’t fortune tellers. But they are pattern recognizers. Probabilistic models give them the tools to make smarter bets, faster course corrections, and more resilient plans.
This Is Where Media Mix Modeling Shines
While you could do some of these improvements within your spreadsheets, Media Mix Modeling (MMM) is one of the best ways to implement probabilistic planning.
Instead of relying on click-path attribution or lead-sourced credit, MMM uses statistical models to measure the historical relationship between marketing investments and business outcomes, then runs simulations to forecast forward.
With MMM, you can:
- Test “what-if” budget scenarios
- See the likely revenue impact of different plans
- Factor in diminishing returns, seasonality, and execution delays
The result: less guessing, fewer internal battles, and more confidence in the next dollar you spend.
The Future of Planning Isn’t Perfect. It’s Probable.
Nobody knows the future, but that’s exactly why we need to stop planning like we do.
It’s time to leave behind rigid funnel math and sourcing battles. And instead, build adaptive, probabilistic plans that evolve with your business.
Your CFO doesn’t expect perfection. Your CRO doesn’t care who sourced the pipeline in your waterfall funnel. And your board doesn’t want MQL volume, they want revenue.
So give them what they really need:
- Flexible forecasts
- Clear signals
- Smart tradeoffs
That’s how you stop managing a funnel—and start managing a business.