Forecasting | MMM Framework

What We're Actually Asking

Forecasting isn't about predicting a single number—it's about understanding the range of likely outcomes given what we know. A forecast that says "We'll sell $10-12M next quarter" is more useful than "$11M" because it helps you plan for both optimistic and pessimistic scenarios.

The key questions forecasting can answer:

What's our expected outcome if we maintain current marketing levels?
How would outcomes change if we increase or decrease budget?
What's the probability of hitting a specific sales target?
What's the downside risk we should plan for?

The Weather Forecast Analogy

When a meteorologist says "70% chance of rain tomorrow," they're not saying it will definitely rain—they're telling you their confidence level. Good weather forecasts communicate uncertainty honestly, so you can decide whether to bring an umbrella.

Marketing forecasts work the same way. "We have a 75% probability of hitting our target" is more useful than a point estimate because it helps you decide how much risk buffer to plan.

The Core Concepts

2.1 Point Estimates vs. Prediction Intervals

A point estimate is a single number: "We predict $11M in sales." A prediction interval gives a range: "We're 94% confident sales will be between $9.5M and $12.5M."

Why intervals matter more than points

Point estimates give false confidence. If someone tells you "sales will be $11M," you might plan your operations, hiring, and inventory around exactly that number. When reality comes in at $9.8M or $12.2M, you're caught off guard.

Intervals let you plan for scenarios: "If we hit the low end, we can handle it. If we hit the high end, we're ready to scale." That's operational resilience.

2.2 The Fan Chart

A fan chart shows how uncertainty grows over time. Near-term forecasts are more certain (narrow band); far-term forecasts are less certain (wide band).

Interactive: Forecast Fan Chart

Adjust the budget multiplier to see how different spending levels affect the forecast.

Budget scenario: 100%

Forecast horizon:

Reading this chart: The solid line is actual sales (historical) and expected forecast (future). The shaded bands show uncertainty: darker = more likely, lighter = less likely. Notice how the band widens further into the future—that's growing uncertainty.

2.3 Scenario-Based Forecasting

Rather than a single forecast, you can generate multiple scenarios based on different budget assumptions. This helps stakeholders understand the trade-offs.

🛡 Conservative

Budget: -20% from baseline

Expected Outcome

$9.2M

[$8.4M - $10.1M]

P(Hit $10M Target)

32%

⚖ Baseline

Budget: Current levels

Expected Outcome

$11.2M

[$10.1M - $12.4M]

P(Hit $10M Target)

89%

🚀 Aggressive

Budget: +20% from baseline

Expected Outcome

$12.8M

[$11.5M - $14.2M]

P(Hit $10M Target)

98%

Target Achievement Calculator

Often the most useful question isn't "What's the expected value?" but rather "What's the probability we hit our target?" This calculator answers that directly.

What's the probability of hitting your target?

Sales Target: $ M

89%

Based on your current marketing plan, there's an 89% probability of exceeding $10M in sales next quarter.

Using target probabilities for decisions

Different targets warrant different confidence thresholds:

Break-even target: You need high confidence (>90%) to ensure survival
Budget target: Moderate confidence (70-80%) is often acceptable for planning
Stretch goal: Lower confidence (40-60%) is fine—it's aspirational

Downside Risk Assessment

Good planning considers not just the expected outcome but the downside risk. What happens if things go worse than expected?

📉

5th Percentile

$8.8M

"Worst case" (5% chance of being lower)

📊

25th Percentile

$10.2M

Conservative planning floor

🎯

50th Percentile

$11.2M

Median expectation

📈

95th Percentile

$13.6M

"Best case" (5% chance of being higher)

Interactive: Budget Sensitivity Analysis

How does the expected outcome and risk profile change as you adjust total marketing spend?

Reading this chart: The solid line shows expected outcome at different budget levels. The shaded band shows the 94% prediction interval. The vertical line marks your current budget. Drag the slider above to explore.

Practical Guidance

Common Pitfalls

Point forecast tunnel vision: Don't plan around a single number. The range matters more than the midpoint.
Extrapolating too far: Forecasts become unreliable with longer horizons. Be cautious beyond 2 quarters.
Ignoring external factors: The model doesn't know about competitor launches, economic shifts, or regulatory changes.
False precision: "$10,247,382" is not more accurate than "about $10M." Don't let spurious precision mislead stakeholders.

Best Practices

Present forecasts as ranges, not point estimates
Update forecasts regularly (monthly or quarterly) as new data arrives
Use scenario planning to explore sensitivities to key assumptions
Build buffer into plans based on the prediction interval, not just the mean
Track forecast accuracy over time to calibrate confidence

Implementation in MMM Framework

Basic Forecasting

import numpy as np 

# Predict with current media plan

baseline_pred = fitted_model.predict()

print(f"Expected sales: ${baseline_pred.y_pred_mean.sum():,.0f}")

print(f"94% CI: [${baseline_pred.y_pred_hdi_low.sum():,.0f}, "

      f"${baseline_pred.y_pred_hdi_high.sum():,.0f}]")

# Output:

# Expected sales: $11,200,000

# 94% CI: [$10,100,000, $12,400,000]

Scenario-Based Forecasting

# Predict with modified media plan (20% budget increase)

X_media_increased = fitted_model.X_media_raw * 1.2

scenario_pred = fitted_model.predict(X_media=X_media_increased)

print(f"With 20% budget increase:")

print(f"Expected sales: ${scenario_pred.y_pred_mean.sum():,.0f}")

print(f"94% CI: [${scenario_pred.y_pred_hdi_low.sum():,.0f}, "

      f"${scenario_pred.y_pred_hdi_high.sum():,.0f}]")

# Output:

# With 20% budget increase:

# Expected sales: $12,800,000

# 94% CI: [$11,500,000, $14,200,000]

Target Probability Calculation

# Calculate probability of exceeding a target

target = 10_000_000

# Sum predictions across time to get total for the period

total_samples = scenario_pred.y_pred_samples.sum(axis=1)  # Sum across time

# Calculate probability

prob_exceed = (total_samples > target).mean()

print(f"Probability of exceeding ${target:,.0f}: {prob_exceed:.1%}")

# Output:

# Probability of exceeding $10,000,000: 98.2%

# Get various percentiles for risk assessment

percentiles = np.percentile(total_samples, [5, 25, 50, 75, 95])

print(f"5th percentile: ${percentiles[0]:,.0f}")

print(f"Median: ${percentiles[2]:,.0f}")

print(f"95th percentile: ${percentiles[4]:,.0f}")

Multi-Scenario Comparison

from mmm_framework.analysis import MMMAnalyzer

analyzer = MMMAnalyzer(fitted_model)

# Define scenarios

scenarios = {

     'Conservative': {'TV': 0.8, 'Digital': 0.8},

     'Baseline': {'TV': 1.0, 'Digital': 1.0},

     'Aggressive': {'TV': 1.2, 'Digital': 1.2}

}

# Run and compare

for name, spend_changes in scenarios.items():

     result = analyzer.what_if_scenario(spend_changes=spend_changes)

     print(f"{name}: ${result.scenario_outcome:,.0f}")

# Output:

# Conservative: $9,200,000

# Baseline: $11,200,000

# Aggressive: $12,800,000