Demand Analytics / Study 07 of 07

Demand Score & Decision Support

Translating predicted demand into an interpretable 0–100 decision layer

Question: How can a weekly point forecast become an intuitive score without introducing subjective weights?
Method: Map predicted customers to a frozen development-period percentile scale.
Evidence: 2025 holdout observed demand rose across score quintiles; the score adds no separate forecast lift.
Decision: Use for pacing and performance review alongside predicted customer counts.

Business Question

How can a weekly point forecast become an intuitive score without introducing subjective weights?

Why It Matters

A common scale helps marketing and operations compare expected demand across weeks. The underlying customer forecast must remain visible so the score does not obscure business meaning.

Data / Method

Locate predicted New Customers within the empirical distribution of usable 2023–2024 development predictions, then scale the percentile to 0–100. Freeze the reference distribution before the holdout.

Analysis

Predicted New Customers
Percentile relative to development-period predictions
0–100 Demand Score

0–20

Unusually weak expected demand

Approx. predicted New Customers: < 0.86

20–40

Below-normal expected demand

Approx. predicted New Customers: 0.86–3.32

40–60

Typical expected demand

Approx. predicted New Customers: 3.32–11.70

60–80

Above-normal expected demand

Approx. predicted New Customers: 11.70–15.67

80–100

Unusually strong expected demand

Approx. predicted New Customers: > 15.67

Results

In the 2025 holdout, observed demand increased monotonically across score quintiles: higher score bands corresponded to higher observed demand. Use the score to compare expected demand alongside the underlying customer forecast.

Method detail

Supporting diagnostic: 2025 holdout Spearman rank correlation ≈ 0.905. Quintile-level observations are not supplied, so no numerical quintile chart is reconstructed.

Interpretation

This is not a manually weighted index. It is a relative demand-intensity layer, not a probability, causal measure, or guarantee of realized acquisition. Use it for pacing and performance review alongside the customer forecast.

Limitations

The score inherits the forecast’s limitations and its development-period scale. Any recalibration must be versioned and validated; the source band boundaries are approximate.

How this fits into the forecasting system

Decision-support layer: translates the provisionally validated integrated forecast into a shared business language without changing its underlying predictions.

Next: Integrated Adaptive Demand Forecast

Full case study

Download Full Case Study ↓