Balance Statistical Rigor with Business Relevance in Models

Haley Vicini

1. Abstract

A successful model is not defined by statistics alone. The best models combine strong statistical performance with clear, intuitive business logic and are easy to explain to stakeholders. This best practice guides users through selecting, validating, and approving models for real-world use.

2. Context

Use this best practice when reviewing Predict results, comparing candidate models, or deciding whether a model is ready to move into Production or be shared with stakeholders.

3. Content

3.1 Why It Matters

High model scores and strong fit metrics can hide fragile or misleading relationships. Models that contradict business intuition often fail adoption, even if they look statistically impressive.

A disciplined selection and validation process ensures:

• Forecasts are defensible
• Drivers behave as expected under changing conditions
• Stakeholders trust and use the results

3.2 How to Apply

Model Selection

1. Review Model Score and R² to identify strong candidates.
2. Model Score > 0.80 is typically a good threshold, depending on the quality of the dependent variable data.
3. Compare the top several models, not just the highest-ranked one.
4. Examine included drivers:
   1. Do they make sense for the business question?
   2. Are any drivers surprising or hard to justify?
5. Check coefficient signs and magnitudes:
   1. Direction should align with intuition
   2. Effects should be reasonable in size

Final Validation

1. Confirm statistical health:
   • p-values < 0.1 for key drivers
   • Acceptable VIF levels
2. Review residual diagnostics:
   • No strong trends or recurring seasonal patterns
   • Residuals centered around zero
3. Validate business explainability:
   • You can explain each driver’s role in plain language
   • The story matches how the business actually operates
4. Prepare a short model summary:
   • What drives the outcome
   • Why those drivers matter
   • What the forecast implies

3.3 Example

Two demand models perform similarly. One includes many marginal drivers and is difficult to explain. The second uses wages, interest rates, and prices — all intuitive drivers — with slightly lower score. The team selects the simpler model because it is more relevant to the business and more likely to be trusted by leadership.

3.4 Common Pitfalls

• Automatically choosing the top-scoring model
• Ignoring counterintuitive coefficient signs
• Overfitting with too many explanatory variables
• Moving models to Production without documentation or review

3.5 Expected Results

• Models that are both statistically strong and business-aligned
• Increased stakeholder trust and adoption
• Forecasts that stand up to questioning and review