Model Accuracy

Lifesight’s Marketing Mix Modeling (MMM) framework is designed to deliver reliable, causal, and decision-ready insights for marketers. Achieving this requires more than just fitting a regression line — it demands a rigorous, multi-layered approach to accuracy, robustness, and validation.

Our modeling practice combines:

Structural Causal Modeling (SCM) to encode business logic, causal hierarchies, and guardrails
Regularized Regression (Ridge) for stable coefficient recovery
Evolutionary Hyper-Parameter Tuning to search across 100,000+ candidate solutions
Bootstrapping & Confidence Intervals to quantify uncertainty and ensure reliability

At the end of this process, Lifesight consistently recovers true incrementality and coefficients within a 95% confidence interval.

To communicate and ensure accuracy, we rely on two complementary buckets of evaluation:

1. Descriptive Accuracy Metrics

These metrics measure how well the model explains historical relationships.

1.1 Goodness of Fit Test (G-O-F-T)

The Goodness of Fit Test evaluates how well the model’s predicted values align with the actual data. The primary metric is R-square, which represents the proportion of variance explained by the model.

Range: 0 to 1
Benchmark: >0.8 is considered strong by industry standards
Interpretation: Higher R² indicates the model captures the underlying drivers more accurately

Lifesight computes:

Training R² — fit quality on the dataset used for estimation
Testing R² — fit quality on data unseen during training (out-of-sample)
Validation R² — performance during hyper-parameter evaluation

Why this matters High R² indicates stability and structural correctness in how the model attributes effects across channels.

1.2 Normalized Root Mean Squared Error (NRMSE)

NRMSE normalizes prediction errors relative to the scale of the data. Lifesight computes:

Training NRMSE
Testing NRMSE

Low NRMSE indicates a tight prediction band and a model that minimizes noise.

1.3 Overall Estimation Error

This quantifies the cumulative deviation of predicted vs. actual values across the time series. It helps detect:

Overfitting
Underfitting
Structural bias
Long-term drift

1.4 Heteroskedasticity Evaluation (Residual Stability Test)

Heteroskedasticity refers to the presence of non-constant variance in the model’s residuals. In MMM, this often occurs due to:

Seasonal spikes (e.g., BFCM)
Channel spending bursts
Asymmetric response functions
Nonlinear saturation or diminishing returns
Structural breaks (product launches, pricing changes, stockouts)

Why Lifesight checks this: A model with heteroskedastic residuals will produce:

Biased standard errors
Unstable coefficient estimates
Overstated significance
Incorrect confidence intervals

This directly affects incrementality accuracy and elasticity interpretation.

How Lifesight evaluates it:

Residual vs. Fitted value plots
Rolling window residual variance checks
White / Breusch–Pagan style tests (conceptually, without strict p-value dependence due to ridge regression’s nature)
Variance clustering detection (peak seasons, promo periods)
Bootstrapped variance stability across top 100 models

What Lifesight ensures:

Residuals do not systematically widen with the level of spend or sales
Variance does not cluster excessively during campaign bursts
Confidence intervals remain meaningful and reliable
Ridge regularization + evolutionary search dampens error variance inflation

If instability is detected, Lifesight:

Adjusts adstock or saturation priors
Corrects structural constraints
Rebalances hyper-parameter search
Re-runs residual bootstrapping

The outcome is stable, well-behaved residuals, which strengthen causal interpretation and improve coefficient recovery.

2. Predictive Accuracy Metrics

hese evaluate the model’s ability to predict future outcomes — a stronger test of robustness.

2.1 Holdout Accuracy

A certain percentage of data (typically 10–20%) is withheld during training. The trained model predicts this unseen window.

Measures pure predictive power
Ensures the model generalizes beyond historical patterns
Guards against overfitting common in high-dimensional MMM datasets

Holdout accuracy is often the most trusted metric for CFOs and growth leaders.

2.2 Short-Term Forecast Accuracy

Lifesight performs a forward prediction (e.g., 4–8 weeks) and compares it to actual realized business outcomes.

This helps answer:

“If I continue current marketing levels, how accurate is the model’s business forecast?”
“Does the model capture the correct elasticity and decay dynamics?”

Forecast accuracy validates the temporal stability of the model, not just its historical fit.

How Lifesight Produces Reliable Results: Evolutionary Modeling + Bootstrapping

Unlike traditional MMM approaches that yield one deterministic model, Lifesight’s pipeline generates:

100,000+ candidate solutions through multi-objective evolutionary search
Top models ranked on R², NRMSE, coefficient stability, saturation realism, decay realism, and causal validity
The best ~100 models selected for bootstrapping
Final coefficients, elasticities, incrementality, and ROAS computed as averages
Uncertainty measured at 2-sigma (95%) confidence intervals

This ensures:

No single model biases the results
Noise is averaged out
Coefficients converge to the true effect size
Every insight is backed by statistical confidence

Putting It Together: Lifesight’s Accuracy Philosophy

Accuracy = Fit + Predictability + Stability + Causality

Most MMM products stop at R². Lifesight goes further by validating:

Fit (R², NRMSE)
Predictability (holdout accuracy, forecast accuracy)
Causal correctness (SCM-based DAG constraints)
Coefficient stability (evolutionary search + bootstrapping)
Uncertainty (95% confidence intervals across 100+ top models)

This ensures every insight — whether ROAS, mROAS, elasticity, or budget recommendation — is not only explainable, but statistically reliable and practically useful for business decision-making.