Auditing Marketing Budget Allocation with Hindsight Regret

2026-04-28

AI Summary

This study addresses the lack of systematic evaluation regarding whether historical marketing budget allocations approximate ex post optimal spending. The authors propose a hindsight-regret-based retrospective auditing framework that estimates spend–response functions and computes feasible ex post optimal allocations under budget and stability constraints. By integrating Monte Carlo methods to quantify estimation uncertainty, the framework effectively disentangles true allocation inefficiency from model estimation error. Introducing hindsight regret into marketing budget auditing for the first time, this approach reveals a practical trade-off between allocation flexibility and detectability, enabling reliable diagnostics when online experimentation is infeasible. Empirical evaluation on real-world marketing logs demonstrates that the method produces interpretable diagnostic reports and that moderate reallocations can capture the majority of measurable gains.

Abstract

Organizations routinely make strategic budget allocations under operational constraints, but often lack a principled way to assess whether realized allocations were close to the best feasible choices in hindsight. We present a retrospective auditing framework based on hindsight regret, defined as the opportunity cost of the realized allocation relative to a constraint-faithful benchmark under the same budget and stability guardrails. The framework estimates regime-specific spend--response functions from historical logs, computes feasible hindsight allocations via constrained optimization, and propagates uncertainty through Monte Carlo evaluation to produce regret distributions, expected lift, and probability-of-improvement summaries. This separates allocation inefficiency from uncertainty in the estimated response surfaces. Experiments on real marketing allocation logs show that the framework yields interpretable post-hoc diagnostics and reveals a practical trade-off between allocation flexibility and detectability: moderate feasible reallocations often capture most measurable gain, while larger shifts move into weak-support regions with higher uncertainty. The result is a practical method for auditing historical budget decisions when online experimentation is costly or infeasible.

Problem

• budget allocation
• hindsight regret
• marketing
• auditing
• opportunity cost

Innovation

• hindsight regret
• budget allocation auditing
• spend-response estimation
• constrained optimization
• Monte Carlo uncertainty propagation