Virtual Quantitative Marketing Seminar

Upcoming Talks

• Monday, November 3, Noon ET - Joonhwi Joo (UT Dallas)

Selecting the Best Arm in One-Shot Multi-Arm RCTs: The Asymptotic Minimax-Regret Decision Framework for the Best-Population Selection Problem

Abstract: We develop a frequentist decision-theoretic framework for selecting the best arm in one-shot, multi-arm randomized controlled trials (RCTs). Our approach characterizes the minimax-regret (MMR) optimal decision rule for any multivariate location family reward distribution with full support. We show that the MMR rule is deterministic, unique, and computationally tractable. We then specialize to the case of multivariate normal (MVN) rewards with an arbitrary covariance matrix, and establish the local asymptotic minimaxity of a plug-in version of the rule when only estimated means and covariances are available. This asymptotic MMR (AMMR) procedure maps a covariance-matrix estimate directly into decision boundaries, allowing straightforward implementation in practice. Our analysis highlights a sharp contrast between two-arm and multi-arm designs. With two arms, the "pick-the-winner" empirical success rule remains MMR-optimal, regardless of the arm-specific variances. By contrast, with three or more arms and heterogeneous variances, the empirical success rule is no longer optimal: the MMR decision boundaries become nonlinear and systematically penalize high-variance arms, requiring stronger evidence to select them. Our multi-arm AMMR framework offers a rigorous foundation that leads to practical criteria for comparing multiple policies simultaneously.

• Monday, November 10, Noon ET - Samsun Knight (Toronto)

Measuring Heterogeneity in TV Advertising Elasticities: Evidence from 135 Retail and Restaurant Brands

Abstract: We estimate the heterogeneity of TV advertising effectiveness across store characteristics and advertising levels using a large-scale panel of 135 US retail and restaurant brands, and then use these estimates to assess strategies for improving TV advertising performance. We find that only 48% to 56% of brands exhibit diminishing marginal returns at median advertising levels, suggesting that simply reducing ad expenditure across-the-board may not reliably lead to higher marginal effectiveness. We find significant heterogeneity in TV advertising elasticity across characteristics for over 93% of brands, but show that firms' observed allocations generally fail to fully exploit this estimated heterogeneity and instead covary much more closely with simple heuristics. For example, we find that firms tend to advertise in areas where they already have high revenue, rather than in the areas estimated to have the highest incremental revenue from advertising.  We project that brands could improve ad lift by a median 2.35 percentage points (relative to <0.5% median baseline ad lift) and earn tens of millions in additional revenue under identical-budget reallocations that better leverage this heterogeneity, and that 14-16 percentage points of brands with negative return-on-investment (ROI) from TV advertising could achieve positive ROI through such reallocations.

• Monday, November 17, Noon ET - Ashesh Rambachan (MIT)

• Monday, December 1, Noon ET - Eric Bradlow (Wharton)