Index: Empirical Findings and Applications

Empirical Findings and Applications

Routing Summary

Empirical generalizations, optimal decisions, and implementation.

• What makes a generalization; meta-analysis; primary vs selective demand → Marketing Generalizations Overview
• Advertising elasticity ≈ 0.10; duration 6-9 months; coupon elasticity; promotions → Advertising and Promotion Effects
• Price elasticity ≈ −2.5; cross-effects ≈ 0.5; asymmetry; neighborhood effects → Price and Distribution Effects
• Dorfman-Steiner; ADBUDG optimization; VAR forecasting → Optimal Marketing Decisions and Forecasting
• Barriers to adoption, DSS, Kalman filter updating → Implementation of Market Response Models

Concept Map

Concept	Note	Type	Depends On	Key Result
What makes a generalization	Marketing Generalizations Overview	concept	Parameter Estimation in Market Response, Markets Data and Sales Drivers	Empirical regularities across studies via meta-analysis, with measurement-error caution
Advertising & promotion effects	Advertising and Promotion Effects	concept	Marketing Generalizations Overview, Carryover Effects and Distributed Lags, Functional Forms in Marketing	Short-run advertising elasticity ≈ 0.10; long-run roughly doubles; low coupon elasticity
Price & distribution effects	Price and Distribution Effects	concept	Marketing Generalizations Overview, Market Share Models, Reaction Functions and Competitive Dynamics, Multivariate Persistence and Cointegration	Own-price elasticity ≈ −2.5; cross-effects ≈ 0.52; distribution exceeds advertising
Optimal decisions & forecasting	Optimal Marketing Decisions and Forecasting	concept	Advertising and Promotion Effects, Price and Distribution Effects, Shape of the Marketing Response Function, Multivariate Persistence and Cointegration, Functional Forms in Marketing, Response Models for Marketing Management	Dorfman-Steiner optimality, ADBUDG allocation, dynamic control, forecasting methods
Implementation	Implementation of Market Response Models	concept	Design of Static Response Models, Design of Dynamic Response Models, Parameter Estimation in Market Response, Optimal Marketing Decisions and Forecasting	Organizational barriers, manager-model interface, and DSS adoption

Notes

• Marketing Generalizations Overview — CONTAINS: the marketing-generalization definition (six criteria), three discovery methods (informal observation, literature review, meta-analysis), meta-analysis framework, key null-hypothesis values table, Schultz-Wittink primary-vs-selective demand decomposition, and the measurement-error caveat ($O=T+E$, Eqs 8.11-8.12).
• Advertising and Promotion Effects — CONTAINS: the Leone-Schultz [LS] short-term advertising-elasticity generalization, long-run effect theorem, duration-interval theorem (Eq 8.13), life-cycle moderation, advertising-price-sensitivity generalizations, coupon-elasticity theorem, temporary price reductions (TPR), trade-promotion pass-through, display/feature multipliers, and the Frito-Lay BehaviorScan advertising-weight tests.
• Price and Distribution Effects — CONTAINS: the price-elasticity generalization (meta-analytic mean −2.5), upside/downside price asymmetry, cross-price elasticity theorem (0.52) with cross-price asymmetry, competitive clout and vulnerability measures (Eqs 8.14-8.15), neighborhood price effects [SSK] (Eq 8.16), life-cycle price dynamics, distribution-and-share example, and the Russell-Kamakura LSES model.
• Optimal Marketing Decisions and Forecasting — CONTAINS: Dorfman-Steiner optimality condition, ADBUDG response-function optimization, dynamic optimization / optimal control, Bertrand-Nash competitive pricing, multi-instrument marketing-mix optimization, four forecasting methods (ARIMA, transfer function, regression/ADL, VAR/ECM), the HP inkjet-printer case study, and forecast-accuracy metrics (MAPE, RMSE, Theil U, MAE).
• Implementation of Market Response Models — CONTAINS: five barriers to implementation (complexity, data, silos, credibility, staleness), calibration vs pure-estimation paradigms, hybrid Bayesian estimation, decision-support-system (DSS) architecture, adaptive estimation (rolling window, Kalman filter), five organizational success conditions, and MRM capabilities and limitations.

Sources

• Hanssens, Parsons & Schultz (2001), Market Response Models, 2nd Ed., Chs. 8-10

See Also

• Market Response Models