Market Response Models — Overview

Summary

A market response model (MRM) is an empirical specification of the quantitative relationship between marketing decision variables and market outcomes (sales, market share). This note provides the book-level overview of Hanssens, Parsons & Schultz (2001), covering the conceptual framework, management applications, and the book’s structure across static models, dynamic models, estimation, time-series, and empirical findings.

What is a Market Response Model?

Market Response Model

A market response model is a mathematical function that maps marketing inputs (advertising, price, distribution, promotion) to market outputs (sales, market share). The canonical form is:

$$Q_t=f(A_t,E_t)$$

where $Q_t$ = unit sales, $A_t$ = marketing effort (advertising, price, promotion, distribution), $E_t$ = environmental factors (competition, macro economy, season).

The decision (spending) rule is modeled symmetrically:

$$A_t=f(P_{t-1},Q_{t-1})$$

Together these form a simultaneous system. The full structural model (Eq 1.1/1.2 in the book) is:

$$Q_t={\gamma }_{12}{A}_t+{\beta }_{11}{Y}_t+{\beta }_{12}{N}_t+{\beta }_{13}+u_{1t}$$ $$A_t={\beta }_{21}{R}_{t-1}+u_{2t}$$

where $Y_t$ = income, $N_t$ = competitor advertising, $R_{t-1}$ = lagged sales revenue.

Four Management Tasks

Management Tasks Supported by MRMs

1. Planning — which marketing instruments to deploy and in what quantity
2. Budgeting — how much total marketing spending to allocate
3. Forecasting — predicting future sales under alternative scenarios
4. Controlling — monitoring actual vs. predicted response; adjusting tactics

The model-based planning cycle iterates: Set objectives → Specify model → Estimate parameters → Optimize → Implement → Monitor → Update model.

Book Structure

Chapter	Topic	Technical Depth
Ch.1	Introduction: management tasks, MRM framework	Conceptual
Ch.2	Data: sources, variables, measurement	Moderate
Ch.3	Static response models: functional forms	Deep
Ch.4	Dynamic response models: distributed lags, competitive dynamics	Deep
Ch.5	Estimation and testing	Deep
Ch.6	Single marketing time series (ARIMA)	Deep
Ch.7	Multiple marketing time series (TF, VAR, cointegration)	Deep
Ch.8	Empirical findings: advertising, price, promotion, distribution	Concise
Ch.9	Optimal decisions and forecasting	Concise
Ch.10	Implementation	Concise

Key Methodological Distinction

ETS Approach

The book’s distinguishing commitment is to Econometric and Time Series (ETS) analysis — formal statistical methods using observed market data, as opposed to managerial judgment or laboratory experiments. All models are estimated from data; all parameters have statistical uncertainty.

Cross-Links by Chapter

Introduction (Ch. 1–2)

• Response Models for Marketing Management — management tasks, planning cycle, model framework (Ch. 1)
• Markets Data and Sales Drivers — data sources, variable types, measurement issues (Ch. 2)

Static Response Models (Ch. 3)

• Functional Forms in Marketing — 10 functional forms with full LaTeX + elasticities (Ch. 3)
• Market Share Models — MCI and MNL market share models (Ch. 3)
• Aggregation of Relations — temporal and cross-sectional aggregation bias (Ch. 3)
• Design of Static Response Models — variable specification, lagged effects, competitive structure (Ch. 3)

Dynamic Response Models (Ch. 4)

• Carryover Effects and Distributed Lags — Koyck, ADL, PDL, geometric lag structures (Ch. 4)
• Reaction Functions and Competitive Dynamics — competitive response, Nash equilibria (Ch. 4)
• Shape of the Marketing Response Function — S-shape, hysteresis, supersaturation (Ch. 4)
• Design of Dynamic Response Models — lag selection, partial adjustment, error correction (Ch. 4)

Estimation and Testing (Ch. 5)

• Parameter Estimation in Market Response — OLS, GLS, SUR, 2SLS, Bayes HB estimation (Ch. 5)
• Model Testing and Specification — RESET, specification errors, multicollinearity (Ch. 5)
• Flexible Functional Forms — translog, Box-Cox, AIDs flexible forms (Ch. 5)
• Model Selection and Exploratory Analysis — information criteria, cross-validation, exploratory tools (Ch. 5)

Time Series Analysis (Ch. 6–7)

• Single Marketing Time Series — ARIMA identification, estimation, and diagnostics (Ch. 6)
• Transfer Function Model — transfer function models for input-output time series (Ch. 7)
• Multivariate Persistence and Cointegration — VAR, cointegration, ECM, Granger causality (Ch. 7)
• Empirical Causal Ordering — Granger causality in marketing systems (Ch. 7)

Empirical Findings and Applications (Ch. 8–10)

• Marketing Generalizations Overview — meta-analysis criteria, null hypothesis values (Ch. 8)
• Advertising and Promotion Effects — advertising elasticity ≈ 0.10, duration 6–9 months (Ch. 8)
• Price and Distribution Effects — price elasticity ≈ −2.5, distribution effects (Ch. 8)
• Optimal Marketing Decisions and Forecasting — Dorfman-Steiner theorem, budget allocation (Ch. 9)
• Implementation of Market Response Models — organizational adoption, software, reporting (Ch. 10)

Related Methods (Cross-Domain)

• The Experimental Ideal, Differences-in-Differences — causal inference counterparts
• Bayesian Workflow - Overview — Bayesian estimation approach to the same models