20 Modeling in Marketing
20.1 Definitions
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Structural in quantitative marketing: An estimation strategy where we assume and impose a structure to the consumer’s maximization problem and these parameters are of the consumers’ utility functions (Tülin Erdem and Keane 1996).
- These parameter are policy-invariant.
Reduced form brand choice model: functions of marketing strategy variables (e..g, marketing mix). Hence, reduced-form models’ parameters are variant to policy. So it’s hard to study outcome of policy implementation may be reliable.
Uncertainty about product characteristics + learning behavior affect brand choice.
Two models (good in cases where consumer learning is vital to the choice process):
Dynamic structural model with immediate utility maximization
Forward-looking dynamic structural model (i.e., consumers don’t choose products based on their current utility, but also future utility).
20.2 Quasi-Experimental
(Goldfarb, Tucker, and Wang 2022) for a review
Settings: Weather, border, contract changes, firm policy, life or regulatory changes.
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Nine Stages to go through in a quasi-experimental design
Research Question
Data Question
Identification Strategy
Empirical Analysis
challenges to research design
Robustness
Mechanism
External validity
Unproven or Caveats
20.3 Transformation
20.3.1 Log-transformation
(Manchanda, Rossi, and Chintagunta 2004; Wies et al. 2019) used 1 in place of 0 for log-transformation. And also use 0.5 and 0.0001 for sensitivity analysis.
To control for firm size effects, (Wies et al. 2019) scale advertising investments by the firm’s total assets in the given year
20.4 Endogeneity
Check Background in strucutral Models section under marketing mix models for more up-to-date academic practice.
Modal paper that address both selection bias and endogenoueity (Frennea, Han, and Mittal 2018)
20.4.1 Control Function
In the context of consumer choice model (Petrin and Train 2010)