MKTG 555: Marketing Models

Transcription

1 MKTG 555: Marketing Models Concluding Session April 25,

2 Papers to Read Little (1970) Shocker and Srinivasan (1974) Guadagni and Little (1983) Roberts and Lattin (1991) Gupta (1988) Balasubramanian (1998) Ailawadi, Kopalle and Neslin (2005) McGuire and Staelin (1983) Venkatesan and Kumar (2004) Bass (1969) Hartmann (2010) 2

3 Papers to Read Rossi, McCulloch, and Allenby (1996) Kannan, Pope, and Jain (2009) Rutz and Bucklin (2011) Godes and Mayzlin (2004) Tirunelli and Tellis (2014) Ringel and Skiera (2016) 3

4 Different Types of Modeling We Covered Descriptive Models Predictive Models Decision Models Structural/Causal Models Analytical/Theoretical Models 4

5 Little (1970) Descriptive decision model (budgeting/resource allocation) ADBUDG aggregate response model Characteristics of good decision models Directly guessing outcomes, versus guessing inputs to a structured model that predicts outcomes The role of science in decision models Quality criteria: Is the model implementable in an organizational context? Does the model do better than what managers are currently doing without the model? Getting models used by decision makers 5

6 Shocker and Srinivasan (1974) Descriptive-Predictive decision model (Identifying new product opportunities) Preferences and choices, but link between them established arbitrarily (distance) Explicit versus implicit measurement of constructs (here ideal point, attribute salience) Ideal point and attribute salience determined via optimization (LINMAP) Iterative search for new product locations that optimize a specified metric (e.g. total revenue). 6

7 Guadagni and Little (1983) Descriptive, Predictive, Theoretical Decision Model (Resource allocation decisions based on modeling customer choices) Random utility model (linear utility) Links preferences to choices via optimal behavior (consumer picks product that maximizes utility) Considers consumer decision from an entire product category (i.e., incorporates competitive context) Independence of Irrelevant Alternatives Specification of brand loyalty and size loyalty (state dependence and heterogeneity) Maximum likelihood estimation Model fit measure (U 2 ) 7

8 Roberts and Lattin (1991) Theoretical-descriptive-predictive model Evoked sets, consideration sets, and choice sets Consumers search before making choices Size of consideration set depends on costs of search When expected benefit of considering a brand i is greater than cost (c) of considering brand, then a consumer include brand i in consideration set. Predictive test using two-stage choice model Maximum likelihood estimation 8

9 Gupta (1988) Descriptive-Predictive model to generate managerial insights Separate models for what (Logit brand choice), for when (Erlang-2 distribution), and for how much (Ordered Logit) consumers buy Modeling of consumer inventory (based on consumption rate) Combine model outputs, and partition sales increase into those due to purchase acceleration, those due to brand switching, and those due to stockpiling. Heterogeneity much stronger than state dependence in explaining choices Maximum likelihood estimation 9

10 Balasubramanian (1998) Game-theoretic model to generate managerial insights Multiple retailers competing with each other, and with one direct marketer Product fit with direct channel varies across product categories Nash equilibria in prices for retailers and for direct marketer A second game in which direct marketer first determines market coverage, and in the second stage there is competition in prices among retailers and direct marketer Direct marketer entry is an equilibrium outcome even in saturated retail markets When product fit with direct channel is low, then direct marketer should lower market coverage to increase profits for everyone. 10

11 Ailawadi, Kopalle and Neslin (2005) Game-theoretic predictive model to test theory Exogenous stimulus for testing (Value pricing strategy) Stackelberg Leader-Follower game Dynamic structural model with deterministic demand Manufacturer decides wholesale prices and deal amounts to offer Retailer decides whether and how much to order, retail prices for national brands, and retail promotion for private label Dynamic programming (backward induction) Tested model against two competing models (non-nested) 11

12 McGuire and Staelin (1983) Game-theoretic model to generate managerial insights Two manufacturers (bilateral monopolies) selling differentiated products each through one exclusive distributor Channel decisions made by each manufacturer leads to Nash equilibrium in prices (Bertrand competition), and as a consequence, determines channel structures. Manufacturer- Distributor has a leader-follower sequence and Manufacturers move simultaneously. Three channel structures: (1) M1, M2, F1, F2; (2) M1-F1, M2-F2; (3) M1-F1, M2, F2 Product substitutability has a major role in determining equilibrium channel structure 12

13 Venkatesan and Kumar (2004) Descriptive-Predictive Decision model Influence of marketing communications on CLV CLV components: Frequency and Contribution margin modeled at the individual level Generalized Gamma model for purchase frequency Comparison of effectiveness of optimizing CLV versus other metrics for resource allocation (customer selection for marketing effort) Genetic algorithm 13

14 Bass (1969) Descriptive-Predictive (theoretical?) model Long-range forecasting of adoption of durables/technology Models the hazard of new product adoption in a population of consumers (additive linear hazard model) Instead of guessing forecasts directly, guess or estimate parameters of a well-structure, well-tested model 14

15 Hartmann (2010) Structural decision model Choice models with social influence (choices of others) Incorporates a simultaneous move coordination game to model endogenous choices of groups of individuals (pairs, triples, etc.) Deals with multiple equilibria Accounts for both heterogeneity in preferences and homophily Shows how to deal with identification issues (namely, whether a unique set of model parameters could generate the observed data) Notion of model-free evidence Hierarchical Bayesian estimation 15

16 Rossi, McCulloch, and Allenby (1996) Predictive (theoretical?) Decision model Targeting individual consumers Random coefficient choice model with observed and unobserved heterogeneity Hierarchical Bayesian estimation Targetability under different information sets (Base, Demographic, Choices only, Full) 16

17 Kannan, Pope, and Jain (2009) Predictive-Decision model with data obtained from experiments Notion of form utility (pdf, web version, printed version) Demand for product bundles Correcting for sample selection bias (choice-based sampling) VAR model to assess impact of an intervention or decision (a pulse variable) 17

18 Rutz and Bucklin (2011) Descriptive model with managerial insights Effects of generic search on branded search Incorporates endogenous effects of bidding process for keywords Bayesian Dynamic Linear Model (Structure and Transition matrices) Validation using an alternative data set (Yahoo!) (Generic activity on Google versus branded activity on Yahoo! And vice versa) 18

19 Godes and Mayzlin (2004) Descriptive-Predictive model with managerial insights Volume and Dispersion (entropy) of WOM to predict (explain?) TV ratings SUR model 19

20 Tirunelli and Tellis (2014) Descriptive-Predictive Big-Data model with managerial insights Extract meaning from online chatter Latent Dirichlet Allocation to determine the latent dimensions (and their valence) in a set of reviews Unsupervised learning via Bayesian learning Labeling extracted dimensions using entropy reduction criterion Validation using human raters and with an independent external data source Bayesian estimation Useful for dynamic brand mapping. 20

21 Ringel and Skiera (2016) Descriptive-Predictive Big Data model with managerial insights Market structure visualization for markets with large numbers of products Conditional probability of joint consideration Louvain community detection algorithm Visualization of Similarity (VOS) Sub-market centric mapping Validation using external data (Google search volume, GfK market share estimates) 21