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Choice modelling in marketing- some examples Hans S. Solgaard, Department of Environmental and Business Economics June 2008

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  • Choice modelling in marketing-some examples

    Hans S. Solgaard, Department of Environmental and Business Economics

    June 2008

  • Overview

    a. What is marketing?

    b. The complexity of the choice context in marketing

    c. Brief history of choice models in marketing.

    d. Some marketing examples-Choice of TV program-An integrated model of purchase incidence, brand choice and quantity decisions-A model of store choice

  • Marketing

    Because the purpose of business is to create and keepcustomers it has only two central functions marketing and innovation. The basic function of marketing is to attract and retain customers at a profit(P. Drucker; 1999).

    Therefore: It is one of the most important areas of research in marketing to understand and measure the effects of consumer/customer choice

  • Complex choice context

    -many choice alternatives

    -high number of attributes, features and characteristics to to characterize alternatives and decision makers.

    -consumers are heterogenous

  • Brief historyDespite the complexity of consumer choice the choicemodelling literature was dominated by fairly simple stochastic brand choice model until beginning of the 80s.

    - parsimonious in behavioral assumptions and in parameterization.

    - contains no decision variables

  • Brief historyMost widely used models:

    -The NBD (negative bionomial distribution) modelPurchase incidence described by Poisson process (memoryless process!?)Heterogeniety modelled by Gamma distribution.

    -The Multinomial-Dirichlet model.

    -Beta-bionomial model.

    -Linear learning model.

  • Brief history

    BIG BANG in choice modelling was the development of the random utility model formulated as a multinomial logitmodel by McFadden et al. (1973).

    An initial appeal of the MNL model was due to it beingstochastic and yet admitting decision variables like price, quality, promotion etc.

    Utility directly incorporated in the model. Utility specified as a stochastic function consisting of deterministic component and a random component.

  • ExampleA model of audience choice of local TV news programSolgaard (1979, 1984)

    The model provides:a description of the associative relationship between viewersactual choices of news programs on the one hand and attributes of the programs on the other. I derive this model from a model of individual viewer choice behavior that specifies a viewers probability of choosing a particular program on a given occasion as a function of the viewers relative preferencetoward that program. The audience model is operationalizedusing a multinomial logit model.

  • ExampleExamples of factors that may influence the choicedecision:

    (1) news program attributes such as cast members (anchorman, weatherman,sportscaster), program contents, the sequence of presentation of the variousprogram segments, photographic coverage, scheduling (i.e., time of day the program is offered) and adjacent programs);

    (2) the general image of the TV station;

    (3) the quality of TV reception;

    (4) the news of the day.

  • ExamplePreference towards a news program:

    uji = Aji + Sji, j=1,2 ... J,

    whereuji = measure of preference assigned to program j by individual i,Aji = measure of individual is attitude toward program j,Sji = unobserved random component,J = number of alternative local TV news programs.

  • ExamplePreference of an arbitrary viewer may be written as:

  • ExampleAttitude towards a news program is specified in the form of a multi-attribute attitude model:

  • ExampleThe preference function can then be specified as:

    The event that an arbitrary viewer will choose program j

  • ExampleAssuming the random terms follow the Type I ExtremeValue Distribution results in the MNL model:

  • Example, results.

  • Economic models of choiceAssume the existence of a scalar measure of consumerutility that can be used to generate a preference ranking of the choice alternatives.

    Consumers are assumed to make choices that areconsistent with the concept of constrained utilitymaximization:

    max u(q), subject to pq y

    Where x denotes a vector of quantities, p denotes the price of each item and y is the budget constraint (i.e. the max expenditure a consumer is willing to make in a product category)

  • ExampleAn integrated model of purchase incidence, brand choice and purchase quantity, (Chiang, 1991 and Chintagunta, 1993, also see Hanemann, 1984)

    -Focuses on a single product category.

    -Shopping basket separated into two groups: one containing the productcategory of interest, the other all other goods purchased. All other goods aretreated as a composite good. The composite good is assumed essential and therefore always bought.

    -Consumers/households make decisions based on product attributes. A consumers (subjective) evaluation of each brand in the category is summarized in an index, , - a function of product attributes, marketing mixvariables and consumer characterisitcs.

  • ExampleConsider a store visit at time t, - made by household i. Where j refers to brand j. The households problem is to maximize u subject to the budget constraint:

    subject to:

    Where z is the composite good.

  • ExampleTwo possible solutions:

    a. Non-purchase with the budget spent entirely on the composite good, and

    b. Brand choice and quantity demanded.

    Solution: Non-purchase if the reservation price/shadow price, say R, is below the quality adjustedprices for the brands in the considered category.

    R < pj/j for all j

    Purchase if the reservation price/shadow price, say R, is above the quality adjusted price for at least one of the brands in the category.

    R > pj/j and R < pk/ k kj

    Explicit specification of the functional forms for u, and the distribution for are of course requisite for empirical applications.

  • ExampleExplicit specification of the functional forms for u, and the distribution for are of course requisite for empirical applications.

    Specifications:u may be specified as the indirect translog utility function. may be given the form:

    exp(aij + xijtss + ijt)Where aij represent hh is intrinsic preference for brand j, xijts is the value of the sth quality attribute af brand j for hh i on store visit t, and s the associatedparameter.These specifications leads to flexible and useful models for each of the choice decisions, for the brand choice it results in the MNL.

  • Store format choice exampleSolgaard and Hansen (2003)


    (1)To model the store choice decision of supermarketshoppers so as to be able to investigate the sensitivity of the store choice decision to changes in shoppersperceptions of the choice determinants.

    (2) to discuss problems involved in operationalizingstore choice models, using the framework of themultinomial logit model, and to suggest alternativemodel specifications to remedy the identified problems

  • ExampleDeterminants of store choice:

    -Store image i.e. perceptions of store values:Quality/service levelPrice levelSamples AssortmentAccessibility

    -Distance-HH descriptors

  • Example Store choiceProbability that consumer i will select store format j on a particular purchase occasion:

  • Example Store choiceProblems with the standard logit model.

    1. The coefficients of variables that enter the model areassumed to be same for all consumers.

    2. The standard logit exhibits the IIA (independence from irrelevant alternatives) property.

    A random coefficients logit model can remedy theseproblems.

  • Example Store choiceOperationalization of the store choice model as a random

    coefficients logit model:

    Where Xij is a vector of observed store attribute perceptions and householddescriptors and ij is a vector of unobserved coefficients for each hh thatvaries randomly over the households according to a distribution G. The term eij is an unobserved random term independent of X and , and distributed IID Type I extreme value.

  • Example store choiceSince eij is IID extreme value, as in the standard logit model

    We estimate the model applying Bayesian estimation.

  • Example Store choice, results

  • References:Chiang, J. (1991), A Simultaneous approach to the whether, what and how much to buy

    questions, Marketing Science, vol. 10, no. 4, pp 297-315

    Chintagunta, P. K., (1993), Investigating purchase incidence, brand choice and purchasequantity decisions of households, Marketing Science, vol. 12, no. 2, pp 184-208

    Hanemann, W.M. (1984), Discrete/continous models of consumer demand, Econometrica, vol. 52, no. 3, pp 541-561.

    Solgaard, H.S. (1979), Modelling choice of local TV news program, Unpublished Ph.D. dissertation, Graduate School of Business and Public Administration, Cornell University, Ithaca NY.

    Solgaard, H.S. (1984), A model of audience choice of local TV news program, International Journal of Research in Marketing, pp 141-151.

    Solgaard, H.S. and T. Hansen (2003), A hierarchical Bayes model of choice betweensupermarket formats, Journal of Retailing and Consumer Services, vol. 10, pp 169-180.