The Use of Markov Chains in Marketing Forecasting

Codruta Dura ()

Annals of the University of Petrosani, Economics, 2006, vol. 6, pages 69-76

Abstract: The Markov chains model is frequently used to describe consumers’ behavior in relation to their loyalty towards a brand, a manufacturer, a product, o a chain of stores, etc. Most frequently, this model is applied in marketing for dynamic forecasts of the market quota against a background of intense rivalry between brands. In a Markov chain, the result of a trial depends on the result of the trial that directly precedes the former. If we associate the conditional probability pjk (which means that if we obtain the result Rj, then the probability to reach the result Rk of the initial trial is pjk) to a set of possible results (Rj, Rk) and if the probability of the result Rj of the initial state is aj (initial distribution), then the conditional probability is, in fact, the transition probability from Rj to Rk.

Keywords: Forecasting methods; Markov chains; steady state vector; transition probability matrix; the loyalty indicator; the persistence of the loyalty indicator; marketing quota; marketing share; sales structure (search for similar items in EconPapers)
Date: 2006
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