Analysis of Application of Markov Chain in Consumer Behavior Prediction

Abstract

The present paper focuses on the application of Markov Chain, a memoryless stochastic model, in predicting consumer behavior and thus optimizing marketing strategies for firms. The ability to model transitions between states based on conditional probabilities makes the Markov Chain a useful tool in foresight concerning the actions of consumers regarding purchasing, loyalty, and brand switching. The paper starts with the introduction of Markov Chains as an important means of predicting future states given the present state and presents a simplified consumer behavior model having states for browsing, purchasing, and leaving. The paper analyzes how businesses can use Markov Chains to realize consumer behavior in trends and preferences in the context of market analysis, consumer loyalty, and determination of the best marketing strategies. The paper, within the section on consumer loyalty, explains how Markov Chains can be applied in modeling brand loyalty and predicting the probabilities of switching over to other brands, having been using a case study on sports shoe brands to show the long-term trend in brand loyalty. Then, it goes on with the application of the Markov Decision Process in determining optimal strategies for Customer Lifetime Value maximization, including rewards and a discount factor that will determine the value of some approach. The paper ends by comparing the Markov Chain with other predictive models, such as the Neural Networks, outlining the fact that Markov Chains are simpler and suitable for situations with scarce data availability.