Modeling the Customer Satisfaction Influence on the Long Term Sales: Example with Leading OTC Analgesics INN on National Market

Background: The customer satisfaction models are used to examine brand loyalty and sales. The utilization of the counter medicines depends directly on the level of knowledge of consumers, preferences and their satisfaction could be considered as an important predictor for their revenue. Objectives: The goal of the current study is to develop a Markov model for assessing the influence of the customer satisfaction on long term sales of leading OTC international nonproprietary names (INNs) of analgesics on the national market. Methods: Two first-order stationary Markov models based on marketing data for OTC analgesics sales and customer satisfaction inquiry, particularly from metamizole (MET), paracetamol (PAR), acetysal (ASA), and ibuprofen (IBU) were created and manipulated. The first model considered the very satisfied customers and the second the very satisfied and the somewhat satisfied customers. Results: MET is the INN with the most loyal customers followed by PAR. The product Markov matrix was derived after multiplications of the matrixes with market share and loyal customers’ probabilities. The steady state is achieved after 17 years for the group of satisfied customers and after 40 iterations for the group of somewhat satisfied. The market fluctuations are more dynamic in the second model probably due to lower determination of customers purchasing behavior. Conclusions: The model allows prediction of the long term changes in sales, differences between the groups of customers and long term marketing fluctuations. It could be useful in companies’ strategic sales management.