Generalized linear modelling for assessing promotions

Price promotions are very important and widely used in marketing and pricing strategy. Therefore, understanding and quantifying the influences of price promotions on demand become interesting. In this study we modelled the demand as a Poisson distributed random variate and built a model using exponential demand function in microeconomics, which considers the variables that have an influence on demand, such as price, yearly seasonality, week days, competition etc. We also generated the necessary data and used our different data sets in the study. Since our main aim is to measure the impacts of price promotions on product demand and forecast the demand, we used generalized linear models, more specifically Poisson regression. GLMs are used for relating random responses to the linear combination of predictor variables. Poisson regression is a GLM for which the response variable is modelled by the Poisson distribution. We found that GLM is very helpful to quantify the effects of price promotions on product demand. If the mean of the daily demand is small, Poisson regression is needed to be used, otherwise ordinary log-level linear regression can be applied instead. We investigated that the results depend very much on the data and number of observations. In addition, we observed that when the price decreases by 20 \%, demand of a product increases about 18 \%. We checked our ``no interaction between covariates'' assumption with the help of AIC-Criterion and found out that the simplest demand model is the best model with the smallest AIC\_Value. We realized that once having reliable and usable real data, the models we suggested in this thesis can be used to quantify the effects of price promotions on demand. These models can help us to plan promotions, forecast the effects of promotions and make revenue maximization. Throughout this thesis R-Software environment has been used for all kind of computations.