Analys av försäljningsdata från Google Adwords med EM-algoritmen
Sammanfattning: The purpose of this paper is to find a model that describes the distribution of Google Adwords order data and to find an appropriate method to estimate the expected value for an order. We do this on behalf of the marketing agency Precis Digital. We receive data for one of their customers. To begin with we fit some widely used probability distributions using the Maximum-Likelihood method and investigate how well these distributions describe the empirical data. We assume that our data is not derived from only one probability distribution. Therefore we go further by fitting a mixture of normal distributions with the EM-algorithm and see how well it fits the data. We evaluate the previous single distributions and the mixed distributions using Bayesian information criterion. It turns out that a mixture model fit the data quite well and better than previous single distributions. We evaluate how well mixed models describe data with few observations by using the EM-algorithm on individual campaigns. We come up with the conclusion that mixture distributions are good at describing the distribution of campaigns with few observations. However the estimated expected value of an order does not differ much from an estimation done with the mean value. Another interesting result is that subpopulations of customers probably exist since a mixed model better fits the data. In further research it would be interesting to deeper investigate these subpopulations. These subpopulations probably represent different customers with certain buying behavior. By further investigating these subpopulations campaigns can be optimized.
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