| Abstract [eng] |
The client loyalty is one of the most vital things to any company. During last two decades the attention to customer relationship management systems has increased significantly. There are many criterias and calculation methods described in customer relationship theories. The existing management rule 80-15 states that when trying to clasify best customers, almost 55% are misclassified and therefore organization losses its investments (there’s not enough attention being paid to the best customers). The main reason of such misclassification is continuous change in business environment and subjective customer loyalty definition. Loyal customers do not stay loyal all the time. Using scientific literature analysis there were selected most widely used methods, which help the company to identify it’s the most important customers. Additionally “wish to buy” indicators were selected, which describe the customer buying potential. Using selected methods and indexes, there is a preposition of a new classifying model, which helps to predict customer class using collected data during contribution period. Model was implemented by using neural network and tested using experiment. It predicted customer class using sales data and the results were as follow: it classified 141 customer from 154 customers in total, made a mistake in 13 cases while classifying loyal customers. When classifying disloyal customers, it made a mistake in 45 cases and classified 102 correctly of 147 in total. Differently from the other tools that are offered at present, the proposed method distinguishes by its flexibility. It does not require strict customer loyalty definition, because it can teach itself by using subjective examples given by any company so they can get information on a customer class in time and implement marketing strategy. Projects consist of 66 pages, including 3 tables, 21 figures and 13 pages of appendixes. |
