Traditional marketing methods of promoting goods and services are aimed at a wide audience and do not take into account the individual characteristics of consumers, which can lead to a small percentage of positive responses and even to negative responses (loss of customers). Wide audience coverage leads to an increase in the cost of marketing interactions and does not guarantee the achievement of the goals of marketing campaigns. In such a situation, the task is to minimize excess costs through a more rational organization of marketing interactions aimed at obtaining maximum profit from each target client. To implement such a strategy, tools are needed that can identify customer segments, marketing interaction with which will lead to a positive response. One of the technologies for building such tools is uplift modeling, which is a section of machine learning and is considered a promising direction in data-driven marketing. In this article, based on the open data X5 RetailHero Uplift Modeling Dataset, provided by X5 Retail Group, a comparative analysis of the effectiveness of various uplift modeling approaches is conducted to identify the segment of customers who are most susceptible to target impact. Various uplift metrics and visual technologies are used to conduct the comparative analysis.

Keywords: effective marketing communications with customers, customer segmentation, machine learning methods, uplift modeling, uplift quality metrics

The article is devoted to the problem of forming a set of corrective measures to deal with a set of the bank's arrears contracts. The problem at hand is topical practically for each bank. Formed set of measures should be adapted to the time and resource capacity of the employees of bank's specialized subdivision, to the predicted effectiveness of the measures and to the requirements of the bank's policy of customer loyalty. The proposed method of solving the problem of managing the contracts of arrears portfolio is based on a multi-criteria optimization model. Multicriteria is explained by the need to consider various criteria of efficiency and predicted effectiveness of the formed set of corrective measures. In order to find the optimal solution for the multi-criteria task, a special genetic algorithm is developed. The algorithm forms a set of non-dominant alternative solutions, which can be chosen by a decision maker. The article gives a brief description of the main steps of the proposed genetic algorithm and computational experiment on the basis of the developed software.

Keywords: Key words: arrears portfolio, multi-criteria optimization model, set of corrective measures, genetic algorithm, set of Pareto-optimal solutions

With the development of wearable technology, unique opportunities have emerged for providing user interaction and highly accurate personalized recognition of his work activity. The purpose of this study is to propose methods for taking into account the evaluation of the economic efficiency of human resources using big data and machine learning methods, which will allow making more informed decisions in the process of human capital management. The article proposes an approach using a hybrid neural network CNN-LSTM, aimed at determining the specific type of work performed by specialists, providing the ability to control the execution of these actions based on data from wearable devices (smart watches, smart bracelets). The accuracy of the developed algorithm in recognizing 18 different types of actions on the test sample was more than 90% according to the Accuracy metric (the proportion of correct answers).

Keywords: human capital, labor productivity, hybrid neural network, convolutional neural network, recurrent neural network