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