PRODUCT MANAGEMENT METHODS IN THE IMPROVEMENT OF IT PRODUCTS

Abstract

The article presents a theoretical study of product management methods used in the process of improving IT products.

The relevance of the research topic is due to the need for enterprises in the information technology industry to effectively manage the development and improvement of IT products in the context of rapid digital transformation, increasing competition, and increased user expectations. At the same time, the main problem is that in the practice of applying product management methods, fragmentation and the absence of a single system are observed, which reduces the effectiveness of management decisions. In this regard, the formation of a holistic approach to product management, which combines flexible development methods, data analysis, and user orientation, becomes relevant.

The purpose of the study is to analyze and systematize key product management methods that contribute to increasing the effectiveness of improving IT products. The methodological basis of the study was the methods of analysis, synthesis, induction, and deduction.

To achieve the goal, a comparative analysis of traditional and modern approaches to product management was carried out within the framework of the study; existing product management methods were analyzed in order to group them into separate blocks according to functional purpose; identified shortcomings of methods for determining user needs and proposed ways to overcome them; developed a model that reflects the interaction of product management methods within the full IT product management cycle.

The obtained scientific results consist in the development of an integrated approach to managing the development of IT products, which includes four key stages: determining user needs (using the CustDev, JTBD and User Stories methods), prioritizing functionality (using the MoSCoW, Kano, RICE and Value-Effort methods), implementing changes with iterative hypothesis testing, as well as tracking effectiveness using NPS, A/B testing, retention rate and cohort analysis. The practical value of the study is that the proposed model allows for the formation of a single methodological basis for decision-making in product teams.

Prospects for further research include analyzing ways to automate product management processes using artificial intelligence, studying the use of data from digital platforms, and developing models to predict user behavior in the context of the dynamic development of the digital economy.