Improvement of the automated NLP system as a factor in improving the quality of marketing strategy formation

Abstract

Natural language processing in company marketing is transforming data analytics, offering new opportunities to understand customers and optimize strategies.

Introduction. Natural language processing simplifies processes such as sentiment analysis, segmentation, and ad targeting. It is important to consider data accuracy, security, and query management skills training for effective use of technology.

Problem statement. One of the main challenges in marketing analytics is the transformation of initial numerical data into understandable and useful conclusions for humans. The way to solve the problem are natural language processing technologies and generative artificial intelligence, which allow you to turn complex data into accessible and useful information for work.

Unresolved aspects. Traditional manual analysis of reviews in marketing analytics has long ceased to meet modern business requirements, because it requires huge human resources, which makes the process extremely costly. Natural language processing offers a solution to this problem through the use of algorithms capable of automatically analyzing the semantics of the text, determining the tone of statements, and isolating key topics from large data sets.

Purpose of the article. The purpose of this study is to develop a system of automated analysis of user reviews based on the developed effective methods and models for automated analysis of user reviews in the field of marketing of companies using natural speech processing technologies.

Main material. The paper describes the problem to be solved and formulates a scientific task; analyzes approaches, methods and models for solving research problems; sets research tasks, analyzes theoretical approaches to solving research problems; considers theoretical aspects of natural language processing; investigates various models and algorithms for analyzing feedback, and also conducts an experimental assessment of their effectiveness on real data; models, algorithms and analysis of their adequacy in solving research problems; methodological support for the organization of research is being improved.

Conclusions. The results of the study can be used to develop software solutions that will allow companies to better understand the needs of their customers, quickly respond to problems and improve the quality of their products and services.