Sales Forecasting from Group Conversation Using Natural Language Processing

会议论文
发表日期：

2024年

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R. S. Shudapreyaa,
P. Santhiya,
S. Kavitha,
P. Prakash

摘要

Natural Language Processing (NLP) refers to a computer program’s ability to understand spoken and written human language. It is utilized to do large-scale research, receive a more objective and accurate analysis, increase customer satisfaction, develop a better understanding of the industry, and empower employees. Forecasting future sales by projecting the number of products or services a sales unit sell is known as sales forecasting. The system anticipates revenue projections from group conversations in WhatsApp using NLP. In recent years, WhatsApp has become the most popular and effective way of communication. WhatsApp chats feature a wide range of talks amongst various groups of people. Several new machine-learning technologies are found advantageous. In this project, the data was fetched from WhatsApp to analyze the chat. By applying sentiment analysis, it provides positive, negative, and neutral parts of the chat which helps identify people’s mindset about a particular product which can be further helpful in sales forecasting. The main objective of this project is to analyze the chat data using the NLP analyzer and extract valuable statistical insights. These insights will be used to enhance sales strategies and product development. By understanding the data obtained from the analyzer, we aim to make data-driven decisions that will lead to improved sales performance and customer engagement. This project analyzed the most active customer, the most active day in the group, the most active hour, the status of each customer who have messaged in the group, the customer who sent more text, media, links to the group, total messages sent per month, total messages sent per day, message count of each customer, top 10 customers who sent a letter to the group and are plotted in the form of a graph for the benefit of the admin. Through this, they can easily get information about the customer who are liking their products and who interacted with them the most.