Historical sales data:
QAZ.AI analyzes past sales, their volumes and dynamics to identify patterns and trends in demand. It takes into account seasonal fluctuations, changes in customer preferences and other factors that determine changes in demand over time. The system analyzes seasonal fluctuations, changes in customer preferences, economic factors and other influencing parameters to determine how demand changes over time. This allows you to predict future demand more accurately and adapt sales or production strategies to changing market trends and needs. Demand forecasting based on historical data analysis helps companies manage inventory, plan production and marketing campaigns more effectively, ultimately leading to improved customer service and increased competitiveness in the marketplace.
Competition data:
The system analyzes seasonal fluctuations, changes in consumer preferences, economic factors and other influencing parameters to determine how demand changes over time. This allows you to predict future demand more accurately and adapt sales or production strategies to changing market trends and needs.
Demand forecasting based on historical data analysis helps companies manage inventory, plan production and marketing campaigns more effectively, ultimately leading to improved customer service and increased competitiveness in the marketplace.
Technological progress and innovation:
QAZ.AI takes into account the impact of new technologies and innovations on demand. This includes both technological changes that increase demand for new products and the obsolescence of existing products.
Machine learning algorithms:
QAZ.AI uses various machine learning algorithms to analyze data and create demand forecasts. This includes regression methods, neural networks, decision trees and others. Regression Methods: This is a class of algorithms that helps determine the relationship between a dependent variable (such as sales volume) and one or more independent variables (such as time, prices, etc.). Linear regression, polynomial regression and other approaches from this class can be used to forecast demand. Neural Networks: These are machine learning techniques inspired by the workings of the human brain. Deep neural networks can be effective tools for analyzing complex data and identifying patterns in demand. Decision Trees: These are algorithms that use tree structure to make decisions based on dividing data into smaller groups. They can be useful in identifying factors influencing demand and forecasting it.
QAZ.AI, using customer interaction data, can significantly improve and optimize the service, making it more personalized, efficient and satisfying customer needs.
