A Tobacco Sales Prediction Method Combining Seasonal Sales Information and Search Behavior Information

Abstract

The invention relates to a method for predicting tobacco sales volumes by means of fusing seasonal sales information with search behavior information. The method includes steps of firstly, implementing a stage for clustering queries relevant to tobacco sales; secondly, implementing a stage for extracting features; thirdly, implementing a stage for building prediction models. The stage for extracting the features comprises a sub-stage for extracting the search query features and a sub-stage for extracting the seasonal features. The method has the advantages that a process for fusing clicking with the queries and then forming information is provided, and the queries relevant to tobacco sales can be identified by the aid of the process; a dynamic smoothing process is provided, so that the adjustability of the exponential weighted moving average models can be improved; the method is provided for predicting the tobacco sales volumes by means of fusing seasonal time sequence analysis with the search behavior information.

Description

technical field [0001] The invention relates to the field of product sales forecasting, in particular to a tobacco sales forecasting method that integrates seasonal sales information and search behavior information. Background technique [0002] With the continuous deepening of the marketization of the tobacco industry, it is particularly important to accurately predict tobacco sales, grasp market demand, and provide a real and effective reference and foundation for the operation of the entire tobacco industry. [0003] Traditional tobacco sales forecasting models use qualitative forecasting or quantitative forecasting methods. Qualitative forecasting mainly relies on the experience of practitioners, and uses their judgments on the nature and degree of future development of things as the main basis for predicting the future, which has greater flexibility, including the forecasting method of business executives and the forecasting of comprehensive opinions of sales staff How...

AI Technical Summary

Problems solved by technology

Qualitative forecasting mainly relies on the experience of practitioners, and uses their judgments on the nature and degree of future development of things as the main basis for predicting the future, which has greater flexibility, including the forecasting method of business executives and the forecasting of comprehensive opinions of sales staff method, consumer survey forecasting method, Delphi method and other methods, but qualitative forecasting methods have strong subjective limitations, and human experience and subjective judgment ability will directly affect the accuracy of forecasting results

Quantitative forecasting methods focus on quantitative analysis, pay attention to the degree of change of the forecast object, and can make an accurate description of the degree of change in quantity. It uses historical statistical data and objective actual data as the basis for forecasting, and uses mathematical methods for processing and analysis. Including arithmetic average method, exponential forecasting method, simple moving average method, weighted moving average method, causal forecasting analysis and other methods. Compared with qualitative forecasting methods, quantitative forecasting methods are less affected by subjective factors, but they are more mechanical and lack qualitative The flexibility of the forecasting method and the higher requirements for information

As more and more people tend to use search engines for pre-purchase consultation, search query volume has become an important indicator for predicting sales trends, but the method of only using search query volume to predict sales trends does not make full use of the richness of search logs. user behavior and cannot simulate complex seasonal sales trends

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