Use massive data to build machine learning model, so as to make the machine know how to think, find rules, and forecast the future trend.
Output the most likely consumer sentiment analysis based on the word vector model and contextual memory. Use clustering technology to divide the competition circle in the most scientific way, and then measure competition relationship, so as to to create alerts of an abnormal situation of competitors in real-time.
A nested Logit model is used to quantify impact on sales from competition between models and other factors, then to break down historical sales into the contribution from various factors, and finally to uncover reasons for sales change.
We use correlation analysis to analyze the deep relationship between sales and prices, and to analyze intelligently the effect of price changes.
We use a multi-layer perceptron algorithm to simulate how much should be sold given a specific sales target, and then synthesize models and algorithms to forecast market sales and price trends, and ultimately to optimize your goals and strategy development.
Modeling is the crucial part of the entire data mining process. It interacts with and promotes the understanding of data. WAYS selects and uses algorithms based on data understanding, matching its algorithm library to enhance the utility of models.
It takes a few seconds to complete the computer's operations that take many years to complete in reality.
The experimental cost of model analysis is much lower than the real cost of trial and error alone
The model can deal with many uncertainty factors in the business and assess risks associated with different actions
The model can accurately and stably quantifies the affecting factors and provides a more accurate base for decision-making
The model will have machine learning and training on WAYS data base to provide better strategic suggestions for unknown market