Data Mining Application In Prediction Of Electricity Customer Behavior Loyalty

With the fast development of the Internet,the competition is becoming more and more intense between enterprises.Our Market research shows that the transaction cost between enterprises and old customers only with the total consumption of customers 1/6.We can see,importance of maintaining old customers in marketing.Customer loyalty is the embodiment of what the part of customer needs to maintain.It refers to the customer for business or brand preference and repeat buying.The real customer loyalty embodies a kind of behavior that it is customer's purchase behavior.In other words,we study the customer's purchase behavior that will be able to predict customer loyalty.Similarly,the loyal customer is the source of enterprise competitive advantage.They can take the initiative to transfer the good reputation for the enterprise,and recommend new customers.Business platform has a great advantage is that can record the purchasing records and browsing records.In the purchased of a large number of business customers to provide a large number of business data records.Using a part of data for training rules and classification,then using the rules to predict the rest of data,and comparing predicted values and real values to calculate the prediction precision of the model.After research the data records,e-business man will find it values.In this study,the log customer behavior of a e-business supplier on the platform as the research object,using the data mining algorithm,carries on the analysis from the customer's perspective to repeat purchase,the target variable electricity supplier to establish customer loyalty prediction model,further to the store business customer loyalty prediction through the target variable electricity supplier to establish customer loyalty prediction model.The results of the study found the optimal algorithm — — Boosting.The identification of the electricity supplier accurate purchase of the crowd,preparing for the electricity supplier after the precision marketing.