Customer loss early warning attribution method and device, computer equipment and storage medium
The embodiment of the invention relates to a customer loss early warning attribution method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining communication data of all customers, carrying out the data cleaning of the communication data, and obtaining...
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| creator | KONG NINGJIANG ZHANG JINGJING ZHOU YU WANG XIAOTIAN LIU WENTAO GOU YUCHEN WANG YAN |
| description | The embodiment of the invention relates to a customer loss early warning attribution method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining communication data of all customers, carrying out the data cleaning of the communication data, and obtaining a plurality of sample data sets; performing feature engineering processing on the plurality of sample data sets; using an XGBoost model and a Shap model to train a customer loss early warning attribution model based on the plurality of sample data sets after feature engineering processing; and on the basis of the trained customer loss early warning attribution model, performing loss early warning on the to-be-detected customer and predicting a loss reason. Therefore, the XGBoost algorithm in artificial intelligence is introduced, the potential tendency of customer loss is mined, the data prediction accuracy is improved, the Shap model is introduced to be combined with the woe coding method, customer loss reason ide |
| format | Patent |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Customer loss early warning attribution method and device, computer equipment and storage medium |
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