A LSTMRM based e-commerce customer churn prediction system in IoT with cloud environment

The customer churn prediction is crucial for e-commerce business as it enables them to develop effective customer retention strategies and execute successful marketing operations. The existing works focus on the correlation rather than the causal factors so it is difficult to inform the effective interventions. This paper introduces the LSTMRM approach for the effective customer churn prediction. Firstly, the dataset of the customer behaviour is collected. Next, preprocessing, involves in imputation of missing values with sparsity handling using KNTSN approach, then de-duplication and data type conversion is done. After that, from the pre-processed results, dynamic customer segmentation is done by KHPKC and simultaneously feature extraction occurs. Then, the customer churn is defined by the CPH model after that CFGL is utilized based on the CPH output and the extracted features. Next, the trend and seasonal pattern is recognized by using the DWC-CHT. After that DTW visualization is done from the results of trend and seasonal pattern and feature extraction takes place. Next, from the extracted features of DTW visualization, the churn is predicted by the LSTMRM approach. Finally, the predicted outcome from the LSTMRM approach and the churn defined outcome from the CFGL model are stored in cloud server. As per the experimental analysis, the proposed model attained 98.95% accuracy.