CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM
This work addresses the need for faster processing of customer feedback to prevent churn in banking, though it is incremental as it applies an existing method (SOM) with a performance optimization.
The paper tackled the problem of analyzing customer complaints for CRM by proposing a visual sentiment analysis framework using Self-Organizing Maps (SOM), with a CUDA-based implementation (CUDASOM) that achieved an average speedup of 44 times on bank complaint data.
With the widespread use of social media, companies now have access to a wealth of customer feedback data which has valuable applications to Customer Relationship Management (CRM). Analyzing customer grievances data, is paramount as their speedy non-redressal would lead to customer churn resulting in lower profitability. In this paper, we propose a descriptive analytics framework using Self-organizing feature map (SOM), for Visual Sentiment Analysis of customer complaints. The network learns the inherent grouping of the complaints automatically which can then be visualized too using various techniques. Analytical Customer Relationship Management (ACRM) executives can draw useful business insights from the maps and take timely remedial action. We also propose a high-performance version of the algorithm CUDASOM (CUDA based Self Organizing feature Map) implemented using NVIDIA parallel computing platform, CUDA, which speeds up the processing of high-dimensional text data and generates fast results. The efficacy of the proposed model has been demonstrated on the customer complaints data regarding the products and services of four leading Indian banks. CUDASOM achieved an average speed up of 44 times. Our approach can expand research into intelligent grievance redressal system to provide rapid solutions to the complaining customers.