RecENGINE™ processes complex data sets using R/SAS based on a predictive algorithm, which accurately defines the risk profile of a customer followed by rule based segmentation to define characteristics of a particular segment to be targeted for a particular offer. Data is dynamically updated for each customer to recommend offers.
Gain an in-depth understanding of the credit card portfolio; Profile customer base to bring forth clear segments in the portfolio; Measure risk that existing segments within the credit cards portfolio hold; Align risk to customer threshold; Propose appropriate and attractive product offerings to existing customers to achieve maximum growth in revenue from higher utilization
Sentimentics is built on a capabilities framework, which involves complex logic & processes like Online Crawler, Big Data, Data Mining and Machine Learning Data Analysis, Sentiment categorization, Visualization, Features and Improvements, Identifying Fraudulent comments, Comparative comments, etc.
Web URLs are variable parameters defined in configuration files – at anytime new web URL can be added or removed from web crawling and data mining; Categorization of the Sentiments are predefined; Sentiments are defined as positive and negatives words as per the AFINN dictionary; Automatic schedule to run the sentiment data analysis of to produce reports; The end reports can be produced in the form of Graphs, data is spreadsheets for further processing; Sentiment tracking can use multiple dimensions for analysis to allow for proactive, customer insight driven actions