This white paper provides a summary of the differences, pros and cons of performing statistics modelling in R and Angoss KnowledgeSTUDIO. The statistical models explored in this paper include Decision Trees, logistic regression and linear regression models.
Successful companies both listen and understand what customers are saying and are taking action in response to customer feedback by incorporating the voice of the customer (VOC) into business strategies for sales, marketing and support. The business case for text analytics is compelling given that an estimated 80% of today’s business relevant data is unstructured or text-based. Moreover, merging unstructured text-based data with structured data gives organizations the ability to include additional predictive variables in their models and thus improves the accuracy and increases the lift (the effectiveness) of these models. This paper explores 3 text analytics uses cases that combine data mining and predictive analytics to improve customer retention, fraud abuse detection and reduction, and segmentation and cross-sell.
Best practices in the financial services industry for consumer lending, notably in the areas of credit card and personal unsecured financial products, have moved toward the use of proprietary credit scores and strategies created using data mining and predictive analytics. This paper discusses market and regulatory drivers that are increasing the usage of data mining and predictive analytics as an integral component of credit lifecycle management.
With more than 30 years of investment experience and more than $85 billion in assets under management (AUM), this leading mutual funds management company is no stranger to the unique and multi-faceted challenges of growing and retaining assets through a large and diversified financial advisors network. The company selected FundGUARD for its simplicity, versatility and quality. As a result, it has realized significant growth in AUM and improved sales productivity.
Capital Card Services provides industry-leading servicing and portfolio management to meet the needs of credit card issuers across the United States to over 500,000 cardholder accounts. The company wanted to apply data analysis software and predictive analytics to its business model in order to meet its strategic objectives of improved efficiencies and productivity.
A savvy analytical software consumer, Sainsbury's Finance department understands the value of its customer data. The company has been active in leveraging this rich information source for customer acquisition, retention and cross-sell/upsell. With millions of customers in its database, the company required an analytics solution that was fast, flexible, user-friendly and capable of handling large volumes of data. After rigorously evaluating many software options, Sainsbury’s Finance selected KnowledgeSTUDIO for its superior quality in all categories.