Predictive modeling typically takes the form of models that are used to drive better decision-making. They shape the background and develop fact-based recommendations to address “business problems”. These data points and data patterns can identify strengths, weaknesses, opportunities and threats (SWOT). Employing transactional, demographic, historical, text, economic data and statistical modeling (ANOVAs, MANOVAs, Chi-Squares, Pearson’s R, Spearman Rho and regression analysis) enables us to develop multiple factors and predict business outcomes with a high level of statistical relevance, accuracy and business impact.
Often times, business problems include:
Attracting new profitable customers
Retaining and increasing revenue from existing customers
Transforming non-profitable customers into profitable customers
Avoiding Risks – financial, claims, fraud, collections, litigation and intellectual property compromises
Measure and develop plans/methods to ensure operational efficiency, reduce errors, costs and redundancy to enhance productivity, output and quality
Increase customer and employee satisfaction, loyalty and their ability to provide meaningful feedback
Resource Planning - financial, people, material, knowledge structure and time
Human Resources Management – recruiting, staffing, retention, rewards, development, performance and culture
Predictive Data Analysis includes:
Social network data
Web behavior data
Leadership & Culture
The overall arching umbrella to effectively employing and utilizing predictive models and data analysis is to ensure an appropriate leadership mindset and the development of a metrics driven culture.