Predictive Analytics: Transforming Companies Through Statistical And Qua NT Itative Analysis

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Predictive Analytics: Transforming Companies Through Statistical And Qua NT Itative Analysis

Category : Financial Planners




The use of predictive analytics is common in industries such as financial services, retail and telecommunications. Businesses with established predictive analytics practices are now moving outside of traditional niches such as marketing and risk management. Organizations are now able to predict website click through rates and overall behavior, they also help the Human Relations staff anticipate which employees are most likely to be disgruntled or dissatisfied. Nowadays organizations use predictive analytics for everything from projecting the growth of markets and market shares to predicting when manufacturing equipment will fail and it involves using visualization to help executives see which events are normal business interventions and which ones require intervention.

Users of


predictive analytics

need to understand that predictive models serve as a decision support tool and how to use output in their own decision making process. This analytics is both a science and an art it takes time and effort to build that first model and get the data right but once the first one is built the next one is less expensive to model. It is all about projecting forward and transforming a company.

Business analytics delivers actionable insights new interpretations and evaluations of business performance based on data and statistical methods. Predictive analytics on the other hand provides customer level behavior prediction to enable businesses to deliver more relevant content to customers, improve response rate, improve retention and improve profitability of the company.

This is a general term for using both simple and complex models to support anticipatory decision making. Analysis of historical data is used to build a predictive model to support decisions. Managers in consumer packaged goods, retail, banking, energy and healthcare industries are the most active users of this kind of analytics. It is becoming increasingly incorporated into in day to day operations management using real time or near real time systems.

This analytics is becoming increasingly important in large and medium sized organizations where the development and use of analytics is becoming a core technology competency of many companies. How important is using predictive models? Very important, every organization should have an enterprise data model. Integrated planning and predictive modeling can enable an organization to adjust policy and execution in response to shifting dynamics in an organizations and business environment.

Today instead of competing on traditional factors, companies are beginning to employ statistical and qualitative analysis as methods for competition. These firms have managed to overcome historical barriers by gathering and managing transaction data and some of the cultural resistance in organizations that are accustomed to making decisions based on their gut feeling because of predictive analytics. It has managed to change the way organizations manage themselves and compete in the marketplace.

This article explains why organizations these days are using predictive analytics for predicting just about anything.Visit us


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