In this technologically challenging scenario, organizations have to rapidly customize themselves to be more responsive towards changing customer needs. We are well versed in delivering these solutions using open source technologies like R, Scilab, Octave, Python and Java. Predictive Research is well positioned to be a partner with Clients in Big-Data Analytics, co-Innovator to businesses who have realized the importance of data, and co-Creator of products in their transformation journey.
Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.
Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer's predictive score informs actions to be taken with that customer — business intelligence just doesn't get more actionable than that.
A predictive model is simply an equation used to predict something.
Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends.
A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results.
Why Predictive Modeling?
Nearly every business in competitive markets will eventually need to do predictive modeling to remain ahead of the curve.
- Predict prospects likely to buy
- Automatically predict risk and pricing at an individual-level
- Use unstructured text sources to predict market performance
- The chance a prospect will respond to an Ad
- When a customer is likely to churn
Statistical approach to forecasting change in a dependent variable (sales revenue, for example) on the basis of change in one or more independent variables (population and income, for example). It is widely used for forecasting and prediction.
In finance, linear regression is used for quantifying and analyzing the investment risk.
We Know How
The Predictive Models help our clients assess the implications for market growth several quarters into the future based on a formal quantification of the relationship between the various indicator series and market growth.
Separate analyses are conducted for the product and service sub-markets of focus, in each of the countries of interest, using the various indicators most closely associated with each of them.