As name suggest Predictive Analysis / Analytics is the tool which is very popular among many industries to judge the market and user base. It has an ability to predict the consumer mind by using many different BI [ Business Intelligence ] tools. Predictive analysis optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks with strategic moves. Lets understand this growing concept in more detail.
What is Predictive Analytics ?
Predictive analysis is the practice of extracting relevant information from existing Database in order to determine user-specific patterns and predict future outcomes and trends by using BI algorithm. Predictive models and analysis are typically used to forecast future probabilities with an acceptable level of reliability base upon the data of information.
For Example : Before election many poll come in to picture from different media and news channels. And base upon the output of poll media predicts the output and compare it after results. In 90 + cases out of 100 it proves near the projection as these data are captured from the market at right time, to judge specific decision about party, and by using media which allow citizen to give input without any identity issue or pressure.
What are standard Predictive Analytics Techniques ?
- Regression Technicque
- Linear regression model
- Discrete choice models
- Logistic regression
- Multinomial logistic regression
- Probit regression
- Logit versus probit
- Time series models
- Survival or duration analysis
- Classification and regression trees
- Multivariate adaptive regression splines
- Machine learning techniques
- Neural networks
- Multilayer Perceptron (MLP)
- Radial basis functions
- Support vector machines
- Naïve Bayes
- k-nearest neighbours
- Geospatial predictive modeling
What is Must to get right Output ?
Accurate output need to be design with considering many key factors and analysis data. It need to be designed with many key elements. Some of the key elements are as under :
Data Quantity : More data will increase accuracy count of the analytics which help to derive final predictive output.
Data Quality: Quality is equally important as it has a power to misguide or mislead if right accuracy was not used while gathering or deriving the data sets.
Purpose and Objective : It should start with very clear purpose and objective of an analysis. Entire data capture process needs to be designed base upon core objective and purpose.
Time and Trend : Time and Trend are very crucial elements as prediction is about future, so right time and trend need to be followed while doing a survey or collecting samples. there are many elements which influence time and trend factor like a place of sample or input, a way of information gathering , process of information gathering etc.
Approach Design: After you have an objective, purpose, time and trend details along with data access, approach design is the most important and critical element where you need right industry expertise, knowledge of a domain and consumer centrism while designing process flow information. Approach need to be designed by an industry expert with BI knowledge to get proper data and analysis towards forecasting of the industry.
Who need Predictive Analytics /Analysis ?
Everyone who is investing to ensure that their current efforts are going in to the right direction towards future growth. Companies do it to ensure that their products will do good , political parties doing it to see if they are going to win or lose the elections, many companies doing it to see competition to design their marketing budget or Branding time.
In short, my advice to IT companies who are in to Business Intelligence is to identify this upcoming trend of BI, those companies can start working on the strategic planing to add PA as one of the key service area which is very niche and premium in many sectors in the market.
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