Predictive modeling is used widely and helps identify accurate insights from a very large set of data and let the other users forecast. To have a major advantage over the competitors, it is essential to hold into the outcomes and the future predictions that your company might confront.
The data from the following sources are used to make a predictive model usually:
- Transaction data
- CRM data
- Data related to customer service
- Surveyor polling data
- Data on traffic on the web
- Economic data
- Data on geographic representation
- Demographic related data
- Data generated through machines
- Digital marketing and advertising data
There are four main types of predictive modeling:
It is related to data. For instance, a SaaS firm makes up for sale of 4,000 licenses in Quarter 2 and 3,200 licenses in Quarter 1. Descriptive analytics helps and provides insights into the query that involves the overall selling in between these two quarters.
Diagnostic analytics moves a step more with the data. It can also make it predict if the rise in sales is exactly due to the way the salespeople performed or is it due to the change in the interest of a certain section of the society.
This method uses techniques like machine learning or data mining, etc. and predicts the future. Here it is about looking into the past data to find out what the future has in store. Predictive analysis and data mining are very different from each other. A SaaS firm would base the data on sales of previous marketing expenses in the sector to create a forecast model to increase income and optimize the money that is spent on marketing by targeted marketing.
Prescriptive analytics gives a proposal for the forecasted outcome. Depending on the historical data, an action plan can also be recommended.
Advantages of Predictive Analysis
Improvement of Prediction efficiency – It allows firms to efficiently create a predictive modeling process that would make use of statistics, and a large number of past data sets let us get the result of the model.
These models let forecasting things from things like ratings in TV or technological advances and also sports.
Predictive Analysis and its Disadvantages
- There is a definite gap with the models regarding predictions and human behavior understanding
- Indirect Power in Decision models
- Failure in polling prediction
Even though we have seen the predictive modeling problems related to mathematics, it has the user’s requirement to make sure and plan because of the company and technical barriers. This might let you not get the past data that you need.