What is Predictive Analytics Software?
Predictive analytics is a branch of analytics using different statistical methods, tools, and techniques to make predictions about unknown future events. Predictive analysis uses many techniques from data mining, statistics, modelling, machine learning, and artificial intelligence for analyzing current data to make predictions. Predictive analysis makes the future of businesses clearer. Incorporating this software into your business makes a peek into what is likely to happen beyond the present. Predictive analysis uses different methods of analyzing and interpreting various data to create a more accurate event forecasting that can occur in some parts of your business effecting your overall performance. Predictive analytics is important for your customer relations management (CRM). The software can also be used for determining various purposes like in determining the impact of taking certain risks as well as manipulating the market.
Predictive analysis is especially useful in project management for analyzing risks and follows a process including a definition of the project, collection, and interpretation of data, analysis of derived statistics and predictive modelling. This software is also used in customer relation management, child welfare, health industry and even for the detection of fraud by federal government.
Working of the predictive analytic software-
Automatic preparation of data
With the help of this feature, this software enables to collect, analyze and prepare data at a very fast rate. It works with big data and capable of analyzing spreadsheets up to five thousand cells. Apart from fastening of the process it also gives accurate and predictable output.
This software enables to access and work with a variety of models using automation and configuration as the program employs artificial intelligence capabilities.
Network and link analyzer
Predictive analytics software helps in analyzing and drawing out any connections and relationships between any given data. This feature makes possible to examine associations between customers pointing out those with the most influential positions.
There is only needed to install the software into necessary sections, lines, and processes of your business. After ensuring of its best performance, you will gain some critical insights into your business essential for overall decision making.
Benefits of predictive analytics
- Your business can connect dots and uncover trends in your sales and customer behaviour by combining big data with predictive analytics. Predictive analytics enables you to:
- Provide in depth customer insight and improve customer relationship.
- Improve supplier networks
- Identify issues in business processes.
- Help adopters in fields as finance, healthcare, retailing, hospitality, pharmaceuticals, automotive, aerospace and manufacturing.
Predictive analytics examples
Organizations are using predictive analytics in endless ways. Here are some examples of organizations making use of predictive analysis.
Predictive analysis helps incorporating records of component sturdiness and failure into future vehicle manufacturing plans. It is also helpful in developing better driver assistance technologies after studying driver behaviour.
It predicts the location and rate of machine failures by optimizing raw material deliveries based on projected future demands.
This technology forecast financial market trends and can predict the impact of new policies, laws and regulations on businesses and markets.
Predictive analysis determines the effect of weather events, equipment failures, regulations and other variables on service costs forecasting long term price and demand ratios.
It is helpful in specific maintenance operations on aircraft reliability, fuel use, availability and uptime.
Predictive analytics model
- Predictive analytic models allow users to turn past and current data into actionable insights, creating positive long term. Some typical types of predictive models are:
- Customer lifetime value model
- Customer segmentation model
- Predictive maintenance model
- Quality Assurance model