The definition of predictive analytics is the usage of statistical algorithms, machine-learning techniques and data in order to identify the probability of future results based on past or historical data.
The goal here is to go past simple statistics and reports on past data to supplying the best evaluation on what could happen in the future. The end objective is to make more efficient decisions and to come up with breakthrough insights that pave the way towards more informed actions.
Predictive analysis models use historical data to come up with a system that can be utilized to predict benefits for new or different data. The model comes up with predictions that amount to a possibility of the target feature (such as revenue) that is based on the estimated importance from a group of input features. This is different from the descriptive models that help you see what transpired and from diagnostic models that clue you in key relationships and help you determine the cause of an event.
There are more organizations that are implementing predictive analytics to help increase their competitive advantage and bottom line. Why is this happening now?
There are growing numbers as well as types of data. There is also more interest in utilizing data to come up with relevant information. There is the availability of cheaper and faster computers along with easy to use software. Economic conditions are tougher thus pre-empting the need for competitive edge among businesses.
Now that easy-to-use and interactive software is becoming more widespread, predictive analytics is no longer limited to statisticians and mathematicians. Managers, executive and analysts are able to manipulate this software as well.
What is Predictive Analytics capable of?
A 2014 report by The Data Warehouse Institute yielded the top five uses of predictive analytics:
- Point out trends
- Comprehend customers
- Develop business performance
- Power tactical decision making
- Forecast behavior
Some of the most usual uses of predictive analytics are:
Security and Fraud Detection – Predictive analytics can help stop the losses stemming from fraudulent actions even before they happen. With the combination of a number of detection procedures—anomaly detection, business rules, link analytics, and more—you are rewarded with better accuracy and greater predictive output. In today’s modern world where cyber security is a growing issue, behavioral analytics can spot abnormalities through a thorough examination of all network actions that may point to zero-day vulnerabilities, persistent threats and occupational fraud.
Marketing – Most organizations of today employ the use of predictive analytics in order to identify customer purchases and responses as well as to make cross-sell opportunities known.
Predictive platforms help companies attract, keep and grow the profitable clients while maximizing their marketing budget.
Operations – Many of today’s companies use predictive analysis in order to predict inventory as manage their resources. Airlines use predictive models to help them decide the number of tickets that they should sell and what price for each flight. Hotels use such models to get an idea of how many guests to expect on a certain night so that they can adjust their prices and maximize occupancy and revenue. Predictive models, as a whole, help businesses to run more smoothly and efficiently.
Risk – One of the biggest examples of predictive models is credit scoring. Credit scores are utilized to evaluate a buyer’s potential of non-payment of purchases ranging from insurance to cars to homes. The credit score is a number that is supplied by a predictive analysis model using all of the relevant data connected to the person’s credit-worthiness. Other uses that are risk-related include collections and insurance claims.
