Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.
The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
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.
Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”
Generally, the term predictive analytics is used to mean predictive modeling, “scoring” data with predictive models, and forecasting. However, people are increasingly using the term to refer to related analytical disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but have different purposes and the statistical techniques underlying them vary.
Several organizations are leveraging predictive analytics to help raise their bottom-line and also to gain competitive advantage. Let us analyze the reasons for this trend:
- Growing volumes and types of data and more interest in using data to produce valuable information.
- Faster, cheaper computers and easier-to-use software.
- Tougher economic conditions and a need for competitive differentiation.
With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.
According to a 2014 TDWI (The Data Warehousing Institute) report, the five major things predictive analytics are used for is as follows:
- Identify trends
- Understand customers
- Improve business performance
- Drive strategic decision making
- Predict behavior
Predictive analytics is a boon, in that, it can prevent losses arising out of fraudulent activity, even before they take place. This is achieved by utilizing several methods for detection such as anomaly detection, link analytics, business rules and so on. Additionally, behavioral analytics too has an important role to play in the scheme of things as it examines every action on a network in near-real time. In this manner, it helps to home in on any abnormalities which point to occupational fraud, advanced persistent threats, and also zero-day vulnerabilities.
Companies all over the world use predictive analytics to understand a gamut of customer-related trends, such as their purchase cycle, responses; and also to elevate opportunities for cross-selling. In short enterprises can leverage predictive models to acquire, retain and nurture their customer base and in turn, optimize market spend.
Companies are increasingly looking to predictive analytics to help meet requirements for a plethora of issues such as, improving budget forecasts and demand planning. This is the reason why predictive analytics is fast turning into a critical aspect of what is regarded as the business intelligence portfolio. Most companies recognize the value of predictive analytics, even if they haven’t yet implemented it. Thanks to the opportunities that Big data has created, BI professionals can easily leverage predictive analytics to help their companies succeed.