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Fundamentals of Predictive Analytics

By   /  October 5, 2017  /  No Comments

predictive analyticsPredictive Analytics is a sub-filed of Data Analytics and Business Intelligence, which deals with an in-depth analysis of past events and forecasts of future events. This specialized branch of Data Analytics combines the power of Data Mining, Data Modeling, Artificial Intelligence, and Machine Learning to make probabilistic predictions of future events.

So, Predictive Analytics (PA) relies heavily on the theoretical foundations of statistics to enable modeling of future behavior based on historical data. Global organizations today depend on Predictive Analytics to better leverage their data assets for business success. The DATAVERSITY® webinar on Descriptive, Prescriptive, and Predictive Analytics discusses this topic in further detail as well. The article Fundamentals of Descriptive Analytics goes into detail on the predecessor to this burgeoning part of the Business Intelligence industry.

Predictive Analytics is commonly used to detect fraud, predict customer churn, plug revenue leakages, optimize marketing programs, setting product prices, plan inventory, lowering operational costs, and reducing risks.

In Dataconomy’s Data Strategy Part 1, you will get a good introduction to PA. In an article by Bernard Marr of Forbes, he mentioned in his article titled 20 Mind Boggling Facts Everyone Must Read that a very low percentage of available data is actually used for analysis. Essentially, what the author wants to point out is that in spite of having access to high volumes of input data, businesses do not make use of the data for increased business benefits. Further, an IDG research report states that the three main challenges facing organizations are unconnected data sources, poor understanding of customer behavior, and poor analysis of sales potential. To each of these challenges, Predictive Analytics offers an answer. Also review What Is Predictive Analytics?

How Predictive Analytics Tackles Unconnected Data Silos

If you review the article titled Seven Reasons You Need Predictive Analytics Today, you will understand why this powerful sub-field of analytics can offer solutions to most business problems. Today, Predictive Analytics is successfully used in all industry sectors for making future forecasts regarding customer behavior or actions, business behavior or actions, or product success or failure in the market, based on historical data. Another Forbes article titled Predictive Analytics Is a Team Sport offers an interesting perspective on the team-involvement aspect of PA. According to this article, the “predictive data analysis” moves far beyond the marketing department to inform and guide other functions such as C-level executives, supply-chain, production, sales, and more. Enterprises moving forward from their nascent Data Analytics setups are more likely to invest in Data Analytics training and support for increased ROI.

A Survey on Marketing Analytics

A Forbes Insights article titled The Predictive Journey: Survey on Predictive Marketing Strategies that Predictive Analytics is guiding marketing decisions in all data technology enabled enterprises today. The survey included the feedback from 308, North-America based, C-level executives of companies with $20 Million or more annual revenue. The full survey Report is available for you from the above link. In Maximize Conversions with Predictive Analytics, Gartner indicates that in traditional marketing, customer buying patterns and purchase figures helped the businesses to plan inventory. In the digital business planning scenario, marketers have the support of advanced predictive modeling tools and IoT data to understand customer behavior.

Gartner’s Magic Quadrant Signals Growth in the Advanced Analytics Market

Gartner’s Advanced Analytics Report caters to the Data Science community, where advanced Data Analytics is conducted for determining enterprise business strategy. In the advanced Data Analytics world, Data Scientists, and Statisticians engage in developing models and algorithms, which may be used for a variety of business purposes.

This activity is distinct from routine Business Intelligence tasks conducted by ordinary business users. In Gartner Magic Quadrant: Advanced Analytics Fast Growth Continues, Gartner indicates continued growth of this market due to the fast adoption of Predictive and Prescriptive Analytics in industries. Further, the emergence of Big Data and Machine Learning has now catapulted the growth of PA to new heights. In near future, organizations will reap the benefits of hosted analytics platforms and ready-made algorithms, apart from investing in in-house Data Centers.

The Industry Applications of Predictive Analytics

The simplest way to understand the impact of Predictive Analytics in business applications is to do enough research. This synopsis offers an example of preventing customer churn through timely Predictive Analytics. If a business can determine which customers are likely to leave, then it can offer timely discounts or other tempting incentives to retain such customers. The choice of a PA platform depends on the user’s needs and expectations, so any discussion of analytics platforms or solutions is beyond the scope of this article.

The article titled Lower Costs with Predictive Analytics offers detailed benefits of sophisticated modeling techniques in enterprise marketing, such as uplift modeling, net lift modeling, and churn modeling. With these predictive models, businesses can essentially gain a deep understanding of current customer behavior for better decision making and improved performance.

The financial industry, one of the biggest beneficiaries of Predictive Analytics, has been using this branch of advanced Data Analytics to reduce fraud, measure risks, maximize customer retention with custom product designs. In the energy industry, everything from equipment failure or resource need to lowering operational costs have been tackled via advanced Predictive Analytics.  Even the Governments presently use Predictive Analytics for political administration and improved public services.

Can Predictive Analytics Misguide the End Users?

A DATAVERSITY® article titled Limitations of Predictive Analytics Lessons for Data Scientists shows the other side of this fascinating field of study. The article reveals how the end users of this type of data enabled predictions or forecasts can be misguided or brainwashed if the analytics processes and especially the available data, are not clean.

Opportunities for Predictive Analytics

The best way to train in this field is through actual project experience. According to this online resource known as Predictive Analytics training, Predictive Analytics for Business, Marketing and Web is one training program that combines theoretical sessions with interactive sessions and practical work. This training program emulates the real-world experience of acquiring data from multiple customer touch points. This program also exposes the students to the “business side of analytics,” without which the training would remain incomplete. The program attendees are provided with a copy of a highly acclaimed publication titled Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, besides the complete course material and an official certificate of completion.

For all those of you contemplating a career in advanced Data Analytics, the book titled Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die includes all the conceptual skills required to become a practitioner, and the reader is gently encouraged to enroll in a training program. Another noteworthy resource for future data analysts is the Predictive Analytics World FAQ, which provides a rich set of links to related topics.  The DATA-ED webinar known as Predictive Analytics gets Stuff Crystal Ball may also be a useful resource for the beginners in this field of analytics.

 

Photo Credit: Production Perig/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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