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Predictive Analytics Use Cases

By   /  October 18, 2017  /  No Comments

predictive analyticsPredictive Analytics (PA) moves businesses beyond the reactive strategies of market response. This advanced Data Management technology helps the business leaders and operators to view the risks and opportunities well in advance, so that they can adequately prepare for the future. Predictive Analytics does not guarantee that businesses will face only positive outcomes; what it does is present accurate forecasts of both “probable” positive and negative opportunities looming in the near future, so that businesses can take proactive steps to prevent the negative possibilities and capitalize on the positive possibilities.

It seems that sensor-driven data has now added a new dimension to the traditional Data Analytics practices. PA is able to forecast demand and price curves, prevent churns, predict maintenance requirements, identify high risk patients, to name just a few.  For those who are unfamiliar with Predictive Analytics, DATAVERSITY®’s Advanced Analytics 101: Beyond Business Intelligence and Fundamentals of Prescriptive Analytics present a convincing case.

The Forbes blog post titled Why Predictive Analytics Is a Marketer’s Unicorn Rainbow Fantasy is somewhat ambivalent about the true power of Predictive Analytics as practiced by Data Scientists. The post seems to question the validity and veracity of available data trends versus the actual outcomes.

If this argument is stretched to marketing domains, then the large realm of “consumer behavior” is still an untapped or under tapped area. Predictive Analytics has been used in sports forecasts for years now, and we all know that predictions do fail in spite of strong data indicators. When it comes to marketing or consumer behavior, the “human” factor is very hard to predict as it is not merely governed by mathematical rules or principles. Thus, even data industry veterans admit that PA has still not reached a stage where it can predict human behavior or future human action by merely studying past events.

In the Predictive Analytics consulting business, the consultants often help their clients by finding answers in the client’s own data troves. The continuing rise of Predictive Analytics has been further fueled by Data Scientists who are perfectly qualified to analyze, evaluate, and compare historical data to predict future outcomes.

Predictive Analytics: Industry Applications

This section simply touches upon some common industry applications of Predictive Analytics. Industry sectors like banking, insurance, telecom, or retail frequently use the power of PA to identify primary markets, assay and mitigate risks, identify new revenue opportunities, identify valuable customers, reduce customer churn, offer segmented products and services, maintain business infrastructure, control quality, evaluate marketing campaigns, and lastly, analyze customers sentiments. The true power of Predictive Analytics, as indicated in the article titled 10 Predictive Analytics Use Cases by Industry, comes from a combination of advanced data technologies and capable manpower.

Have you ever wondered how real-time, data technologies are shaping the business models across industries? Where Predictive Analytics Is Having the Biggest Impact  demonstrates how the different types of live data sources are contributing to the existing Predictive Analytics setups in auto, aircraft, banking, oil, and energy industries.

Six Popular Predictive Analytics Use Cases

Predictive Analytics in Agriculture: If there is one thing that modern farming technology could not control, that was weather.  The agricultural community has perennially faced the wrath of Nature, leaving vast agricultural lands at the mercy of poor rain or flood. With the help of data technologies like PA, agriculture has grown into a science of accurate weather predictions and market forecasts. Hopefully, now the struggling farmers will see a turn in their fortunes, which they failed to realize even with the most modern farming technologies and tools.

Predictive Analytics in Manufacturing: The use of sensordriven data channels in the manufacturing units has greatly eased the process of monitoring and facing problems typically surfacing during the manufacturing operations. Problems are detected and resolved in real time, thus drastically reducing the manufacturing overhead.

Predictive Analytics in Healthcare: The healthcare delivery landscape has undergone a sea change ever since this industry sector embraced data technologies to enhance their physical infrastructure. All the way from predicting diseases and high-risk patients, Big Data, Machine Learning, and EHRs have made patient-care a collaborative engagement between the healthcare providers and the patient.  Healthcare is as much about prevention as about treatment, and more the global healthcare industry share and collaborate on patient care options, the better they will be equipped to prevent the occurrence of such diseases in future. Learn more about the rapid technological strides made in the healthcare industry in Four Use Cases for Healthcare Predictive Analytics, Big Data.

Predictive Analytics in Government: There is a rising trend in the Government bodies and organizations to follow and mine the social channels for better decision making. PA is now applied on social channels to analyze and predict public responses to Government policies and laws.

Predictive Analytics in Digital Testing: Knowing what and how to test the most crucial components of digital solutions can help to launch products and services faster in the market. Combining powerful techniques like data mining and Machine Learning, this capability can separate the winners from the losers. For all the digital geeks, the article titled Seven Predictive Analytics Use Cases for Your Digital Strategy presents some strong cases for use of Predictive Analytics in testing digital systems.

Predictive Analytics in Marketing: The ultimate goal of any marketing department is to maximize the returns (ROI) from their marketing spend. PA has made it possible to collect live data from various customer touch-points, both static and dynamic, to enhance the effectiveness of future marketing campaigns. Highly sophisticated market strategies – micro-markets, customer segmentation, spot campaigns or contests, real-time pricing, contactless conversions—have all been possible because of Predictive Analytics in the recent years. Read Can Predictive Analytics Really Do? To see how digital channels have transformed marketing into a 360-degrees customer-focused game!

How Predictive Analytics Is Changing the Retail Industry discusses how Big Data is transforming the retail landscape.  Also, review the blog post titled 9 Practical Use Cases of Predictive Analytics to discover some other popular uses of Predictive Analytics. You may find additional case studies in IBM case studies for the retail industry.

Frontier Technologies in Predictive Analytics

Business data assumes most power when it can help uncover “probable patterns,” thus chartering a course of preventive or proactive actions for the future management of business. Data can be a formidable input to Predictive Analytics, especially when it is put to test through powerful technologies like Machine Learning and Deep Learning.

McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning has made the following observations:

  • Interoperability between IoT systems will raise the potential value of such systems by at least 40%.
  • US-based retail operations have experienced a 19% jump in operating margins after implementing data technologies in the last five years.
  • The manufacturing units are experiencing positive outcomes of advanced analytics in the areas of product design, supply chain, and post-sales support.

Ever since McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity, it has witnessed the rise and triumph of Machine Learning, especially in Predictive Analytics. The full Report discusses Machine Learning use cases across 12 industry sectors. The input for this Report came from more than 600 industry experts.

 

Photo Credit: EtiAmmos/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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