Predictive analytics is enabling insight-driven decision making across businesses worldwide
Published Date: February 7, 2017 Author: Shikha Sinha
Predictive Analytics, well remarked as the future of advanced analytics, is leading the priority list of the CIOs worldwide. Predictive analytics, already a billion-dollar industry, is turning out to be a game-changer for the various sectors, addressing all the Why’s, When’s, How’s, and Where’s pertaining to the businesses. The increasing competitive scenario demands well analyzed data driven decisions rather than the ones based on assumptions, and therein predictive analytics tool is being significantly embraced by the industries globally.
Predictive Analytics empowers the organizations with the forecasting capabilities based on the insights derived from the large pool of unstructured data. Having deployed the descriptive and diagnostic analytics, which determined what happened and why it happened in the past, now is the era for predictive analytics, which foretells what will be the future outcome, based on the understanding and analysis of the historic data.
What is Predictive Analytics?
According to Eric Siegel, Executive Director of Predictive Analytics World, “Predictive analytics is a business technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.”
Predictive analytics is a technology used by businesses to derive actionable insights from the large set of data from internal and external sources such as surveys, transactional data, etc.
Predictive analytics optimizes the efficiency of an organization by designing a near accurate model to build the strategies pertaining to their revenues, production, operations, etc. It uses statistical algorithms, machine learning, and data mining techniques to determine the likelihood of the future events.
How Predictive Analytics works?
Predictive analytics works broadly on two models: Classification and Regression. Classification model works on the binary system predicting the results in terms of 0 or 1, where 1 represents the occurrence of targeted event.
Regression model analyzes the data through statistical algorithms, data mining, machine learning techniques, etc. to predict the results in terms of exact numbers. For example- the exact revenue generated by a customer, number of machines required to meet the manufacturing demand, the number of months in which the machine will need repair, etc.
Applications of Predictive Analytics
Optimum Efficiency- Every organization in today’s competitive era is focused on maximum resource utilization to enhance its productivity and efficiency. Predictive analytics assists the companies to build a clear strategy for the future by forecasting the desired variables such as target market, production demand, revenue, etc. A higher ROI, which is the priority focus of every organization, is being well met by the usage of predictive analytics tool. For instance, Virgin Atlantic uses predictive analytics tool to set ticket costs based on the number of customers who will be travelling.
Marketing - The hit and trial method of marketing is no longer a preferred technique by the companies, as targeting an unsuitable market hampers the companies in terms of cost, time, and productivity. Predictive analytics help the firms build a strategy to target the appropriate market according to their products and solutions. E-commerce giants like Amazon use predictive analytics for product recommendation based on the customer’s website search patterns.
Fraud Analysis - With the large pool of data, there has been a broader ground created for the cyber security threats. The companies are adopting the predictive analytics tool to identify the vulnerability in their systems and to protect the possible data breaches. For instance, PayPal uses this tool to protect its customers against frauds. It fetches the past transaction pattern of the clients, such as their payment history, device used, location, etc. into machine learning algorithms to detect and analyze fraudulent activities in every future transaction.
Workforce Management - Predictive analytics is creating a vital impact on Human Resource or Workforce Management. It is being widely adopted in workforce for core processes such as talent acquisition, employee sentiment analysis, capacity planning, and attrition risk management. Predictive Analytics World for Workforce*, a conference scheduled in May (14th-18th), 2017, will cover predictive solutions for workforce optimization challenges. The conference will feature analytics professionals, HR Specialists / Officers, business leaders, IT specialists, etc. and will highlight the application and impact of predictive analytics in workforce.
*Please use the following code while registering for a PAW event to get a discount of 15% off the price of registration for 2 Day and Combo passes: GMSBP17
Risk Reduction - This is the prime application of predictive analytics, where credit scores for an individual is determined by using predictive analytics tool. Banking, real-state, and insurance are some of the major application sectors determining the lending risks associated with specific customers. Insurance companies are also using this tool to determine premium values for the customers, based on their life expectancy. For instance, Liberty Mutual, extend premium offers to its customer by extracting the information about their lifestyle, medical conditions, etc. to predict their probable lifetime.
How predictive analytics is creating impact on businesses across industries?
Retail - Predictive analytics is transforming the retail space by building strategic models based on the predictions derived from customers’ behavioral patterns. Predictive analytics is being widely adopted by the retail companies to launch suitable marketing campaigns, set optimal pricing of the products, identifying the right customer base, facilitate social media penetration, enhance sales, and develop a profitable inventory model. As an example of predictive analysis application, retail companies conduct “sentiment analysis”, by analyzing the customer’s perspective about a product through machine learning algorithms. The predictions based on this result are used by the companies to launch appropriate marketing strategies and determine the top selling product over the coming years.
BFSI - BFSI is one of the topmost industries embracing the predictive analytics tool as a vital part of its business process. The reason is quite justified by the large amount of data, money, assets, and customers associated with this sector. Even the slightest system vulnerability can result in a tremendous price to be paid by the organizations. Predictive analytics is being widely used by the Banking and Financial services industry to detect and prevent fraudulent activities (Fraud Analytics), reduce credit risks (Risk Analytics), and retain customers (Customer Analytics). For instance, Commonwealth Bank uses the SAS predictive analytics model to detect fraudulent activities for any transaction prior to its authorization, within 40 milliseconds of the transaction start.
Manufacturing - Degrading quality, resource wastage, reduced productions are some of the major issues addressed by the deployment of Predictive Analytics in the manufacturing space. Manufacturing is estimated to generate almost 1/3rd of the overall data gathered by the various sectors. The predictions derived from this data helps the manufacturing industry to improve the production quality, forecast the demand, design an efficient supply chain model, proper machine utilization, and prevent machine failure by alerting about the maintenance requirement from various machines. Predictive Analytics World for Manufacturing**, a conference to be held in Chicago from June 19-22, 2017, will cover the latest trends and predictive analytics adoption in manufacturing space. The conference will highlight forecasting, supply chain connectivity, cost and price modeling, text mining for product development, warranty data analytics, fault detection, visualization and dashboards, failure prediction, in-process verification tools, machine system/sensor health, product data mining, big data generation and management, process monitoring and correction, and the Internet of Things (IOT).
**Please use the following code while registering for a PAW event to get a discount of 15% off the price of registration for 2 Day and Combo passes: GMSBP17
Healthcare - Information management is the healthcare industry is becoming a top priority with an aim to derive actionable insights from huge healthcare data, which exceeded 187 thousand petabytes in 2015. The increasing healthcare expenditure and increasing digitization in the healthcare industry demands for huge adoption of predictive analytics in this space to crucially handle the patient data. According to Global Market Insights, Inc. “Healthcare Predictive Analytics market, worth USD 1 billion in 2015, is likely to witness a substantial growth over the coming years.” Read more at https://www.gminsights.com/industry-analysis/healthcare-predictive-analytics-market .
Government - Government and Public Sector is witnessing huge adoption of advanced technologies over the past few years, in turn resulting in large database. This sector is significantly embracing predictive analytics to enhance service & performance and meet evolving customer needs. Moreover, rising security threat is also boosting the predictive analytics deployment in the government sector.
Oil & Gas / Energy - Performance enhancement, optimal resource utilization, reducing risks, equipment functioning and maintenance, etc. are few of the major areas where predictive analytics is influencing the energy sector.
According to the studies, the average ROI for companies who incorporate Predictive Analytics tool is almost double to that of the companies who don’t. Predictive analytics is reinventing the business world not only by facilitating the performance of the industries, but also by enabling the sectors to take data-driven strategic decisions rather than once based on assumptions or gut-feel. The growing trend of IOT and the envisioning of the connected era are also poised to influence the predictive analytics deployment trends over the coming years.
Having said so, predictive analytics is on the verge of becoming a prioritized tool for every sector, if not yet incorporated.
Special Offer : Predictive Analytics World is the leading cross-vendor event for predictive analytics professionals, managers and commercial practitioners. Please use the following code while registering for a PAW event to get a discount of 15% off the price of registration for 2 Day and Combo passes: GMSBP17 – visit http://www.predictiveanalyticsworld.com/ .