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Sharjah

Predictive Analytics

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Published in: Business | Business Strategy | Business Studies
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Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events.

Supriya / Abu Dhabi

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  1. MODULE 5 Predictive Analytics
  2. Predictive Analytics Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics Uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends.
  3. Predictive Analytics DATA Reporting/ Analysis What happened Why that happene ACTION Monitoring What is happening Predictive Analytics What is gong to Happen in future?
  4. Predictive Analytics Process Define Project Data Anal sis Statistics e In De loyment o e om orm
  5. Advantages of Predictive Analytics Detecting Fraud Improving Operations Optimizing Marketing Campaigns Reducing Risk
  6. Applications of Predictive Analytics Banking & Financial Services Retail Oil, Gas & Utilities Governments & Public Sector Health Insurance Manufacturing
  7. Predictive Models Using data to make decisions and to take actions using models that are empirically derived and statistically valid. It is a form of data mining technology that works by analyzing historical current data and generating a model to help predict future outcomes. Predictive models are created whenever data is used to train a predictive modeling technique. Data + predictive modeling technique = Predictive Models
  8. Principles of Predictive Models Definition & Support Principles 1. Principle of Similarity 2. Principle of Extensibility 3. Principle of Robustness 4. Principle of Fault Tolerance 5. 01 6. Principle of Completeness Interference Principles 1. Lack of Knowledge Principle 2. Lack of Concern Principle 3. Lack of Definition Principle 4. Lack of Engineering Principle 5. Lack of Responsibility Principle
  9. 1. Types of Predictive Models Logic Driven Predictive Models: Logic-Driven Models: based on experience, knowledge, and logical relationships of variables and constants connected to the desired business performance outcome situation. 2. Data Driven Predictive Models: Use data collected from many sources to quantitatively establish model relationships Influence Diagrams visually show how various model elements relate to one another
  10. Logic Driven Predictive Models Equlprt.r,t Cause poop 1. Cause Effect Problem 10
  11. 1. 2. Logic Driven Predictive Models Single-Period Purchase Decisions: One time purchasing decision (Examples: selling t-shirts at a football game, newspapers, fresh bakery products, fresh flowers) Seeks to balance the costs of inventory over stock and under stock Multiple Time Period Models: Fixed-Order Quantity Models Event triggered (Example: running out of stock) Fixed-Time Period Models Time triggered (Example: Monthly sales call by sales representative)
  12. 1. Data Driven Predictive Models Retail Pricing Markdown Model: Discount from a retail or selling price, creating sale or volume discount prices Discounted/Marked-down prices must still be above costs or we lose money on each sale! Markdowns are typically expressed as a percent Sale Price = Retail Price x (1 - Markdown % as a decimal) 12
  13. Data Driven Predictive Models 2. Modeling Relationships and Trends in Data: Linear function, y = a + bx Logarithmic Function, y = In(x) Polynomial Function, y = ax2 + bx + c Power Function, y = axP Exponential Function, y = abX
  14. 1. 2. 3. 4. 5. 6. Advantages of Predictive Models Higher Fault Tolerance & System Reliability Better Load Balancing Faster Error Diagnosis, Recovery and Error Aversion Deeper understanding of Business Objectives and Relationships Ability to Address and Answer Business Strategy Decisions Better & More Reliable Strategic Planning
  15. l. 2. 3. 4. Models Involving Uncertainty - DSS Decision Support Systems are computer-based information systems that provide interactive information support to managers and business professionals during the decision making process. DSSs use: Analytical models, Specialized databases, A decision makers own insights and judgments, An interactive, computer based modeling process to support semi structured business decisions.
  16. Models Involving Uncertainty - DSS Components of DSS: Legacy software Web browser User Interface Functions Other softwares Multimedia, 3D Visualization Model Management Functions Analytical Modeling, Statistical Analysis Data Functions Data extraction, valic.iation, Sanitation, integration, and replication Operation al Data M a rket Data Sales Data Customer 16
  17. l. 2. 3. 4. Models Involving Uncertainty - DSS A decision support system involves an interactive analytical modeling process. There are four basic types of analytical modeling activities are involved in using a DSS: What-if analysis , Sensitivity analysis, Goal-seeking analysis, and Optimization analysis. 17
  18. Models Involving Uncertainty - DSS Type of Analytical Activities and examples Modeling What-if analysis Sensitivity analysis Goal-seeking analysis Optimization analysis Observing how changes to selected variables affect other variables. Example: what if we cut advertising by 10%? What would happen to sales. Observing how repeated changes to a single variable affect other variabEs. Example: Let's cut advertising by 100% repeatedly so we can see its relationship to sales. Making repeated changes to selected variables until a chosen variable reaches to a target value. Example: Let's try increases in advertising until sales reach Finding an optimum value for selected variables, given certain constraints. Example: What's the best amount of advertising to have, our budget and choice of media?
  19. Analytics in Telecom The rapid rise in the use of smart phones and other connected mobile devices has triggered a spurt in the volume of data flowing through the networks of telecom operators. It is necessary that the operators process, store, and extract insights from the available data. Big Data analytics can help them increase profitability by helping optimize network usage and services, enhance customer experience, and improve security. Research has shown that the potential for telecom companies to benefit from Big Data analytics is substantial. The potential of Big Data, however, poses a challenge: how can a company utilize data to increase revenues and profits across the value chain, spanning network operations, product development, marketing, sales, and customer service. 19
  20. Analytics in Telecom Big Data analytics, for instance, enables companies to predict peak network usage so that they can take measures to relieve congestion. It can also help identify customers who are most likely to have problems paying bills as well as those about to change operators, thus exacerbating churn. Operators are usually advised against taking the usual top- down approach when it comes to Big Data analytics, which marks out the problem to be solved and then seeks out the data that may help resolve it. Instead, the operators should focus on the data itself, using it to make correlations and connections. If done correctly, the data could reveal insights that could form the basis of more streamlined operations. 20
  21. Analytics in Location based Intelligence Marketing • Location intelligence is a tool to analyze the spatial components of business data. LI is capable of combining business data with spatial data and running complex analytics to offer location-enabled business intelligence. It can be used to derive meaningful insights, discover relationships and identify trends. Technologies like the Internet of Things (IOT), autonomous vehicles and sensors are capturing information that has never been captured before, creating entirely new avenues for geospatial data collection. As a result, location-based analytics and platforms that can process and detect trends and provide intelligence are becoming more popular.
  22. Analytics in Location based Intelligence Marketing • Just to give a perspective, by 2020 there will be 20.4 billion connected "things" in use, up from 8.4 billion in 2017. This huge jump can be attributed to the growing popularity of smart connected devices or also be termed as the Internet of Things. • 10T is already creating a buzz and in days to come will lead to creation of an overwhelming amount of data about what we do, how we do it, and where it happens. Since everything happens somewhere, an enormous amount of the data that companies collect has a spatial component, collecting and analyzing this type of location data can be intimidating because it involves new methods, technology, and talent. 22
  23. 1. 2. 3. 4. 5. 6. 7. 8. Analytics in Consumer Packed Goods Direct Consumer Relationships Mobile & Location Based Services Demand Prediction Demand Driven Supply Chain Management Idea-to-Product Acceleration Store Clustering Price Optimization Shelf-Space Allocation 23
  24. 1. 2. 3. 4. 5. Analytics in Utilities Asset Management Reduce fraud and leakage using intelligence from smart devices Improve quality of service with predictive modeling of grid performance Improve drilling Improve operations
  25. 1. 2. 3. 4. 5. Analytics in Health Care Comparative Effectiveness Disaster Planning Patient Flow Radio Frequency Identification (R FID) Genetics 25
  26. 1. 2. 3. 4. 5. 6. 7. Analytics in Online Retail Optimizing Marketing Mix Customized Product Recommendations Effective Overall Supply Chain Predictive Search Recommendations & Promotions Fraud Management Business Intelligence 26

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