Predictive Analytics With Oracle Data Mining

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1 1 Predictive Analytics With Oracle Data Mining How it can help to answer business questions? Presented by Gautham Sampath Innive Inc January 27, 2016

2 Welcome What we plan to discuss 2 Predictive Analytics a business perspective Datamining Predictive Analytics in Action Oracle Technical Footprint Demo Q & A

3 3 Meet Gautham Sampath Technology leader and entrepreneur About Gautham Leads Innive Inc., Oracle Partner, focused on business analytics, predictive modeling and big data analysis. Regular speaker at OOW, OAUG, BIWA, FORCES and other events about Analytics, OBIEE, Hyperion etc. On Oracle Advisory Board for BI & Big Data Instrumental in Pinellas County 2014 Achievement, Award in the category of Civic Education and Public Information for Financial Transparency Tampa Airport winning Oracle center of Excellence award for Innovation. Prince George County gaining status as Model school for K12 analytics Gautham Sampath Previously Chief Information Technologist, Pinellas County Chief Information Technologist, Tampa Airport Lead Implementer Hyperion, OBIEE and E Biz Suite Gautham Sampath Chief Executive Innive Inc gsampath@inniveinc.com

4 Predictive Analytics Some practical questions Katy Jones San

rate is this just a chance? Will our large customer stick with us?

year how likely are we to meet last year targets?

pre-med is likely to continue the course?

4 4 Predictive Analytics Some practical questions Katy Jones San Francisco, US We are launching this new product in Asia will the product succeed? Tomas Ellison Kentucky, US We have been experiencing a low churn rate is this just a chance? Will our large customer stick with us? Chris Stark Minnesota, US We are heading to holiday season this year how likely are we to meet last year targets?. Isaac Newhall Pittsburgh, US How much of the current batch of pre-med is likely to continue the course? What will be the demand for med school? Different folks.. different questions.. different industries.. but one common theme Can I predict my business outcome?

5 A Typical Business Problem We understand our past but what about the future?

. We have successfully cracked open the past.

We understanding what has changed and what has caused the change.

5 5 A Typical Business Problem We understand our past but what about the future? Past Cracked Open Future remains a closed shell.. We have successfully cracked open the past. We understand our past in greater details. We understanding what has changed and what has caused the change. But We have traditionally used our sense of judgement to predict the future. We continue to drive by trial and error with limited insights. We are yet to capitalize on the wealth of our data to achieve a data-driven

6 BI Evolution The Ladder to the Greatness D Foresight 6 Prescriptive What should I do about it?. C Predictive What is likely to happen? B Business Insight is evolving.. A Diagnostic Why is it happening? Most companies have reached diagnostic level in the continuum. Next step in the journey is Predictive Analytics Descriptive Understanding what drives the business and what influences it What is happening? Bringing machine learning and power of data analytics to predict what is likely to happen Hindsight Data mining allows data driven analytics discovers correlations not previously known or imagined

7 7 Predictive Analytics Data mining is core of predictive analytics. It all starts with understanding the data deeply Listen to what data says. Learn correlations and influencing factors Apply. Data Mining is knowledge discovery in data, discovering non-trivial, hidden and previously unknown information from large collections of data

8 8 Need for Data Mining Can humans not do a better job? Humans are good at spotting simple trends involving a small number of dimensions e.g. Age and Sex But humans find it difficult to: Find patterns when you need to analyze large amounts of data over many different dimensions Spot associations or affinities with probabilities for determining a level of confidence Data mining puts system power to use to analyze volumes of data with complex relationship Produces a repeatable model, capable of continuous learning and improvement

9 Predictive Analytics A data driven approach to

Reveals what factors influence to what extent

those factors Identifies all factors that influence

combined effect Builds on the driver based approach,

modeling and machine learning to provide a predicted

9 9 Predictive Analytics A data driven approach to predict future. Reveals what factors influence to what extent Predicts the factors and derives result based on those factors Identifies all factors that influence business Determines combination of factors and their combined effect Builds on the driver based approach, common in human forecasting, with statistical modeling and machine learning to provide a predicted result, with what-if capabilities. Continuously learns, tests and revises model to minimize margin of error

10 10 Data Driven Predictive Analytics in Action

11 Income 11 Predictive Analytics in Action Predicting Customer Churn - A conceptual flow and approach Concept for Demo Only Cell Phone Churners vs. Loyal Customers Step 1 Plots the customers churners vs loyalists - across the number of calendar months of their continuance with the company Customer Months Objective: To predict the level of churn to expect in future To predict if a specific customer is likely to continue or not..

12 Income 12 Predictive Analytics in Action Predicting Customer Churn - A conceptual flow and approach Cell Phone Churners vs. Loyal Customers Step 2 Evaluate relationship with a linear regression line Observation The method does not produce a line of best fit The model is not able to isolate the churners and loyalists effectively Decision: Need to build a different model Customer Months

13 Income 13 Predictive Analytics in Action Predicting Customer Churn - A conceptual flow and approach Cell Phone Churners vs. Loyal Customers Step 3 Test with a different model, developed with a different algorithm/ relationship analysis Observation The method is better than the first but still not good enough The model has not been able to identify the correlations effectively Customer Months Decision: Continue developing another model(s)

14 Income 14 Predictive Analytics in Action Predicting Customer Churn - A conceptual flow and approach Segment #3: IF CUST_MO > 7 AND INCOME < $175K, THEN Prediction = Cell Phone Churner, Confidence = 83%, Support = 6/39 Cell Phone Churners vs. Loyal Customers Step 4 Build a different model, with a combination of factors Observation The method is able to effectively identify the Churners from Loyalists The model may not be 100% accurate in real world scenario, we cannot expect 100% accuracy Customer Months Segment #1: IF CUST_MO > 14 AND INCOME < $90K, THEN Prediction = Cell Phone Churner, Confidence = 100%, Support = 8/39 Decision: Use this model for predictive analysis

15 Income 15 Predictive Analytics in Action Predicting Customer Churn - A conceptual flow and approach Segment #3: IF CUST_MO > 7 AND INCOME < $175K, THEN Prediction = Cell Phone Churner, Confidence = 83%, Support = 6/39 Cell Phone Churners vs. Loyal Customers Insight & Prediction Customer Months Segment #1: IF CUST_MO > 14 AND INCOME < $90K, THEN Prediction = Cell Phone Churner, Confidence = 100%, Support = 8/39 Step 5 Use model to predict future Churn rates as well as probability of continuance of any specific customer. Outcome Have a scientific method to predict Backward testing in any past period helps to continuously test and validate the model

16 Income 16 Predictive Analytics in Action Evaluating Prediction and Continuous Learning/Improvement Per Prediction vs. Failed Prediction Step 6 Validating predicted results from model vs actual result. Observation Except for 3 cases, others are compliant with the predictive analysis This is to be expected in any predictive model Customer Months Improvement Analyze the failed cases for the root cause to confirm they are not exceptions/ outliers. Refine model with new inputs.

17 17 Oracle support for Predictive Analytics

as part of Oracle Database Enterprise Edition, provides full support for datamining and

18 18 Oracle Advanced Analytics Comprehensive footprint to support predictive analytics Oracle provides comprehensive support for Predictive Analytics Oracle Advanced Analytics, provided as part of Oracle Database Enterprise Edition, provides full support for datamining and statistical methods. The addition of OBIEE for visualization enriches the output from Advanced Analytics

19 Oracle Data Mining Part of Advanced Analytics Database Option

All data stays in the same instance and used for datamining ODM

algorithms can help build complex analytical models to explain

19 19 Oracle Data Mining Part of Advanced Analytics Database Option Oracle Data Mining (ODM) is a component of the Oracle Database All data stays in the same instance and used for datamining ODM supports various algorithms to mine the historical data These algorithms can help build complex analytical models to explain your data The statistical models can then be used to help answer Business questions. Analytical Model Using ODM New Factors from Current Data Customer Service History No data movement outside the database Churn?

GROUP BY o ORDER BY o RANK Automated knowledge discovery, model building and deployment Domain expertise to assemble

20 20 Power of Oracle Transaction SQL plus Oracle Data Mining Transaction SQL Oracle Data Mining Human-driven queries Domain expertise Any rules must be defined and managed SQL Queries o SELECT o DISTINCT o AGGREGATE o WHERE o AND OR o GROUP BY o ORDER BY o RANK Automated knowledge discovery, model building and deployment Domain expertise to assemble the right data to mine ODM Verbs o PREDICT o DETECT o CLUSTER o CLASSIFY o REGRESS o PROFILE o IDENTIFY FACTORS o ASSOCIATE

21 21 Predictive Analytics with Oracle Options for Effective Use On Premises Part of Oracle Enterprise Database Hybrid Database On-prem + Advanced Analytics on Cloud Cloud Database, including Advanced Analytics on Cloud Companies can make use of the Cloud option to try out predictive analytics for their use cases After confirming applicability and value, decision for on-prem can be taken.

22 22 Predictive Analytics Use Case Demos K12 Analytics

23 K12 Early Warning System Heatmap of Students and their promotion probability Predict level of promotion for any year, at beginning of school year Provide visibility at individual

23 23 K12 Early Warning System Heatmap of Students and their promotion probability Predict level of promotion for any year, at beginning of school year Provide visibility at individual student level via Heat Map Allows school/ school district to devise programs to address students in Red and Yellow categories Target audience - Principals, School Administrators, Teachers.

are used as predictor variables Promotion status of each student is converted in to a categorical

24 24 K12 Early Warning System Sample code and approach Student information such as previous year GPA, attendance, SRI score etc. are used as predictor variables Promotion status of each student is converted in to a categorical variable with 1 and 0 as values. This categorical variable is then used as a dependent variable. Using logistic regression, promotion probability of the student is calculated.

25 K12 Early Warning System Beginning of Year + Quarterly Update The Early Warning provides heatmap and promotability of each student at beginning of school year.

25 25 K12 Early Warning System Beginning of Year + Quarterly Update The Early Warning provides heatmap and promotability of each student at beginning of school year. The school administration implements programs/ changes to specifically address students in yellow and red categories. Every quarter end, students are reevaluated for their updates status of profitability. School administrators compare baseline at beginning of the year, with quarterly updates for critical fall-outs. Specifically any change from green to yellow or yellow to red are closely scrutinized. Model is evaluated for refinement, based on material differences.

26 26 K12 Analytics Demo

27 27 Budget Forecasting Using OER Budget forecast system uses advanced analytics to estimate the budget for the future month/ year. Tampa budget estimate is the average budget calculated using three estimating Technique such as Linear Estimate, Average Percentage Estimate & Percentage UPTO-Date Estimate. All three estimate calculations are performed using Oracle R enterprise solutions and the results are stored in the datawarehouse

28 28 Budget Forecasting Using OER Linear Estimate: Identify the Slope and Intercept for the Budget against Month. Oct is referred as value 1 and September as 12, since October is the start of the budget month and September is the end of the budget month. Using Slope and Intercept linear estimate is calculated using the following formula. y= m*x + b y - Month Variable (1, 2, 3,... 12) x - Budget Amount b - Intercept m - Slope

29 29 Share Use Cases + Q&A Please share your use cases

30 30

Drive Better Insights with Oracle Analytics Cloud

Drive Better Insights with Oracle Analytics Cloud Thursday, April 5, 2018 Speakers: Jason Little, Sean Suskind Copyright 2018 Sierra-Cedar, Inc. All rights reserved Today s Presenters Jason Little VP of