THE PREDICTIVE MODELLING PROCESS
|
|
- Rhoda Harrington
- 8 years ago
- Views:
Transcription
1 THE PREDICTIVE MODELLING PROCESS Models are used extensively in business and have an important role to play in sound decision making. This paper is intended for people who need to understand the process for developing predictive models because they interface in some way with technical analysts. This could be as a business user who interacts with the analytics team, a person involved with the preparation of data sets or a user of the outputs from a modelling process. This paper does not instruct people on how to build models, but covers the steps involved and the practical issues to consider. WHAT IS A MODEL? A mathematical model is an expression of relationships between variables, frequently in the form of an equation. An equation is an expression that contains an equal sign (=). To many the concept of an equation, or model, seems more complex than it actually is. The model itself is not complex, complexity arises from the process and work required to generate a good model. Consider the following, R = P x Q, where R is the revenue, P is the price and Q is the quantity sold. This equation can be used to calculate revenue by multiplying the price by the quantity sold. The equation defines mathematically the relationship between the two variables Q and R. That is, given the price of a product we can calculate R for any value of Q. This is the basis for mathematical models. Equations (such as the one above) can be represented by a straight line on a graph and are referred to as linear equations. In real life relationships between variables are more complex, to represent this complexity in equations, variables are squared, rooted and cubed etc. These equations are referred to as non-linear since they cannot be represented by a straight line. This paper focuses on the approach required to develop sound predictive models. Predictive modelling is the process of using past data to determine statistically, what is likely to happen in the future on the assumption that past trends will continue to apply. One application is in the forecasting area: predicting what will happen in the short to medium term future, in order to take pre-emptive measures such as allocating resources. Another use of predictive modelling is to identify individuals who are most likely to respond positively to some intervention. They can then be THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 1
2 preferentially targeted to achieve maximum effect. Applications in the marketing arena include customer acquisition, customer retention and cross-selling. A key aspect of predictive modelling is the application of regular feedback, updates to reflect current conditions and maximise efficiency to ensure a model that can accurately predict an outcome for a customer base. STEPS IN PREDICTIVE MODEL DEVELOPMENT PLAN BUILD IMPLEMENT Define the objective Build model Apply model Create data sets Calculate a score Rank customers Validate model Drive initiatives Figure 1: steps in predictive model development Define the objective A clear specific objective for the model is required. Each model is developed for a specific purpose and cannot be used effectively in another situation. For example a model that predicts home loan customer churn cannot be used to predict credit card churn. An example of a clearly defined model objective contains the event or action that the model is to predict and the period it is likely to happen. For example, the objective could be to predict customers that are likely to churn their credit card within the next month. THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 2
3 Create data sets An important step in the modelling process is the creation of the dataset to use for the model development. Broadly the dataset covers behavioural, demographic, geographic and external eg competitor information or weather. Variables that are not included in the dataset will not form part of the prediction. Variables cover both static fields such as income and triggers such as change in spend. Both technical and business people need to be involved in the decisions regarding the contents of the dataset. Focus needs to be on the behavioural information as this is more powerful for predictions than demographic data. Consideration needs to be given to which customers to exclude from the model build process. Customers need to be excluded if they are going to impair the performance of the model. Potential exclusions include bad debt, staff and new customers as they have insufficient history. The actual exclusions applied relates to the specific purpose of the model. For example, if the model is to predict customers that are likely to become bad debts, bad debt customers would be included in the model! Build statistical model The model will be built using a sample from the data set created. This is the part that can be left with the technical analysts. The resulting model will contain a subset of the original list of variables considered for the model. This is ok, and happens because some of the variables considered for the model will be correlated with each other, for example floor number and height of building. Others will have been discarded as they add little or nothing to the model s predictive power. The building of statistical models is the domain of statisticians and the technical aspects are not covered in this paper. Calculate a score The model developed will be an equation that, when applied to the customer base, will allocate a score to each customer. The score represents a customers likelihood to do whatever the model is predicting. For example, predicting a customer s likelihood to churn within a month or predicting a customer s expected order value. THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 3
4 Validate model Typically models are validated against a hold out group. This group contains customers that have not been included in the development of the model. As such, they represent a group of previously unseen customers that are representative of the customer base. To achieve an accurate prediction of lift the hold out group must not be made up of the customers that for one reason or another have been excluded from the model development process. Figure 2: shows that by targeting the top 20% identified by the model, almost 40% of the targets the model is aimed at are actually identified. If left to a random selection only 20% would occur in 20% of the base. Apply the model The model will be built on a subset of data. Once the model is complete and has been validated the model will be run over the customer base. THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 4
5 Rank These scores allow a customer base to be ranked in order of the predicted score, such as from highest expected order value to lowest or from most likely to least likely to churn. In reality some customers will churn and some customers will not. Therefore, in absolute terms, these predictions will not be accurate! The ranked list provides a superb base upon which to vary the treatment, and therefore the level of service or marketing spend, to groups of customers. Other useful considerations Key issues that arise are how often to run the model across the base and score the customers. This will depend on the actions being taken as a result of the model and the speed of change within the customer base and market. For example, telecommunications is a faster moving industry than insurance. Once a model has been applied and actions are being taken there is a requirement for tracking and managing the interactions. For example, control files will be required to test initiatives and to test the model performance. The results from this will help determine how frequently the model needs to be refreshed. As a rule of thumb, a model needs to be reviewed, and possibly rebuilt, annually. THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 5
6 MEASURING EFFECTIVENESS The following provides a framework for monitoring the effectiveness of the model and communications. It is recommended that this is discussed and implications on implementation addressed during the model build process. Customer Base High Score Customers Lower Score Customers 90% for programme 10% control 10% control group 2 Tests the effectiveness of the communications Tests the accuracy of the model BUSINESS USER KEY INVOLVEMENT To summarise, the following are the key times that the business user needs involvement in the modelling process Communicating and verifying the purpose of the model Contributing to the list of variables for consideration in the model Identifying exclusions from the model build Organising the implementation of model related communicating and testing Socialising and on-boarding the use of models within the business Being a sounding board and supporting the analysts with their endeavours! THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 6
7 BRIEF AUTHOR BIOGRAPHY Sally Carey Sally is Director of Datamine Ltd, a New Zealand based analytics consultancy that moves its clients beyond guesswork. Sally has over 25 years of B to B and B to C marketing and using quantitative approaches for business decision making. Sally has an MBA from Bradford University (UK) and is a Fellow of the Institute of Direct & Digital Marketing (UK). Sally believes that extraordinary results are achieved by a combination of analysis and intuition, and have even been referred to by some clients as magic. Key words: analytics, predictive modelling, process, model, equation THE PREDICTIVE MODELLING PROCESS SALLY CAREY DATAMINE LTD 7
KEEPING CUSTOMERS USING ANALYTICS
KEEPING CUSTOMERS USING ANALYTICS This paper outlines a robust approach to investigating and managing customer churn for those in the business-to-consumer market. In order to address customer retention
More informationCREATING CUSTOMER INSIGHT
CREATING CUSTOMER INSIGHT Organisations have discovered that the merging of their market research department with their database marketing team has not necessarily led to a stream of insights that revolutionise
More informationPast, present, and future Analytics at Loyalty NZ. V. Morder SUNZ 2014
Past, present, and future Analytics at Loyalty NZ V. Morder SUNZ 2014 Contents Visions The undisputed customer loyalty experts To create, maintain and motivate loyal customers for our Participants Win
More informationDISCOVER MERCHANT PREDICTOR MODEL
DISCOVER MERCHANT PREDICTOR MODEL A Proactive Approach to Merchant Retention Welcome to Different. A High-Level View of Merchant Attrition It s a well-known axiom of business that it costs a lot more to
More informationMathematics Online Instructional Materials Correlation to the 2009 Algebra I Standards of Learning and Curriculum Framework
Provider York County School Division Course Syllabus URL http://yorkcountyschools.org/virtuallearning/coursecatalog.aspx Course Title Algebra I AB Last Updated 2010 - A.1 The student will represent verbal
More informationnot possible or was possible at a high cost for collecting the data.
Data Mining and Knowledge Discovery Generating knowledge from data Knowledge Discovery Data Mining White Paper Organizations collect a vast amount of data in the process of carrying out their day-to-day
More informationAccurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios
Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are
More informationSTATISTICA. Financial Institutions. Case Study: Credit Scoring. and
Financial Institutions and STATISTICA Case Study: Credit Scoring STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table of Contents INTRODUCTION: WHAT
More informationCross Validation. Dr. Thomas Jensen Expedia.com
Cross Validation Dr. Thomas Jensen Expedia.com About Me PhD from ETH Used to be a statistician at Link, now Senior Business Analyst at Expedia Manage a database with 720,000 Hotels that are not on contract
More informationExperian s UK Credit Bureau Scores. Version 1.6
Experian s UK Credit Bureau Scores Version 1.6 January 2014 About Experian Decision Analytics Experian Decision Analytics enterprise-wide solutions combine data intelligence, predictive analytics, decisionenabling
More informationThe Value of Negative Credit Bureau Alerts to Credit Card Issuers
The Value of Negative Credit Bureau Alerts to Credit Card Issuers Authors: Chris Slater & Nick Gudde Release Date: October 2012 About The International Risk Partnership The International Risk Partnership
More informationDetermine If An Equation Represents a Function
Question : What is a linear function? The term linear function consists of two parts: linear and function. To understand what these terms mean together, we must first understand what a function is. The
More informationChapter. Break-even analysis (CVP analysis)
Chapter 5 Break-even analysis (CVP analysis) 1 5.1 Introduction Cost-volume-profit (CVP) analysis looks at how profit changes when there are changes in variable costs, sales price, fixed costs and quantity.
More informationMONTE CARLO SIMULATION FOR INSURANCE AGENCY CONTINGENT COMMISSION
Proceedings of the 2013 Winter Simulation Conference R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds MONTE CARLO SIMULATION FOR INSURANCE AGENCY CONTINGENT COMMISSION Mark Grabau Advanced
More informationData Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
More informationAcquiring customers profitably. With Credit Bureau Scores
Acquiring customers profitably With Credit Bureau Scores Uncover the true face of new customers before it s too late In the current climate, characterized by tough competition and economic slowdowns, identifying
More informationCreating the customer experience
Creating the customer experience INSIGHT. EXECUTION. ADVANTAGE. Customer experience outsourcing that transforms business performance 3 Your customer management future 5 The Webhelp difference 8 Services
More informationImproving Demand Forecasting
Improving Demand Forecasting 2 nd July 2013 John Tansley - CACI Overview The ideal forecasting process: Efficiency, transparency, accuracy Managing and understanding uncertainty: Limits to forecast accuracy,
More informationApplied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets
Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification
More informationData Mining Algorithms Part 1. Dejan Sarka
Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses
More informationSection A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I
Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA - Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting
More informationwww.moxonsolutions.com
www.moxonsolutions.com Introduction Moxon Intelligence Systems is a specialist predictive analytics development company. We focus on delivering software, consulting and training solutions that enable the
More informationTHE THREE "Rs" OF PREDICTIVE ANALYTICS
THE THREE "Rs" OF PREDICTIVE As companies commit to big data and data-driven decision making, the demand for predictive analytics has never been greater. While each day seems to bring another story of
More informationThe Power of Predictive Analytics
The Power of Predictive Analytics Derive real-time insights with accuracy and ease SOLUTION OVERVIEW www.sybase.com KXEN S INFINITEINSIGHT AND SYBASE IQ FEATURES & BENEFITS AT A GLANCE Ensure greater accuracy
More informationHow Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK
How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK Agenda Analytics why now? The process around data and text mining Case Studies The Value of Information
More information4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4
4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Non-linear functional forms Regression
More informationA Primer on Forecasting Business Performance
A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.
More informationExploiting the Single Customer View to Maximise the Value of Customer Relationships
Exploiting the Single Customer View to Maximise the Value of Customer Relationships An Experian briefing paper focusing on consumer financial services Contents Executive summary... pg 2 Introduction...
More informationUsing reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management
Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Paper Jean-Louis Amat Abstract One of the main issues of operators
More informationIBM's Fraud and Abuse, Analytics and Management Solution
Government Efficiency through Innovative Reform IBM's Fraud and Abuse, Analytics and Management Solution Service Definition Copyright IBM Corporation 2014 Table of Contents Overview... 1 Major differentiators...
More informationLinear Approximations ACADEMIC RESOURCE CENTER
Linear Approximations ACADEMIC RESOURCE CENTER Table of Contents Linear Function Linear Function or Not Real World Uses for Linear Equations Why Do We Use Linear Equations? Estimation with Linear Approximations
More informationFORECASTING. Operations Management
2013 FORECASTING Brad Fink CIT 492 Operations Management Executive Summary Woodlawn hospital needs to forecast type A blood so there is no shortage for the week of 12 October, to correctly forecast, a
More informationThe Top 9 Ways to Increase Your Customer Loyalty
Follow these and enjoy an immediate lift in the loyalty of your customers By Kyle LaMalfa Loyalty Expert and Allegiance Best Practices Manager What is the Key to Business Success? Every company executive
More informationCredit Risk Analysis Using Logistic Regression Modeling
Credit Risk Analysis Using Logistic Regression Modeling Introduction A loan officer at a bank wants to be able to identify characteristics that are indicative of people who are likely to default on loans,
More informationAn Introduction to Survival Analysis
An Introduction to Survival Analysis Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda Survival Analysis concepts Descriptive approach 1 st Case Study which types
More informationanalytics stone Automated Analytics and Predictive Modeling A White Paper by Stone Analytics
stone analytics Automated Analytics and Predictive Modeling A White Paper by Stone Analytics 3665 Ruffin Road, Suite 300 San Diego, CA 92123 (858) 503-7540 www.stoneanalytics.com Page 1 Automated Analytics
More informationIncreasing Marketing ROI with Optimized Prediction
Increasing Marketing ROI with Optimized Prediction Yottamine s Unique and Powerful Solution Smart marketers are using predictive analytics to make the best offer to the best customer for the least cost.
More informationBANKING ON CUSTOMER BEHAVIOR
BANKING ON CUSTOMER BEHAVIOR How customer data analytics are helping banks grow revenue, improve products, and reduce risk In the face of changing economies and regulatory pressures, retail banks are looking
More informationEasily Identify Your Best Customers
IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do
More informationHow To Understand The Role Of A Crom System
May 2012 The promise of CRM Type the words Promise of CRM into Google and you ll find that industry experts have been bemoaning CRM s failure to deliver on its promises for more than a decade. And yet,
More informationSuperior consumer intelligence for your competitive advantage
Superior consumer intelligence for your competitive advantage Delivering superior insight into the behaviour of your customers. The quality of our data means you can make more informed decisions and improve
More informationWhite Paper. Data Mining for Business
White Paper Data Mining for Business January 2010 Contents 1. INTRODUCTION... 3 2. WHY IS DATA MINING IMPORTANT?... 3 FUNDAMENTALS... 3 Example 1...3 Example 2...3 3. OPERATIONAL CONSIDERATIONS... 4 ORGANISATIONAL
More informationThe Connected Consumer Survey 2015: Fixed Broadband Customer Retention
Brochure More information from http://www.researchandmarkets.com/reports/3098986/ The Connected Consumer Survey 2015: Fixed Broadband Customer Retention Description: Service bundling is not a direct priority
More informationAlgebra 1 Course Information
Course Information Course Description: Students will study patterns, relations, and functions, and focus on the use of mathematical models to understand and analyze quantitative relationships. Through
More informationFactors Influencing Price/Earnings Multiple
Learning Objectives Foundation of Research Forecasting Methods Factors Influencing Price/Earnings Multiple Passive & Active Asset Management Investment in Foreign Markets Introduction In the investment
More informationTEXT ANALYTICS INTEGRATION
TEXT ANALYTICS INTEGRATION A TELECOMMUNICATIONS BEST PRACTICES CASE STUDY VISION COMMON ANALYTICAL ENVIRONMENT Structured Unstructured Analytical Mining Text Discovery Text Categorization Text Sentiment
More informationThe Advantages of Predictive Analysts
management+business Predictive Analytics ORGANISATIONS CAN TURN OPERATIONAL DATA INTO VALUABLE ACTIONABLE INSIGHTS BY USING PREDICTIVE ANALYTICS AND GOING BEYOND CONVENTIONAL BUSINESS INTELLIGENCE. Patricia
More informationADVANCED MARKETING ANALYTICS:
ADVANCED MARKETING ANALYTICS: MARKOV CHAIN MODELS IN MARKETING a whitepaper presented by: ADVANCED MARKETING ANALYTICS: MARKOV CHAIN MODELS IN MARKETING CONTENTS EXECUTIVE SUMMARY EXECUTIVE SUMMARY...
More informationCallidus for Insurance
White Paper Callidus for Insurance From Producer On-boarding to Pay for Performance: The Need for an Integrated Insurance Suite Does your organization have multiple legacy systems? How long does it take
More informationDigging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA
Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of
More informationSolve Your Toughest Challenges with Data Mining
IBM Software Business Analytics IBM SPSS Modeler Solve Your Toughest Challenges with Data Mining Use predictive intelligence to make good decisions faster Solve Your Toughest Challenges with Data Mining
More information01 In any business, or, indeed, in life in general, hindsight is a beautiful thing. If only we could look into a
01 technical cost-volumeprofit relevant to acca qualification paper F5 In any business, or, indeed, in life in general, hindsight is a beautiful thing. If only we could look into a crystal ball and find
More informationWhitepaper. Power of Predictive Analytics. Published on: March 2010 Author: Sumant Sahoo
Published on: March 2010 Author: Sumant Sahoo 2009 Hexaware Technologies. All rights reserved. Table of Contents 1. Introduction 2. Problem Statement / Concerns 3. Solutions / Approaches to address the
More informationHow To Recruit For A Contact Center
How to Win the War for Contact Center Talent: Seven Secrets to Better Hiring A Business Optimization White Paper by: Kevin G. Hegebarth Vice President, Marketing HireIQ Solutions, Inc. 1101 Cambridge Square,
More informationCredit Scorecards for SME Finance The Process of Improving Risk Measurement and Management
Credit Scorecards for SME Finance The Process of Improving Risk Measurement and Management April 2009 By Dean Caire, CFA Most of the literature on credit scoring discusses the various modelling techniques
More informationCustomer Lifecycle Management How Infogix Helps Enterprises Manage Opportunity and Risk throughout the Customer Lifecycle
Customer Lifecycle Management How Infogix Helps Enterprises Manage Opportunity and Risk throughout the Customer Lifecycle Analytics can be a sustained competitive differentiator for any industry. Embedding
More informationA NEW METHOD FOR DEVELOPING THE MOST COST- EFFECTIVE REHABILITATION PROGRAMS FOR OUR AGEING SEWER NETWORKS
A NEW METHOD FOR DEVELOPING THE MOST COST- EFFECTIVE REHABILITATION PROGRAMS FOR OUR AGEING SEWER NETWORKS Introduction Toby Bourke (MWHSoft), Graham McGonigal (MWH) There is currently widespread concern
More informationTelecommunications Overview. Enhance customer loyalty with customer-centric communications and interaction
Telecommunications Overview Enhance customer loyalty with customer-centric communications and interaction Communications Service Providers face many challenges with requirements to provide diversified
More informationOPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING
OPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING 1 Introductions An insights-driven customer engagement firm Analytics-driven Marketing ROI focus Direct mail optimization 1.5 Billion 1:1 pieces
More informationCORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREER-READY FOUNDATIONS IN ALGEBRA
We Can Early Learning Curriculum PreK Grades 8 12 INSIDE ALGEBRA, GRADES 8 12 CORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREER-READY FOUNDATIONS IN ALGEBRA April 2016 www.voyagersopris.com Mathematical
More informationSolve your toughest challenges with data mining
IBM Software IBM SPSS Modeler Solve your toughest challenges with data mining Use predictive intelligence to make good decisions faster Solve your toughest challenges with data mining Imagine if you could
More informationQlikView for Telecommunications. Delivering Unprecedented Customer Intelligence
QlikView for Telecommunications Delivering Unprecedented Customer Intelligence QlikView for Telecommunications: Delivering unprecedented Customer Intelligence Collaboration, visibility and efficiency:
More informationWhat is Modeling and Simulation and Software Engineering?
What is Modeling and Simulation and Software Engineering? V. Sundararajan Scientific and Engineering Computing Group Centre for Development of Advanced Computing Pune 411 007 vsundar@cdac.in Definitions
More informationDeepening Member Relationships With Big Data And Analytics
Deepening Member Relationships With Big Data And Analytics Rich Weissman President and CEO rich.weissman@dmacorporation.com Agenda Look at ways in which the industry traditionally goes about deepening
More informationA Property & Casualty Insurance Predictive Modeling Process in SAS
Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing
More informationINVENTORY MANAGEMENT, SERVICE LEVEL AND SAFETY STOCK
INVENTORY MANAGEMENT, SERVICE LEVEL AND SAFETY STOCK Alin Constantin RĂDĂŞANU Alexandru Ioan Cuza University, Iaşi, Romania, alin.radasanu@ropharma.ro Abstract: There are many studies that emphasize as
More informationAsset Management Plan Overview
Council Strategy Asset Management Plan Overview City of Albany 2013 File Ref: CM.STD.6 Synergy Ref: NMP1331749 102 North Road, Yakamia WA 6330 Version: 25/06/2013 PO Box 484, ALBANY WA 6331 Tel: (08) 9841
More informationApplication of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America
Application of SAS! Enterprise Miner in Credit Risk Analytics Presented by Minakshi Srivastava, VP, Bank of America 1 Table of Contents Credit Risk Analytics Overview Journey from DATA to DECISIONS Exploratory
More informationTennessee Department of Education. Task: Sally s Car Loan
Tennessee Department of Education Task: Sally s Car Loan Sally bought a new car. Her total cost including all fees and taxes was $15,. She made a down payment of $43. She financed the remaining amount
More informationMBA Jump Start Program
MBA Jump Start Program Module 2: Mathematics Thomas Gilbert Mathematics Module Online Appendix: Basic Mathematical Concepts 2 1 The Number Spectrum Generally we depict numbers increasing from left to right
More informationconsumerlab Keeping Smartphone users loyal Assessing the impact of network performance on consumer loyalty to operators
consumerlab Keeping Smartphone users loyal Assessing the impact of network performance on consumer loyalty to operators An Ericsson Consumer Insight Summary Report June 2013 contents USER BEHAVIOR IS CHANGING
More informationPrescriptive Analytics. A business guide
Prescriptive Analytics A business guide May 2014 Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications
More informationIncreasing marketing campaign profitability with Predictive Analytics
Increasing marketing campaign profitability with Predictive Analytics Highlights: Achieve better campaign results without increasing staff or budget Enhance your CRM by creating personalized campaigns
More informationCredit Scoring: Data and Decisioning
Credit Scoring: Data and Decisioning What should scoring do for collections? : Best Practice in Credit Management & Collections Le Meridien Queen s Hotel, Leeds, Wednesday 19 th June 2002 Helen McNab SCOREPLUS
More informationForecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs
PRODUCTION PLANNING AND CONTROL CHAPTER 2: FORECASTING Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs
More informationGuide to Predictive Lead Scoring
Guide to Predictive Lead Scoring Lead scoring has an important role to play in modern B2B marketing and sales. It is a useful system for gauging a prospect s likelihood of buying, allowing salespeople
More informationAPPLICATIONS OF PERFORMANCE SCORING TO ACCOUNTS RECEIVABLE MANAGEMENT IN CONSUMER CREDIT John Y. Coffman * Gary G. Chandler
1 APPLICATIONS OF PERFORMANCE SCORING TO ACCOUNTS RECEIVABLE MANAGEMENT IN CONSUMER CREDIT John Y. Coffman * Gary G. Chandler Abstract Performance scoring offers credit grantors improved ability to manage
More informationBreakeven, Leverage, and Elasticity
Breakeven, Leverage, and Elasticity Dallas Brozik, Marshall University Breakeven Analysis Breakeven analysis is what management is all about. The idea is to compare where you are now to where you might
More informationHow To Improve Your Business Performance Through Predictive Analytics
Increasing Business Performance through Predictive Analytics Many companies already run well-controlled, lean processes and so they are increasingly turning to their data as a new means of competitive
More informationDefinition 8.1 Two inequalities are equivalent if they have the same solution set. Add or Subtract the same value on both sides of the inequality.
8 Inequalities Concepts: Equivalent Inequalities Linear and Nonlinear Inequalities Absolute Value Inequalities (Sections 4.6 and 1.1) 8.1 Equivalent Inequalities Definition 8.1 Two inequalities are equivalent
More informationApplication of Predictive Analytics to Higher Degree Research Course Completion Times
Application of Predictive Analytics to Higher Degree Research Course Completion Times Application of Decision Theory to PhD Course Completions (2006 2013) Rachna 1 I Dhand, Senior Strategic Information
More informationOBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS
OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS CLARKE, Stephen R. Swinburne University of Technology Australia One way of examining forecasting methods via assignments
More informationanalytics+insights for life science Descriptive to Prescriptive Accelerating Business Insights with Data Analytics a lifescale leadership brief
analytics+insights for life science Descriptive to Prescriptive Accelerating Business Insights with Data Analytics a lifescale leadership brief The potential of data analytics can be confusing for many
More informationGerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I
Gerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I Data is Important because it: Helps in Corporate Aims Basis of Business Decisions Engineering Decisions Energy
More informationBalance collections with retention for each customer. Decision Analytics for debt management in retail banking
Balance collections with retention for each customer Decision Analytics for debt management in retail banking Debt management for retail banking In the highly competitive retail banking environment, banks
More informationExploiting the Single Customer View to maximise the value of customer relationships
Exploiting the Single Customer View to maximise the value of customer relationships October 2011 Contents 1. Executive summary 2. Introduction 3. What is a single customer view? 4. Obstacles to achieving
More informationCoolaData Predictive Analytics
CoolaData Predictive Analytics 9 3 6 About CoolaData CoolaData empowers online companies to become proactive and predictive without having to develop, store, manage or monitor data themselves. It is an
More informationHow To Understand The Value Of Data Mining
Getting Real Business Value out of Oracle BI and Oracle Data Mining Antony Heljula Brendan Tierney Peak Indicators Limited Agenda Aim of Presentation About Oracle Data-Mining What you need to know about
More informationAdvanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously
More informationLead Scoring. Five steps to getting started. wowanalytics. 730 Yale Avenue Swarthmore, PA 19081 www.raabassociatesinc.com info@raabassociatesinc.
Lead Scoring Five steps to getting started supported and sponsored by wowanalytics 730 Yale Avenue Swarthmore, PA 19081 www.raabassociatesinc.com info@raabassociatesinc.com LEAD SCORING 5 steps to getting
More informationIndiana State Core Curriculum Standards updated 2009 Algebra I
Indiana State Core Curriculum Standards updated 2009 Algebra I Strand Description Boardworks High School Algebra presentations Operations With Real Numbers Linear Equations and A1.1 Students simplify and
More informationThis unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions.
Algebra I Overview View unit yearlong overview here Many of the concepts presented in Algebra I are progressions of concepts that were introduced in grades 6 through 8. The content presented in this course
More informationApplying the Principles of Business Intelligence to Improve Collections Performance. A Decision Analytics briefing paper from Experian
Applying the Principles of Business Intelligence to Improve Collections Performance A Decision Analytics briefing paper from Experian February 2007 Introduction Deploying Business Intelligence tools within
More informationChapter 7: Data Mining
Chapter 7: Data Mining Overview Topics discussed: The Need for Data Mining and Business Value The Data Mining Process: Define Business Objectives Get Raw Data Identify Relevant Predictive Variables Gain
More informationQUANTITATIVE METHODS FOR MANAGEMENT
3rd Term MBA-2016 QUANTITATIVE METHODS FOR MANAGEMENT COURSE OUTLINE 1. Introduction The main task of a manager is to make decisions. To make decisions managers need information. Information is spread
More informationTowers Watson pricing software
pricing software Adding value to the pricing of personal lines and commercial business s pricing software is used by 17 of the world s top 20 P&C insurers. pricing software Effective pricing is fundamental
More informationThree Ways to Improve Claims Management with Business Analytics
IBM Software Business Analytics Insurance Three Ways to Improve Claims Management with Business Analytics Three Ways to Improve Claims Management Overview Filing a claim is perhaps the single most important
More informationData Mining for Business Analytics
Data Mining for Business Analytics Lecture 2: Introduction to Predictive Modeling Stern School of Business New York University Spring 2014 MegaTelCo: Predicting Customer Churn You just landed a great analytical
More information