Capabilities Apollo and SQL Server Data Mining Presented
Capabilities Apollo and SQL Server Data Mining Presented by Jeff Kaplan, Principal Client Services Paul Bradley, Ph. D. , Principal Data Mining Technology 312. 787. 7376 1
Agenda Apollo Overview Data Mining 101 Project REAL Case Study SQL Server 2005 Data Mining Demo Real-life Examples 2
PART ONE Apollo Overview 3
overview Company Background First company delivering true predictive analytic solutions 10 plus years in data mining and data warehousing Premier Partner for SQL Server 2005 Data Mining Cater to a wide range of business including Microsoft, Sprint, Wal-Mart, Barnes & Noble, Seattle Times, Knight Ridder Variety of Industries • Retail and Consumer Goods • Media • Financial Services • Manufacturing • Public Services 4
overview Industry Recognition 5
overview Testimonials 6
overview Testimonials 7
overview Testimonials 8
overview Analytic Landscape 9
overview Capabilities Sales & Distribution Marketing Operations Market Research • Customer Acquisition • Inventory Forecasting • Correlation Analysis • Claim Analysis • Campaign Targeting • Sales Forecasting • Key Driver Analysis • Call Center Analytics • Cross-sell/Up-sell • Pricing Optimization • Verbatim Summarization • Data Warehousing • Customer Segmentation • Next Best Offer • Dashboard Reporting • Retention Modeling • Market Basket Analysis • Behavioral Targeting • Recency & Frequency • Personalization Modeling 10
overview Customer Targeting Models • Join Customer Data Sources • Run Predictive Algorithms • Score Model Results • Deliver Targeted Predictions Customer Clustering Models Red Card Phone Predictive Models Web Booking SQL-Server 2005 Call Center Automate Predictions for Targeting, Forecasting, Detection, etc. Email Dashboard & Ad-hoc Reporting Stores Direct Mail Measure Promotion Success 11
PART TWO MS Data Mining 12
ms data mining Background Fastest Growing BI Segment (IDC) • • Data Mining Tools: $1. 85 B in 2006 Predictive Analytic projects yield a high median ROI of 145% Uses • • • Marketing: Customer Acquisition and Targeting, Cross-Sell/Up-Sell Retail: Inventory Forecasting, Price Optimization Market Research: Driver Analysis, Verbatim Summarization Operations: Call Center Analytics Finance: Fraud Detection, Risk Models Mainstream Emergence • • • E-commerce (e. g Amazon. com) Search (e. g. Vivisimo. com) Behavioral Advertising SQL-Server is in a Unique Position to Service Market Needs 13
ms data mining Evolution of SQL Server Data Mining 00 0 2 L SQ Enter the Game l Create industry standard l Target developer audience l V 1. 0 product with 2 algorithms 05 0 2 L SQ Win Leadership l Continue standards and developer effort l Comprehensive feature set l Penetrate the Enterprise l Thought leadership 14
Relative Business Value ms data mining Value of Data Mining Business Knowledge SQL-Server 2005 Data Mining OLAP Reports (Adhoc) Reports (Static) Easy Difficult 15
ms data mining Reporting Services Analysis Services OLAP & Data Mining Integration Services ETL SQL Server Relational Engine Ma n a g e m e n t T o o l s Devel o p men t T o o l s SQL-Server 2005 BI Platform 16
ms data mining SQL Server 2005 BI Platform Embed Data Mining: Development Tool Integration • Make Decisions Without Coding • Customized Logic Based on Client Data • Logic Updated by Model Reprocessing – Applications Do Not Need to be Re. Written, Re-Compiled, and Re-Deployed Data Mining Key Points • Price Point to Achieve Market Penetration • Database Metaphors for Building, Managing, Utilizing Extracted Patterns and Trends • APIs for Embedding Data Mining Functionality into Applications 17
ms data mining SQL-Server 2005 Algorithms Decision Trees Clustering Sequence Clustering Time Series Association Linear and Logistic Regression Neural Net Naïve Bayes 18
PART THREE Project REAL 19
project real Client Profile – Inventory Forecasting • Create a Reference Implementation of a BI System Using Real Retail Data. • Partners - Barnes & Noble, Microsoft, Scalability Experts, EMC, Unisys, Panorama, Apollo • Forecast Out-of-Stock for 5 Book Titles Across Entire Chain (800 Stores) • Predictive Models to Flag Items That Are Going to be Out-of-Stock • Model on 48 Weeks of Data, Predictions for Month of December • Models Predicted Out-of-Stock Occurrences > 90% Accuracy • Conservative Sales Opportunity for just 5 Titles: $6, 800 per year • Extrapolate Across Millions of Titles - Million Dollar Sales Opportunity 20
project real Predictive Modeling Process STEP 1 STORE + ITEM STEP 2 Identify the cluster which the store belongs to, for the category of that item. STEP 3 Utilize sales data predict item sales 2 weeks out. Each item belongs to a category CATEGORY For the category, create a set of store clusters predictive of sales in the category 21
project real Store Clustering Demo 22
project real Out-of-Stock Data Preparation Summary Apollo Explored 3 Data Preparation Strategies 1. Use Sales, On-Hand, On-Order History Data for All Stores in the Same Cluster Build One Mining Structure per Cluster, For All Stores in that Cluster for Each Title Build One Mining Model per Store, per Cluster for Each Title Negative: Few OOS Examples per Store, Computation to Deploy One Mining Model per Store/Title Combination 2. Use Sales, On-Hand, On-Order History for All Stores, Across All Clusters Build One Mining Structure per Book, Use Cluster Membership of Store as Input Attribute Positive: Optimizes OOS Examples per Title by Considering All Stores Negative: Does Not Capture Derivative Sales Information 3. Removed Negative of Strategy 2 Included Historical Week-on-Week Sales Derivative Information for Each Title Increase the Information Content of the Source Data for Modeling 24
project real Creating Variables for Success Using: • • Sales and Inventory History from January 2004 to end of November 2004 Recommend two (2) years of Historical Data to Increase accuracy for training model Key: • Store + Fiscal Year + Week. ID Predicted Variables • • 1 Week Ahead OOS Boolean 1 Week Ahead Sales Bin (None, 1 to 2, 3 to 4, 4+) 2 Week Ahead OOS Boolean 2 Week Ahead Sales Bin (None, 1 to 2, 3 to 4, 4+) Input Attributes • • Store Cluster Membership (Derived from Store Cluster Model) Current Week Sales, On-Hand, On-Order Preceding 1 -5 Week Sales, On-Hand, On-Order Sales Derivative Atttributes 25
project real Model Training and Testing Scenarios Purpose: Intelligence on Model Training Frequency • Scenario 1: Train Models Every 2 Weeks Training Dataset: All Data Prior to Last 2 Fiscal Weeks in December 2004 Test Dataset: Last 2 Fiscal Week in December 2004 • Scenario 2: Train Models Monthly Training Dataset: All Data Prior to End of Fiscal November 2004 Test Dataset: Fiscal Month of December 2004 26
project real Balancing Training Data When Considering All Stores, Still Have Un-Balanced Datasets • [# Store/Week Combinations Where OOS is False] >> [# Store/Week Combinations Where OOS is True] • Common in Many Data Mining Applications Training Datasets were Balanced • Sample Store/Week Combinations Where OOS is False to Obtain Equal Proportion of True/False Values “Cost” of Predictive Errors are Equal • Requested by Client 27
project real Prediction Methods Algorithm Selection Microsoft Decision Trees for Predicting OOS Boolean flags Consistently High Overall Accuracy Straightforward Interpretation Data Preparation • Scenario 2 • Rebuild models monthly Predictive Models are Contextual and Optimized for Behavior in the Coming Month 28
project real Prediction Methods Modeling Methodology Benefits • Scalability (Titles and Stores) • Saves 4 x to 5 x on Computational Cost when Rebuilding Models (versus Neural Networks) 5 Minutes for All 5 Titles => 1 Minute per Title for All Stores 29
project real Out-of-Stock Prediction Demo 30
project real Inventory Prediction Results 1 week and 2 week prediction accuracies 32
project real Sales Opportunity Data Mining created revenue generating opportunity Based on 55 titles for Jan 2004 - Dec 2004 • • • (# of weeks OOS across all stores)(Apollo Boolean Predicted Accuracy) X (actual % of actual sales across all stores) x (retail price) = Yearly Increase in Sales Opportunity using Apollo OOS Predictions Sales bins produced $3. 4 K, $6. 8 K potential lift in sales 33
PART FOUR Client Profiles 34
client profiles Client Profile – Customer Acquisition • Decrease Subscriber Churn • Increase New Subscriptions • Segment Geo-Demographic and Attitudinal Behaviors for Subscribers and Non-Subscribers • Build Predictive Models to Identify Likely New Subscribers • Using Analysis to Deliver Targeted Marketing Campaigns for Acquisition • Increased Stop Saves by 2% 35
client profiles Client Profile – Cross sell / Up sell (Global Catalog Retailer) • Increase Average Purchase Size • Deploy Product Recommendations on their Website • Modeling Historical Sales to Determine Product Affinities • Incorporate Business Logic into Modeling Process (e. g. Same category recommendation) • Increase Average Shopping Cart Size • Increase Sales Lift • Data Mining Driven Product Recommendation Performed Better than Manual Recommendations 36
client profiles Client Profile – Customer Support Automation • Increase Visibility into Customer Service Center • Increase Speed of Customer Support • Utilizing Text Mining Engines to Automate Processing of Customer Support (Email, Web Inquiries, etc. ) • Automating the Process of Rolling up Keywords into Concepts • Customer Support Center has the Ability to View Trends in Minutes versus Weeks • Improved Accuracy - Text Mining Engines Removed the Bias and Inaccuracies Often Occurring in Call Center Representative Notes and Tagging. 37
client profiles Client Profile – Key Driver Analysis • Evaluate Customer Satisfaction Metrics • Increase Customer Satisfaction • Partnered with Apollo to Develop Market Research Database and Reporting • Developed Models to Identify “Key” Satisfaction Drivers • Successfully Identified Drivers to Increase Customer Satisfaction • Delivered Driver Recommendations to Field Operations - Insight into Action • Company Wide (sales, marketing, executive level) Visibility into Customer Satisfaction Metrics 38
Presented by Jeff Kaplan Principal Client Services jeff@apollodatatech. com 312. 787. 7376 39
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