1 / 37

Capabilities Apollo and SQL Server Data Mining

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. Agenda. Apollo Overview Data Mining 101 Project REAL Case Study SQL Server 2005 Data Mining Demo Real-life Examples.

paul2
Download Presentation

Capabilities Apollo and SQL Server Data Mining

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CapabilitiesApollo and SQL Server Data Mining Presented by Jeff Kaplan, Principal Client Services Paul Bradley, Ph.D., Principal Data Mining Technology 312.787.7376

  2. Agenda • Apollo Overview • Data Mining 101 • Project REAL Case Study • SQL Server 2005 Data Mining Demo • Real-life Examples

  3. PART ONE Apollo Overview

  4. 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

  5. overview Industry Recognition

  6. overview Testimonials

  7. overview Testimonials

  8. overview Testimonials

  9. overview Analytic Landscape

  10. overview Capabilities Sales & Distribution Operations Marketing Market Research • Claim Analysis • Call Center Analytics • Data Warehousing • Dashboard Reporting • Inventory Forecasting • Sales Forecasting • Pricing Optimization • Next Best Offer • Market Basket Analysis • Recency & Frequency Modeling • Customer Acquisition • Campaign Targeting • Cross-sell/Up-sell • Customer Segmentation • Retention Modeling • Behavioral Targeting • Personalization • Correlation Analysis • Key Driver Analysis • Verbatim Summarization

  11. overview Customer Targeting Models • Score Model Results • Join Customer Data Sources • Run Predictive Algorithms • Deliver Targeted Predictions Red Card Customer Clustering Models Phone Predictive Models Booking SQL-Server 2005 Web Call Center Automate Predictions for Targeting, Forecasting, Detection, etc. Email Dashboard & Ad-hoc Reporting Stores Direct Mail Measure Promotion Success

  12. MS Data Mining PART TWO

  13. ms data mining Background • Fastest Growing BI Segment (IDC) • Data Mining Tools: $1.85B 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

  14. Win Leadership • Continue standards and developer effort • Comprehensive feature set • Penetrate the Enterprise • Thought leadership ms data mining Evolution of SQL Server Data Mining SQL 2005 SQL 2000 Enter the Game • Create industry standard • Target developer audience • V1.0 product with 2 algorithms

  15. Data Mining OLAP Reports (Adhoc) Reports (Static) ms data mining Value of Data Mining Business Knowledge SQL-Server 2005 Relative Business Value Easy Difficult

  16. Management Tools Development Tools Reporting Services Analysis Services OLAP & Data Mining Integration Services ETL SQL Server Relational Engine ms data mining SQL-Server 2005 BI Platform

  17. 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

  18. ms data mining SQL-Server 2005 Algorithms Decision Trees Clustering Time Series Neural Net Sequence Clustering Association Naïve Bayes Linear and Logistic Regression

  19. Project REAL PART THREE

  20. 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

  21. 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. Each item belongs to a category Category CATEGORY For the category, create a set of store clusters predictive of sales in the category STEP 3 Utilize sales data predict item sales 2 weeks out.

  22. project real Store Clustering Demo

  23. project real Out-of-Stock Data Preparation Summary • Apollo Explored 3 Data Preparation Strategies • 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 • 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 • 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 + WeekID • 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 4x to 5x 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

  31. 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 $3.4K, $6.8K potential lift in sales

  32. PART FOUR Client Profiles

  33. 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%

  34. 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

  35. 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.

  36. 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

  37. Presented by Jeff Kaplan Principal Client Services jeff@apollodatatech.com 312.787.7376

More Related