1. Loss Prevention, Auditing & Safety Conference 2009 Title Sponsor:

2. Data Sharing: Myth or Fact? Basia Pietrawska Research Analyst CAP Index Claude Verville VP of Loss Prevention Lowe’s David Johnston Business Development LP Innovations

3. Outline  Data Sharing and Pooling  Bridge Between Science and Business  Shrink Classification and Analysis Today and Tomorrow  Introduction to the National Shrink Database  Benchmarking Value of the National Shrink Database  The Future of the National Shrink Database  Conclusion

4. The Quest for Knowledge  Today’s retail environment requires the ability to gather information quickly, efficiently and effectively to make actionable business decisions.  It is our nature to gather information (data) and share ideas, but the goal is to seek knowledge.  Through knowledge, we can formulate strategies, build models and increase our ability to make better decisions. “Information is not synonymous with knowledge. Information is only data, parts of the whole.” – Philosopher Ruth Nanda Anshen “Information is not synonymous with knowledge. Information is only data, parts of the whole.” – Philosopher Ruth Nanda Anshen

5. Data Sources – Store Analysis Shrink History Inventory Variances Incident Data Physical Security Financial Variances HR Information Risk Assessments Insurance Reports INTERNAL SOURCESEXTERNAL SOURCES

6. Data Sharing  In our industry today  Conferences  Peer Groups  Mutual Associations  LPRC  LERPnet Pooling  Used throughout our world to make decisions using varied and collective resources.

7. Concerns with Data Sharing  Competitive Intelligence  Anonymity  Data Security  Difficulty in Providing Necessary Data  Shared Contribution (evenness of data input)  Legal / Liability  Return on Investment

8. Benefits of Data Pooling  Increased Data Pool  Larger Database  More Reliable, Valid and Accurate Results  Establish Predictive Models and Profiles  Creation of Standards and Benchmarks “Information is a source of learning. But unless it is organized, processed, and available to the right people in a format for decision making, it is a burden, not a benefit.” – William Pollard, Former CEO of Servicemaster “Information is a source of learning. But unless it is organized, processed, and available to the right people in a format for decision making, it is a burden, not a benefit.” – William Pollard, Former CEO of Servicemaster

9. Benefits of Data Pooling ZIP 37934 ZIP 86303 Each Robbery Robbery Data for Retailer A Only

10. Benefits of Data Pooling ZIP 37934 ZIP 86303 Pooled Robbery Data for Multiple Retailers Each Robbery

11. Benefits of Data Pooling ZIP 37934 ZIP 86303 Overlaid Risk Assessment Data Each Robbery

12. Bridge between Science and Business Science Business

13. Data Quality Considerations  Researcher’s Role:  Data Quality  Assessment  Improvement  Data Standardization  Definitions  Counting Rules  Comparison of “Apples to Apples” instead of “Apples to Oranges”

14. An Example of Shrink Classification and Analysis Today

15. Measuring & Validating Current Risk Levels Shrink Budgeting and Forecasting Note: Shrink %s are not actual Company numbers.

16. Expense Reduction Former New Store Site Survey Process  Latitude & Longitude are emailed to Field LP Personnel.  A site visit is conducted to access the physical location and environment to include:  Local Retailers & Businesses, Schools, Universities, Prisons, Bus Stops etc…  Shrink data, Countermeasure data and Shoplifting statistics are gathered where possible.  CAP Scores are acquired.  Applicable Shrink data is applied.  Site Survey “Formula” is calculated

17. Expense Reduction Former New Store Site Survey

18.  Provide Industry Benchmarking down to a location specific level to achieve actionable results.  Updated Information throughout the Year  Insight into the “Loss Prevention Landscape”  Specific Comparisons  Retail Segments  Specific Geographies “The goal is to transform data into information and information into insight” – Carly Fiorina, Former Chairwoman Hewlett-Packard “The goal is to transform data into information and information into insight” – Carly Fiorina, Former Chairwoman Hewlett-Packard The National Shrink Database®

19. Value to the Industry  Industry Analysis  Increased Understanding of Our Industry  Available to support Retailers and Solution Providers  Location Specific Analysis  Qualify Target Store Locations  Improve Budgeting  Justify Loss Prevention Expenditures  Improve Resource Allocation

20. NSD Quality Control Pooled Data Retailer C Retailer B Retailer A Creating the Value Data Results User Access Retailer B Retailer A Retailer C No Individual Specific Data

21. Nationwide Representation

22. National Shrink Database Location Search

23. National Shrink Database Search Results

24. Benchmarking Value of the National Shrink Database

25. “Retail Loss Prevention Landscape”

26. Evaluating Shrink Performance Retail Shrink by Sector

27. The Future of the National Shrink Database Shrink Security Measures Incident Data? National Shrink Database

28. Regional Statistics Census Region/Division State Major Metropolitan Areas Etc. New Development Incident Data Statistics by Category Loss Prevention Incidents Location-Specific

29. Data Sources – Store Analysis Shrink History Inventory Variances Incident Data Physical Security Financial Variances HR Information Risk Assessments Insurance Reports INTERNAL SOURCESEXTERNAL SOURCES

30. Data Sources – Store Analysis Shrink History Inventory Variances Incident Data Physical Security Financial Variances HR Information Risk Assessments Insurance Reports INTERNAL SOURCESEXTERNAL SOURCES National Shrink Database ?????????????

31. Data Pooling: Moving Us Forward (All) External Data Sources Internal Data Sources Decision-Making Analytics Predictive Modeling Benchmarking

32. Data Sharing: Myth of Fact? Questions Basia, Claude and David thank you for your time and attention. Enjoy the conference.