Peter Dailey – Retail Analytics.

1. Peter Dailey – Retail Analytics.

2. The Journey From: Business Analytics toRetail Analytics Cubewise Retail Analytics Division

3. Retail Environment • Retailers are facing new challenges in a changing world of Global Retailing and Consumer Awareness: • We have to deliver lower price points and gain a competitive edge over global and online retailers that were not part of the traditional Australian landscape • The net result is smaller Profit Margins and minimal Comparable Store Growth • Thus the level of risk in day to day operations is higher than it was in the past

4. What Counts • Requirements for decision making are becoming more detailed, more frequent, and more critical to success • BusinessAnalytics in today’s retail environment is essential for success - talent and gut instinct has driven success in the past and we still need this Art of Retailing • But it needs to be backed by a strong RetailAnalytics process to ensure the wins are bigger and the losses are minimised.

5. What’s the Difference? • Traditionally Business Analytics covered off the strategic decisions that had major effects on the profits of the business • Now there is an importance to everydaydecisions, thus the analysis has to be executed by Retailers in real time and In Season. This cannot be dependent on specialised Business Analysts, thence the need for Retail Analytics.

6. How does this apply in Merchandise • Product Lifecycle Management “Right Stock, Right Price, Right Place, Right Time” • Right Assortment • Predict Salesand manageStock Flow • EOL Management Effectively Clear Product – Accurately Manage Clearance Predict Sales – Reduce Sell Outs, Minimise Over-Stocks Right Product In the Right Stores – No Dead Stock

7. What makes analysing these Principals Difficult • Devil is in the detail • Massive data sets • Consolidating information masks issues • Time Constraints • Knowledge 2 billion data points

8. Causational Investigation • Top Down Analysis often identifies issues once they are big enough problems to be noticed, as the early results are masked by consolidation • We need to identify issues before they become larger and irreversible problems

9. In The Past the Answer was… • Exceptions • In todays environment the exceptions are too numerous • What ‘s needed is actions to address what is happening at the lowest levels, but is prioritised, manageable and gets substantial results.

10. The Ultimate Answer is: • Predictive Analytics • Season Decomposition • Store Clustering • Basket Analysis • Customer Segmentation

11. But Also…… • Is there something else….. • Something that can facilitate day to day decisions • Something that gives me more visibility in an operational sense • An On Demand, Action Initiating process to drive more Return On Inventory • The answer is yes…..

12. The Power of N Level • Consolidate the effect not the result, and do this from a Product / Location / Time intersection • Indicators built with business acumen to produce specific goals based on Product Lifecycle Management • These Rules at the lowest level can guide Top Down Analysis towards the issues in those hidden branches • Or instantly identify the biggest issues rather than servicing the loudest anecdotal issue.

13. Assortment Precision • The Problem • Not all the Retailers Products will be sold in all Stores. • Smaller or “Specialised” Stores carry smaller widths of product due to floor space restrictions or completely different products based on the customer segmentation. • Million Products and Hundreds of Stores • Retailers traditionally group stores into clusters (or Grades), and Product into Assortments (or Ranges) • Not only do these principals need to be executed down to Store Level but the mix of product within the Stores also has to make sense. • This is all planned before the season starts in the Assortment Plan, but the real challenge is how to manage this once your “In Season” with supply and demand pushing the bounds of Planning accuracy which was set up to 9 months in the past. Right Product In the Right Stores – No Dead Stock

14. Assortment Precision • The Answer • At Store and Product Level determine those products not working to plan and take corrective action. • Work to through a prioritised process to ensure the most valuable decisions are made first • Use TM1 rules to locate stock that isn’t as productive as planned and then take action to adjust the assortment to ensure maximum Return on Inventory.

15. The Calculation – Dead Stock • IF • Last 4 Weeks Sales Units <=0 • Product Status is “Active” • Stock On Hand Units> 0 • On Show Date + 35 days < Now • Add • Stock On Hand Retail

16. The Calculation – Excessive Stock Retail • IF • Weeks Cover Units > Benchmark by Department • Product Status is “Active” • On Show Date + 28 days < Now • Add • Stock On Hand Retail above the Benchmark Weeks Cover Retail

17. Demo • Top Down Example • Top X Reporting • Location Cluster

18. Top Down Analysis

19. Top X Reporting

20. Size Ratio Results

21. Location Cluster Split

22. Benchmarking - Traditional Sales Stock Weeks Cover Stock Turn

23. Benchmarking - PPM Dead Stock Excessive Stock

24. The Assortment Issue Dead Stock % of SOH

25. The Results • Assortment Precision • Ensure the Product Assortment is productive in each Store Cluster, by monitoring No Sales and Forward Weeks Cover • Price Optimisation • Monitor No Sales and Forward Cover, and use the pricing lever to drive demand • Clearance Profitability • Set benchmarks for clearing stock manage pricing decisions at a lower level, to avoid costly and unnecessary blanket decisions • Product Adoption Management • Analyse Sales of Product being introduced into the business to guide the future Product Planning process • Inventory Life Span Determination • Identify and action slow moving obsolete inventory (Aged Inventory)

26. The Results • Margin Preservation • Monitor margin fallouts actively to ensure margin goals are met • Quantification Revisions • Monitor forward cover to validate quantification decisions • Allocation Maximisation • Ensure the execution of allocations to Stores by monitoring Excessive Stock measures by Store at higher levels of the hierarchy • Size Ratio Optimisation • Revise execution of Ratio packs based on live stats on Sales and Stock Patterns built from size level up • Out of Stock Controls • Identify and action Stock shortages quickly by assessing the important issues rather than swelling inventory with blanket solutions