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Boost Merchandise Buy Accuracies with Predictive Analytics

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From the 5/4/2017 live webcast.

Published in: Retail
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Boost Merchandise Buy Accuracies with Predictive Analytics

  1. 1. Boost Merchandise Buy Accuracies With Predictive Analytics
  2. 2. Celect’s Purpose • Predictive analytics SaaS platform to help retailers optimize store assortments, overall inventory portfolios • We leverage a groundbreaking advance in Machine Learning and Optimization. • An MIT Artificial Intelligence Lab Top 50 Technology Innovation © 2017 Celect, Inc. Confidential. Do Not Distribute.2 $1 Trillion flows through the Inventory Value Chain each year
  3. 3. EXPECTATIONS HAVE CHANGED © 2017 Celect, Inc. Confidential. Do Not Distribute.3 Your customers expect everything, everywhere, all the time. LOOK FAMILIAR?
  4. 4. © 2017 Celect, Inc. Confidential. Do Not Distribute.4 DECISIONS HAVE BEEN MADE THIS WAY FOR YEARS 1980s 1990s 2000s Today
  5. 5. © 2017 Celect, Inc. Confidential. Do Not Distribute.5 1980s 1990s 2000s Today SO WHY CHANGE NOW?
  6. 6. PRESSURES I NTENSI F I ED INVENTORY CHALLENGES DEMAND UNCERTAINTY SHRINKING MARGINS / PROFITS LOST SALES © 2017 Celect, Inc. Confidential. Do Not Distribute.6
  7. 7. © 2017 Celect, Inc. Confidential. Do Not Distribute.7 DECISIONS ARE MADE BASED ON HISTORICAL TREND DATA, USING EXCEL AND GUT INSTINCT
  8. 8. A ‘Simple’ Example • $250M spent annually on Shoes • 100K Shoe ‘types’ or SKUs (style/color/size) • 52 Stores (with room for 5K Shoe SKUs) • 6M unique customers © 2017 Celect, Inc. Confidential. Do Not Distribute.8 An American Luxury Department Store Goal: Avoid Stock-outs and Markdowns Solution: True Demand Prediction
  9. 9. EXPECTATIONS HAVE CHANGED © 2017 Celect, Inc. Confidential. Do Not Distribute.9 Your customers expect everything, everywhere, all the time. YOUR JOB IS HARD
  10. 10. There’s a Better Way © 2017 Celect, Inc. Confidential. Do Not Distribute.10 Bring Science to the Art of Retail • Supplement your experiences, knowledge, and intuition with machine learning • Leverage the data you already have Predicting Customer Choice • How products interact and influence each other • Assortment Planning, Buy Accuracy, & Allocation
  11. 11. Predicting Customer Choice? © 2017 Celect, Inc. Confidential. Do Not Distribute.11 A Choice Model tells you what a customer would prefer to buy when given the choice. Today, you understand what your customer bought. ✔ ✗ ✗ What if you also knew what they didn’t buy, but had the option to? TLOG PRODUCTS BROWSE SHIPMENTS & RECEIPTS LOCATIONS INVENTORY
  12. 12. Predictive Analytics Impact on Buys • Planners and Buyers: Optimize buy quantities for new or reordered styles ‒ Input buy sheets to view demand predictions, optimized buy quantities, and ranking • Make sure those big buys sell big! ‒ Analyze the attribute coverage across the entire buy sheet, before and after the optimization ‒ See related styles and attribute rankings © 2017 Celect, Inc. Confidential. Do Not Distribute.12
  13. 13. • It all starts with your buy sheet. Your Buy Sheets, Optimized © 2017 Celect, Inc. Confidential. Do Not Distribute.13 Style Description, Class, Brand, VendorProduct Attributes (e.g. silhouette, material, sleeve length)Planned Number of ColorsPlanned Number of Stores and Date AvailablePlanned Buy Quantity and Target Sell-Through
  14. 14. Upload a Buy Sheet © 2017 Celect, Inc. Confidential. Do Not Distribute.14
  15. 15. Buy Optimization Output © 2017 Celect, Inc. Confidential. Do Not Distribute.15 Predicted Net Sales + RankOptimized Buy Quantity
  16. 16. Associated Products Details © 2017 Celect, Inc. Confidential. Do Not Distribute.16
  17. 17. Attribute Information Details © 2017 Celect, Inc. Confidential. Do Not Distribute.17
  18. 18. Attribute Coverage Details © 2017 Celect, Inc. Confidential. Do Not Distribute.18
  19. 19. Download Your Optimized Buy Sheet © 2017 Celect, Inc. Confidential. Do Not Distribute.19
  20. 20. Measuring Results of Buy Optimization • Compare Actual Regular Price Sales of styles to: ‒ Original Planned Buy Quantity • What Buyer would have done without Celect ‒ Celect’s Demand Prediction • What Celect predicted the demand would be © 2017 Celect, Inc. Confidential. Do Not Distribute.20 • Metrics measured: ‒ Markdown Units ‒ Lost Sales Units ‒ Markdown $ • Markdown Units * Cost ‒ Lost Sales $ • Lost Sales Units * (Retail Price – Cost) Note: One Lost Sale unit does not need to equate to one Markdown unit in $ value
  21. 21. Example 1: Savings in Markdown Units © 2017 Celect, Inc. Confidential. Do Not Distribute.21 Style: XYZ Retail Price: $68 Cost: $17 ORIGINAL PLANNED BUY QUANTITY CELECT’S DEMAND PREDICTION Buy Quantity 7,856 5,104 S/T Rate 64% 98% Markdown Units 2,848 96 Markdown $ $48,416 $1,632 Lost Sales Units 0 0 Lost Sales $ 0 0 $ Value Lost $48,416 $1,632 Celect saved $46,784 for this style Actual Reg. Price Sales Quantity: 5,008
  22. 22. Example 2: Savings in Lost Sales Units © 2017 Celect, Inc. Confidential. Do Not Distribute.22 Style: ABC Retail Price: $150 Cost: $49 Original Planned Buy Quantity Celect’s Demand Prediction Buy Quantity 4,135 2,511 S/T Rate 71% 117% Markdown Units 1,187 0 Markdown $ $58,163 0 Lost Sales Units 0 437 Lost Sales $ 0 $44,137 $ Value Lost $58,163 $44,137 Celect saved $14,026 for this style Actual Reg. Price Sales Quantity: 2,948
  23. 23. Celect Buy Optimization • Part of the Celect Optimization Platform • Use your own buy sheets • Instant optimized results using your product attributes ‒ Material, Silhouette, Color, etc. © 2017 Celect, Inc. Confidential. Do Not Distribute.23
  24. 24. Thank you! © 2017 Celect, Inc. Confidential. Do Not Distribute.24 Questions? info@celect.com More webcasts and information celect.com/resources Twitter Email us @celect todd@celect.com carrie@celect.com

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