boost merchandise buy accuracies with predictive analytics
TRANSCRIPT
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
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$1 Trillion flows through the Inventory Value Chain each year
EXPECTATIONS HAVE CHANGED
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Your customers expect everything, everywhere, all the time.
LOOKFAMILIAR?
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DECISIONS HAVE BEENMADE THIS WAY FOR YEARS
1980s 1990s 2000s Today
PRESSURESI N T E N S I F I E D
INVENTORY CHALLENGESDEMAND UNCERTAINTY
SHRINKING MARGINS / PROFITSLOST SALES
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DECISIONS ARE MADE BASED ON HISTORICAL TREND DATA, USING
EXCEL AND GUT INSTINCT
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
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An American Luxury Department Store
Goal: Avoid Stock-outs and Markdowns Solution: True Demand Prediction
EXPECTATIONS HAVE CHANGED
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Your customers expect everything, everywhere, all the time.
YOUR JOBIS HARD
There’s a Better Way
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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
Predicting Customer Choice?
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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
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
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• It all starts with your buy sheet.
Your Buy Sheets, Optimized
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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
Buy Optimization Output
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Predicted Net Sales + RankOptimized Buy Quantity
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
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• 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
Example 1: Savings in Markdown Units
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Style: XYZRetail Price: $68Cost: $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
Example 2: Savings in Lost Sales Units
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Style: ABCRetail Price: $150Cost: $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
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.
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Thank you!
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