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Boost Merchandise Buy Accuracies With Predictive Analytics

Boost Merchandise Buy Accuracies with Predictive Analytics

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Boost Merchandise Buy Accuracies With Predictive Analytics

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

EXPECTATIONS HAVE CHANGED

© 2017 Celect, Inc. Confidential. Do Not Distribute.3

Your customers expect everything, everywhere, all the time.

LOOKFAMILIAR?

© 2017 Celect, Inc. Confidential. Do Not Distribute.4

DECISIONS HAVE BEENMADE THIS WAY FOR YEARS

1980s 1990s 2000s Today

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1980s 1990s 2000s Today

SO WHY CHANGE NOW?

PRESSURESI N T E N S I F I E D

INVENTORY CHALLENGESDEMAND UNCERTAINTY

SHRINKING MARGINS / PROFITSLOST SALES

© 2017 Celect, Inc. Confidential. Do Not Distribute.6

© 2017 Celect, Inc. Confidential. Do Not Distribute.7

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

© 2017 Celect, Inc. Confidential. Do Not Distribute.8

An American Luxury Department Store

Goal: Avoid Stock-outs and Markdowns Solution: True Demand Prediction

EXPECTATIONS HAVE CHANGED

© 2017 Celect, Inc. Confidential. Do Not Distribute.9

Your customers expect everything, everywhere, all the time.

YOUR JOBIS HARD

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

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

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

• 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

Upload a Buy Sheet

© 2017 Celect, Inc. Confidential. Do Not Distribute.14

Buy Optimization Output

© 2017 Celect, Inc. Confidential. Do Not Distribute.15

Predicted Net Sales + RankOptimized Buy Quantity

Associated Products Details

© 2017 Celect, Inc. Confidential. Do Not Distribute.16

Attribute Information Details

© 2017 Celect, Inc. Confidential. Do Not Distribute.17

Attribute Coverage Details

© 2017 Celect, Inc. Confidential. Do Not Distribute.18

Download Your Optimized Buy Sheet

© 2017 Celect, Inc. Confidential. Do Not Distribute.19

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

Example 1: Savings in Markdown Units

© 2017 Celect, Inc. Confidential. Do Not Distribute.21

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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