Price Optimization

Understanding Consumer Demand

Copyright © 2022, Oracle and/or its affiliates

Copyright © 2022, Oracle and/or its affiliates3

Oracle Retail Analytics & Planning

Affinity Analysis

Profiling Analytics

Customer Decision

Trees

Demand Transference

Innovation Workbench

Attribute Extraction &

Binning

Advanced Clustering

Customer Segmentation

Merchandise Financial Planning

Assortment Planning

Assortment & Space

Optimization

Promotion & Markdown

Optimization

Offer Optimization

Replenishment & Inventory

Optimization

Retail Insights & Data Visualizations

Demand Forecasting

Forecasting Engine

Supports merchandising &

marketing objectives,

reinforcing brand image &

maximizing margin

Maximize accuracy using machine

learning & optimization with a

modern AI Foundation

Holistic modeling to drive

incremental value to the lifecycle

While adhering to key business rules

Automated Analytical Price Optimization

Pricing

Scenario Number Scenario Description

Pricing 1 Strategy and Resource Roadmap- Today, TCS pricing is managed by 2 people and manual processes. There is limited visibility to the need for or impact of price changes

Pricing 2 Elasticity Modeling- We need to determine price elasticities (at the item, category level) to aid predictive modeling.

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Business Constraints & Objectives

Demand Sensing

Statistically forecasted demand

Lifts and demand estimates Price elasticity

Pricing strategy Business rules Target goals

Lifecycle eligibility

Price Recommendations & Estimated Impact

Optimization

Demand Sensing With Time Series & Machine Learning Techniques

Demand Sensing

Trends Seasonality

Causal Factors Holidays Events

Promotional Events Campaigns

Marketing Vehicles

Decomposing effects of promotions and price changes

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Campaign: Back to School

30% off offer

Coupon Offers

Clearance: 60% off

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13

Base Campaign lift Offer lift Coupon effect Markdown effect

Interaction effects

Pr om

ot io

n Ef

fe ct

s Pr

om ot

io n

Ca le

nd ar

Price Elasticity Advanced Modeling for Price Optimization

% of Change in Quantity Demand

% of Change in Price

Style- Color

Style- Color

Sku Sku Sku Sku

Style

Style- Color

Style- Color

Sku Sku Sku Sku

Style

Subclass

Attribute 1 Attribute 2

Price elasticities are modeled at multiple levels of the hierarchy.

Pricing

Scenario Number Scenario Description

Pricing 4 What-if scenarios & simulations- We would like to be able to control select SKU prices and view suggestions for other SKUs in the same category/collection in relation to that SKU.

Pricing 5 Pricing Optimization- ability to recommend price changes.

Pricing 7 Customer Loyalty Based/ Offer Pricing

Pricing 8 What-if scenarios & simulations- optimize and estimate impact of promotions & offers.

July 2022Copyright © 2022, Oracle and/or its affiliates | Confidential10

RECOMMEND NEW ITEM REGULAR PRICE

Carried-over ItemNew Item All models are trained to read new items and place them into the right bucket for pricing

Carried-over Items are always at most up-to-date pricing recommendation when changes happened

Automates current manual price setting

Intelligent data processing -- efficient and fast

5% revenue improvement from optimized prices

*unconstrained

Expected Optimization Benefits

RECOMMEND PROMOTION, MARKDOWN & OFFERS

Offer Optimization

Promotion & Markdown Optimization In-season price recommendations to drive margin, revenue and sell- through

Determine customer segments with the highest probability of redemption

Sell-Thru Targets

Time Constraints

Promotion Rules

Markdown Rules

Product Groups & Associations

Min/Max Discount # of Markdowns

Min/Max Discount Marketing Vehicles

All Items Promoted/Markdown All Items Priced the Same

Business Constraints & Objectives

Promotion and Markdown Optimization

Demand Shaping- Promotion/ Markdown & Offer Optimization

Product Introduction $54

Promotion 10% Off

Promotion 15% Off

Markdown 15% Off

Markdown 25% Off

Markdown 50% Off

Targeted Offer Highlighting

Relevant 10% Off Promotion

Targeted Offer Special 25%

Offer for “Trend Setter”

Targeted Offer Optimization

Wk 1 Wk 3 Wk 5 Wk 7 Wk 9 Wk 11 Wk 13 Wk 15 Wk 19Wk 17 Wk 21 Wk 23

Pricing

Scenario Number Scenario Description

Pricing 6 Market Basket Analysis

July 2022Copyright © 2022, Oracle and/or its affiliates | Confidential16

FAST ACCURATE Scalable & automated to drive exception management

AI/ML Models of choices Transparent and rigorous error measurement

- Optimization with inflation adjustment and elasticity

Highly agile and compatible with various business focuses

FLEXIBLE

SOLUTION DIFFERENTIATORS, LEVERAGING AI

The Value of One Transforming Inventory Precision

$1 .0

$1 .3

Net Revenue $ Power of One Net Revenue $s

$1.020

$1.010

$1.000

$1 Billion retailer, AUR $36, 30% markdown revenue

-1 +1

$ Bi

lli on

s

Customers typically see anywhere from

2 – 4 day improvement

➢ Robust Analytical & Machine Learning Techniques ➢ Optimization ➢ Automation ➢ Scalability ➢ Integration

+ $2,500,000 One time benefit for 1 day reduction in inventory *unconstrained

Supports merchandising &

marketing objectives,

reinforcing brand image &

maximizing margin

Maximize accuracy using machine

learning & optimization with a

modern AI Foundation

Holistic modeling to drive

incremental value to the lifecycle

While adhering to key business rules

Automated Analytical Price Optimization