Oracle Retail Lifecycle Price Optimization Demo Deck for Tillys 3.14.24

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Oracle Retail Planning Overview

March 2024

Tillys Oracle’s Lifecycle Price Optimization

Oracle Retail Team

Retsci, Director of Science Analytics

Bill Zacher

Retsci, Chief Operating Officer

Ryan Somers

Product Management & Strategy

Rachel Lewis

Principal Sales Consultant, Retail Planning

Mike Daves

Account Executive

Kevin Walsh

Sales Consulting VP

Bob Zacher

Senior Cloud Architect

Carla Anderson

Oracle Retail Experience at Scale

310 Million SKU

52.7 Trillion Managed SKU Locations

43 Million Daily Transactions

163K Stores

380 Million Customer Records

499.3 Billion Annual Revenue

35+ years developing and deploying retail software

130K+ Cores of Compute

16 Petabytes of storage

Agenda

Introductions

Overview

Demonstration

Science as a Service & Implementation with Retsci

Wrap Up & Next Steps

Tillys Opportunities

Scenario Modeling

Platform to Grow withEfficiency

Maximize Margin

Proactive vs. Reactive

Oracle Named a Leader in IDC MarketScape: Worldwide

Retail Price Optimization Solutions 2023

“Built for scale: Oracle's price optimization solution is well designed to meet the scale needs of any retailer, regardless of size, industry segment, geography, business model, or channel mix.”

A Leader in Retail Price Optimization

Price & Strategy

How do I ensure that I am pricing optimally throughout an item’s lifecycle? How do I ensure I am maintaining price perception while balancing margin and revenue goals?

Price & the details

How can I understand how product – store- customer price sensitivity affects revenue and margin and product availability ? How can I take all pricing decisions faster without deviating from my pricing strategy?

Move Inventory

How do I place inventory when and where my customers want it and in the right quantity and at the right price?

Price & Effectiveness

How do I monitor and ensure I meet my goals season after season? How can I grow share instead of cannibalizing sales?

Customers

What and when are my customers buying from me? When do they switch to competitors? How can I increase their share of wallet?

Work the Value Chain

How do I monitor and ensure seamless alignment across the value chain?

Precision in Pricing

T R A N S F O R M

B r o w s e a n d p l a n

T r a n s a c t

A c q u i r e

C o n s u m e

R e v i e w

I N N O V A T E

Unified Planning Process

B r o w s e a n d p l a n

T r a n s a c t

A c q u i r e

C o n s u m e

R e v i e w

24 months – 6 months 6 Months – 1 Week In Season Next Season

Pre-Season MFP Category Plans

In-season Assortment Analytics | OTB Re-Planning | Price Recommendations & Targeted Offers Localized Assortments Initial Buy & Price Plan

In-Season Forecasting, Allocation & Replenishment Weekly Item Plan

Sales, Inventory, Receipts

Analyze &

Review

Regular Price

Promotions

Markdowns

Retailer’s Data

Applied AI for Unified Planning

Retail Analytics & Planning Common UI (Retail Home, Workflow, Analytics)

Retail Insights

Merchandise Financial Planning

Assortment and Item Planning

Assortment & Space

Optimization

Inventory Planning

Optimization

Lifecycle Pricing

Optimization

Retail AI Foundation

Advanced Clustering

Forecasting Engine Attribute Extraction Customer

Segmentation

Customer Decision Trees

Demand Transference

Size Profile Optimization

Affinity Analysis Innovation Workbench

Descriptive

Descriptive analytics is a point in time metric that provides an interpretation for how business is performing

Predictive

A more advanced method of data analysis that uses a variety of methods and models to make assessments of what could happen in the future

Prescriptive

While predictive analytics show the anticipated results of potential actions, prescriptive analytics provide recommendations to achieve specific results

How is my new assortment selling, and where is it selling best/worst?

How much am I forecasted to sell over the next weeks and months?

What can I do to sell more, make more margin, and achieve my inventory goals?

Supporting the Full Range of Analytics

Lifecycle Pricing Optimization drive higher revenue & margin by

optimizing prices based on demand and pricing rules and strategies

Provides robust what-if scenario planning

Guided Learning and Chat bot to aid the users

Built on AI Foundation

Machine learning and deep learning techniques to forecast and evaluate

impact of optimized recommendations

Pricing Strategies management, including key competitive strategies &

ranking and prioritization of rules Allow for high automation and

configuration of workflow

Lifecycle Pricing Highlights Driven by Automation

Maximizing the Value of Your Assortment with In-Season Promotion/ Markdown & Offer Optimization

Promotion and Markdown Optimization

Product Introduction $58.99

Promotion 10% Off

Promotion 15% Off

Markdown 30% Off

Markdown 50% Off

Markdown 70% 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

Business Constraints & Objectives

Demand Sensing

Statistical forecast Lifts and demand estimates Price elasticity

Pricing strategy Business rules Target goals Lifecycle eligibility

Price Recommendations & Estimated Impact

Recommendation Engine

Constraints Organization

Typically defined at Global and Optimization Level, with

overrides at Class, Subclass and Item Levels

Presented in a Consolidated View for Management and

Visibility

Discount Ladders set for Promotion and

Markdown and Location

Sell-Through Targets

Time-related

Constraints

No-touch Periods

Time-between

Promotion Length

Product Lifecycle

Promotion Constraints

Promotion Days

Discount % Min / Max

Total Promotions

Markdown Constraints

Markdown Days

Discount % Min / Max

Total Markdowns

Product Group Rules

At most one item

All items promoted

All items promoted same

Defining the Optimization Business Rules / Constraints

Promotion Markdown & Offer Optimization Demonstration

AI : Like Items using Image Similarities Identifies similar item based on product images so that it can be used to identify like items for forecasting or initial pricing

• Deep learning-based segmentation to eliminate background information and noise in images

• Segmented images are transformed into embedding vectors using deep neural networks

• Embedding vectors to compute similarity scores

• Compare the query image with all others to identify similar images based on calculated scores

4-10% increase in Gross Margin

Increase in Forecast Accuracy

2-5 days reduction in inventory

Up to 50% increased staff efficiency

Automation

Exception Management

Collaboration

Reduction in Vendor Variability

Increased Sell-Thru % 4-10%

Faster IRR

The Value based on 15+ retail case studies

Retailer’s Data

Applied AI for Unified Planning

Retail Analytics & Planning Common UI (Retail Home, Workflow, Analytics)

Retail Insights

Merchandise Financial Planning

Assortment and Item Planning

Assortment & Space

Optimization

Inventory Planning

Optimization

Lifecycle Pricing

Optimization

Retail AI Foundation

Advanced Clustering

Forecasting Engine Attribute Extraction Customer

Segmentation

Customer Decision Trees

Demand Transference

Size Profile Optimization

Affinity Analysis Innovation Workbench

Demo Slide 1 Slide 2: Oracle Retail Team Slide 3 Slide 4: Agenda Slide 5 Slide 6 Slide 7 Slide 8 Slide 9 Slide 10 Slide 11 Slide 12 Slide 13 Slide 14 Slide 15: Maximizing the Value of Your Assortment with In-Season Promotion/ Markdown & Offer Optimization Slide 16 Slide 17: Defining the Optimization Business Rules / Constraints Slide 18 Slide 19: AI : Like Items using Image Similarities Identifies similar item based on product images so that it can be used to identify like items for forecasting or initial pricing Slide 20: The Value Slide 21

Oracle Retail Lifecycle Price Optimization Demo Deck for Tillys 3.14.24

Item Type: pdf