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Copyright © 2016, Oracle and/or its affiliates. All rights reserved.

Bill Boezinger – Planning & Optimization SIG Leader Marc Koehler – Solutions Director, Planning & Optimization March 17, 2016

Customer Segmentation Science Oracle Retail Planning & Optimization SIG

Copyright © 2016 Oracle and/or its affiliates. All rights reserved |

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Customer Segmentation Science Agenda

• Confirm Market Trends

• Customer Segmentation – High-Level Overview

• How Are Customer Segments Used Throughout the Oracle Retail Portfolio?

• High-level Data Requirements

• Questions / Discussion Topics

• Wrap Up



Market Trends A Constantly Evolving Marketplace – Customer Is King!

92% of customers report they will not wait for a product to come into stock

21% of customers who cannot find a brand will buy

that brand elsewhere

Intelligent localization can drive category sales by as much is 5%

*Source: Oracle, The New Retail Democracy – March 2014

*Source: The Category Management Association, The Theory and Practice of Optimizing Assortment and Space - 2014 *Source: ECR Europe



Top Merchandising Challenges Modern Merchandising: Managing Complexity with New Tools and Techniques

43% of all retailers polled claimed Understanding Customer Preferences as their top merchandising strategy challenge *Source: RSR, Modern Merchandising: Managing Complexity with New Tools and Techniques – Feb.2015

Top-Tier retailers continue to acquire technology companies to create in-house science capabilities

“Retailing is no longer about what you want to sell. It’s about figuring out what the customer wants to buy.” *Source: Niki Baird, RSR, Modern Merchandising: Managing Complexity with New Tools and Techniques – Feb.2015



Solving The Problem Improving Customer Satisfaction While Maximizing Profit

Consumer Inspired & Targeted Planning/Execution Multiple Data Points

Performance Data

Customers Data Market Data

Attributes

Oracle Retail Portfolio Assets



• Dedicated Retail Science Team of PhDs plus joint research with Oracle Labs

• Methodology conducted with retail data/processes; building purpose built retail solutions

• Active joint research with MIT, University of South Carolina, University of Michigan and Duke University o Multiple Recognitions, Patents and Published Papers

• Cross-disciplinary team of PhDs o Operations Research o Machine Learning o Mathematics

• 50+ US patents granted or pending plus additional International Planning & Price Optimization - 16 Forecasting & Data Modeling - 10 Social / Big Data Analytics - 5 Assortment & Space Optimization - 4 Pack & Size Optimization - 3 Inventory Management & Optimization - 2 Inventory Allocation - 1 Augmented Reality User Interface - 1

Oracle Retail Science Leading Innovation

o Physics o Management Science



Advanced Science Engine

ASO Assortment &

Space Optimization

RMI Retail

Merchandising Insights

RCI Retail Customer

Insights

Advanced Science Cloud Services Customer Centric / Purpose-Built SaaS Based Services Launched Summer of 2015

Science-Driven Actionable Insights

Assortment Planning & Optimization

Category Planning … Consumer

Engagement

AC Advanced Clustering

CS Customer

Segmentation

CDT/DT Customer Decision

Trees/Demand Transference

Switching Not Likely

Switching

Highly Likely

Outdoor Enthusiasts

Insulated (8)

Weight: .07

Outdoor Vests

Marmot (6) Weight: .19

The North Face (8)

Weight: .36

Soft Shell (9)

Weight: .23

Fleece (9) Weight:

.15

Patagonia (26)

Weight: .45

MBI Market Basket

Insights


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• Create an un-biased view of core customers enabling the localization of assortments, customer specific/targeted pricing and promotions and personalized shopping experiences

Customer Segmentation Functionality Localize, Target and Personalize

• Leverage multiple segmentation approaches Demographics Category Purchase Behavior Segmentation Customer Purchase Behavior (ie. Frequency and Monetary)

• Rich insights providing the ability to identify the key attributes related to each customer segment

• Predict how customer shopping patterns evolve over time

• Key insights enable users to prioritize workload as well as easily identify the most meaningful attributes for a segment

• Ability to seamlessly integrate with planning & optimization, pricing/promotion and execution solutions





Segmentation Strategies Enabling Multiple Segmentation Approaches

Demographic Segmentation • Age • Income • Gender • Marital Status, etc.

Understanding Your Target Customers

Category Purchase Driven Segmentation

• Average Amount Spent by Category • Purchase Rate by Category • Average Sales Price (i.e. AUR) per

Category

Grouping Customers Based on Category Purchase Behavior

Customer Purchase Behavior Segmentation

• Purchase Frequency • Average Amount Spent by Customer • Average Basket Size • Promo Spend

Targeting Customers Based on Trip Frequency & Spend





Customer Segmentation Functionality Segment Criteria – Defining Initial Segmentation Parameters


Key Segmentation Inputs Are Contextual Based on Segmentation Approach

Selection



Customer Segmentation Functionality Explore Data – Analyze Customer Distribution


Review Data Distribution of Customers Across All

Attributes

Further Drill To Review % Distribution of Customers by Each

Attribute



After Customer Segments Have Been Created; Review

Results / Insights

Customer Segmentation Functionality Segment Results / Insight Analysis




So How Are Customer Segments Used Throughout the Oracle Retail Portfolio?



Customer Segmentation Functionality Leveraging Customer Segments Across Enterprise Process & Solution

Promotion Planning Lifecycle Pricing Customer Engagement

Supply Chain & Execution

Assortment Planning

Web Personalization

Customer Segmentation


Insights / Analytics




• Leverage customer insights to localize planning & execution processes; driving a notable increase in sales / customer satisfaction

• Dynamic and flexible allowing the ability to dynamically cluster at multiple hierarchy levels

Advanced Clustering Cloud Service A Single / Central Enterprise Clustering Solution

• Leverage multiple clustering approaches Product / Location Attributes Consumer Segments Performance Mixed Attributes

• Key insights enable workload prioritization

• Identify the optimal # of clusters

• Embedded scoring logic to assess the quality of store clusters

• Use What-If capabilities to identify the optimal clusters



Advanced Clustering Cloud Service Enabling Clusters Based on Consumer Behaviors

Cluster Stores Based On Consumer Segment

Behaviors Stores Are Grouped Based on Common

Shopping Patterns Of Consumer Segments



• Utilize YOUR data to drive the creation of the CDTs; enabling the ability to understand customer purchase preferences / trade-offs

• Compare your specific CDTs with supplier (or external) CDTs to determine / understand key differences

• Reduce duplication in assortment

• Prevent dropping unique items that may result in lost customers

CDT and DT Science Cloud Service Create ‘Customer Decision Trees’ (aka Shopper Decision Trees)

Instant

Coffee

Peet’s Coffee Folgers

Whole/Ground Single Serve

Starbucks



Assortment Planning & Optimization Review / Manage Assortments Leveraging AC and CDT/DT Science Inputs

Review and Make Assortment Decisions While Leveraging Customer Decision Trees

Demand Transference Detail Is Readily Available; Enabling Users to Plan in the

Most Productive Manner



Identify / Range the Applicable Attributes by Customer Segment

Assortment Planning – Fashion Using Segments within Assortment Planning Fashion


As The Assortment Is Created Understand / Manage

Assortments; Validating The Attribute Mix By Customer Is

Aligned With Expectations




Segment

Service Event

Trigger

Promotion Event

Targeting

Loyalty Eligibility

Rules

Systems Integration

Configurable Query ‘Templates’

Configurable Options

*Note: Export Formats And Content Are Configurable! Used Extensively Across The Application

Customer Engagement Using Segments within Customer Engagement



What Data Is Needed To Support Customer Segmentation?



Objects Granularity Required /Optional

Hierarchies Product & Location & Fiscal Required

Customer-Linked Transaction

Transaction/SKU/Store Required

Customer Attributes Customer Optional

Alternate Hierarchies Category Management Group, Trade Area or Planning

Period Optional

Customer Segmentation Functionality Data Needs



Define An Action Plan Building a Foundation That Grows With Process Maturity / Overall Business Goals

Step# 2 – Begin to Leverage, Build Trusts & Report on Customer Data

Create An Unbiased View Of Your Customers

Begin To Incorporate Learnings Into Core Planning / Execution Processes

Step #3 – Take Action Upon Solution Recommendations

Leverage Embedded Science to Gain Real-Time Planning Recommendations

Deliver the Right Assortment To Each Location

Step #1 – Build A Foundation

Leverage Best Practice Solutions / Templates

Gain User Adoption

Ensure End-to-End Capability


Maximize Investments While Improving Customer Satisfactions


Why Oracle Retail?

Embedded Science & Automation

Highly Visual & Mobile Platform Role Based, Modular End-to-End Process

Proven, Scalable and Configurable

Named a consistent Leader by both Gartner & Forrester

Consumer Inspired Planning Customer is the Centerpiece



Questions / Discussion Topics • How Would You Leverage This Functionality Within Your Organization?

• What Processes Within Your Organization Would Gain the Most Benefit?

• Does This Functionality Help With Your Current Customer Segmentation Initiatives?

• What Do You See As the Most Lucrative / Beneficial Functionality?

• What Are Your Largest Obstacles To Adopting This Functionality?



Oracle Industry Connect - 2016

Oracle Industry Connect – Orlando, FL (Apr 11 – 13, 2016) Venue – Walt Disney World Swan and Dolphin Resort https://www.oracle.com/oracleindustryconnect/retail/index.html

Joint Planning & Opt. / Supply Chain SIG (Apr 10th, 2016) 2:45pm – 6pm (Toucan 2 – Swan Lobby Level)


https://www.oracle.com/oracleindustryconnect/retail/index.html�





Oracle Industry Connect ORUG Pre-conference Networking Sessions

3

Commerce Merchandising Planning & Optimization Stores Supply Chain Technology Functional

• To Register for the ORUG pre-conference SIG https://www.surveymonkey.com/r/ORUGatOIC

• If you haven't already, register at Oracle Industry Connect to confirm your participation in the retail program today. Attendance is limited to retailers and our program sponsors to ensure that the focus remains on retailers learning from one another.

• Send your questions to ArMand Nelson (ORUG President) and Rosemarie Lao (Oracle)

SIG 1:00-2:30PM 2:45-4:15PM 4:30-6:00PM

Commerce (ATG & Endeca) Conference Room: Toucan 1

Merchandising Conference Room: Mockingbird 2 Conference Room: Mockingbird 2

Omni-channel (Micros – OCP, OMS, CE, OB) Conference Room: Toucan 2

Joint Planning & Supply Chain Conference Room: Toucan 2 Conference Room: Toucan 2

Joint Stores & SIM Conference Room: Toucan 1 Conference Room: Toucan 1

Fashion (closed-group) Conference Room: Mockingbird 2

• Monday April 11, 2016 • Time: 1:00 pm – 6:00pm • Walt Disney World Swan Resort

https://www.surveymonkey.com/r/ORUGatOIC�

http://www.oracleindustryconnect.com/retail�

mailto:[email protected]; [email protected]?subject=ORUG: Pre-conference Networking sessions at OIC (Question)�

mailto:[email protected]; [email protected]?subject=ORUG: Pre-conference Networking sessions at OIC (Question)�

http://www.swandolphin.com/home.html�


The RACK Micro site Update

3

Highlights Retail Asset and Community Knowledge • https://go.oracle.com/OracleRetailRack • Improved navigation and visualization • New categories for fast access to most sought-

after information – Customer content by vertical – Event proceedings by event (e.g., Open World, ORUG,

etc.)

• Oracle Solutions features latest solution area roadmap(s)

• Post-conference materials • Search by keyword (ORUG)

https://go.oracle.com/OracleRetailRack�

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