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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 |
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
Oracle Confidential – Internal/Restricted/Highly Restricted
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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
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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
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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
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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
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• 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
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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
AC Advanced Clustering
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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
AC Advanced Clustering
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Customer Segmentation Functionality Segment Criteria – Defining Initial Segmentation Parameters
AC Advanced Clustering
Key Segmentation Inputs Are Contextual Based on Segmentation Approach
Selection
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Customer Segmentation Functionality Explore Data – Analyze Customer Distribution
AC Advanced Clustering
Review Data Distribution of Customers Across All
Attributes
Further Drill To Review % Distribution of Customers by Each
Attribute
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After Customer Segments Have Been Created; Review
Results / Insights
Customer Segmentation Functionality Segment Results / Insight Analysis
AC Advanced Clustering
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So How Are Customer Segments Used Throughout the Oracle Retail Portfolio?
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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
AC Advanced Clustering
Insights / Analytics
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• 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
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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
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• 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
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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
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Identify / Range the Applicable Attributes by Customer Segment
Assortment Planning – Fashion Using Segments within Assortment Planning Fashion
AC Advanced Clustering
As The Assortment Is Created Understand / Manage
Assortments; Validating The Attribute Mix By Customer Is
Aligned With Expectations
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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
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What Data Is Needed To Support Customer Segmentation?
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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
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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
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Maximize Investments While Improving Customer Satisfactions
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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
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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?
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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)
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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
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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)
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