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Oracle® Retail Science Cloud Services Implementation Guide
Release 19.1.003.2
F40917-01
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Contents
Title and Copyright Information
Send Us Your Comments
Preface
Audience
Documentation Accessibility
Related Documents
Customer Support
Improved Process for Oracle Retail Documentation Corrections
Oracle Retail Documentation on the Oracle Technology Network
Conventions
1
Introduction
ORASE and Business Agility
Oracle Retail Advanced Clustering
Reporting and Analysis
Oracle Retail Assortment and Space Optimization
Dynamic Creation of Space Clusters
Conduct Micro-Space Optimization What-if Analysis
Preview Results Leveraging Shelf Preview Capabilities
Oracle Retail CDT Science and DT Science
Customer Decision Trees
Demand Transference Science
Oracle Retail Attribute Extraction
Oracle Retail Affinity Analysis
Common Workflow
Interacting with ORASE
Hardware and Software Requirements
FAQs
2
Implementation Overview
Implementation Process
Implementation Steps
Configure the Application Roles and Users
Data Load Overview
Edit and Load Common Seed Data
Perform Attribute Preprocessing for CDT and DT, as Appropriate
Mandatory Configuration Parameters
3
Customer Decision Trees
Input Data
Overview
Transactions Data Requirements
Attribute Data Requirements
Attributes with a Large Number of Values
Grouped vs. Raw Attribute Values
Attribute Splitting
Functional-Fit Attributes
Customer Segments
Location Hierarchy
Setting Up Categories
Calculating Customer Decision Trees
Setting the Top Attribute
Excluding Attributes from the Calculation
Handling of the Brand Attribute
Limitations of the CDT Calculation
Choosing the Time Interval
Understanding the Filter Settings
Segments vs. Location
Setting the Escalation Path
How the CDT Score is Calculated
Understanding CDT Pruning
Overriding the CDT Calculation
Using the Calculation Stage
Setup Stage
Data Filtering Stage
Calculation Stage
Advanced Use
4
Demand Transference
DT and CDT
Demand Transference Model
An Example
Historical Similarity Data
Historical Sales Data
The Role of Attributes in Calculating Similarities
Attribute Data Requirements
Guidelines on Number of Attributes and Attribute Values
The Effect on Similarity Values
Avoiding Attributes with Many Values
Functional-Fit Attributes
Using Null as an Attribute Value
The Effect of Null Attribute Values on Similarity Values
Categories Containing Sub-Categories
Customer Segments
Location Hierarchy
Setting Up Categories
5
Using Demand Transference
Seasonality in Historical Sales Data
Assortment Elasticity
The Importance of Assortment Changes in Historical Data
Estimation of Assortment Elasticity
The Meaning of the Possible Values of Assortment Elasticity
The Substitutable Demand Percentage
No Requirement for a Time Interval
Segments vs. Locations
Setting the Escalation Path
Automatic Updating
Avoiding Categories with Small Assortments
Implementing DT for Fashion Categories
Proper Level for Fashion Categories
Seasonality (Life Cycle) Considerations
6
Advanced Clustering
Overview
Data Requirements
Multiple Hierarchies and Level Support
Clustering Criteria Supported in Store Clustering
Attributes in Store Clustering
Configuration Process
Copy Clusters Using Like Product Mapping
Multiple Clustering Approach
New Stores or Stores with Poor History
Outliers
Export to Excel
7
Customer Segmentation
Overview
Data Requirements
Multiple Hierarchies and Support
Supported Segment Criteria
Customer Segmentation Attributes
Configuration Process
Attribute Preprocessing
Segmenting Approach
Customer Segment Store Profile Generation
Preprocessing
Customer Metrics
8
Affinity Analysis
Overview
Data Requirements
MBA_ARM_SRVC_LOC_STG
MBA_ARM_SRVC_CONFIG_STG
Science Algorithms/Services
ARM_PH
ARM_PH_PROMO
ARM_PH_CS
Configurations
Data Output
9
Assortment and Space Optimization
Overview
ASO Data Input Requirements
Assortment Data
Planogram Data
Assortment-to-Planogram Mapping
Assortment to POG Mapping Process
Input Data
Automated Process
Mapping Errors
Product Images Data
Replenishment Data
Optimization Science
Optimization Algorithm Overview
Sales and Inventory Model
Sales and Inventory Modeling Considers All Possibilities
Inventory Levels After Replenishment
Calculating the Facing Capacity for a Product/Fixture Combination
Maximum Capacity of a Product
Replenishment Parameters
Sales Inventory Model Output
Objective Function
Constraints
Product Family Group Constraints
Diagnosis of Visual Guidelines
Product Groups
Validation Tool (Sanity Checker)
Diagnosis of Dropped Products
Checklist for Optimization Results Diagnosis
Monitoring Batch Processes
Overview
Batch Process Failure
Global Validation Issues
Sending Data in Data Files
Assortment-Related Files
POG-Related Files
Display-Style Files
POG Historical Data and Store CDAs
Mapping, Replenishment, and Other Files
10
Assortment Recommender
Prerequisites
Producing Better Assortments
Run Groups and Run Frequency
The Run Group Parameters
Data Used by the Assortment Recommender
Halo Effects
Troubleshooting
11
Offer Optimization
Overview
Security
Data Input Requirements
Hierarchy Data
Retail Sales Data
Promotions
Warehouse Inventory Allocation
Competitor Prices
Product Attributes
Product Images Data
Season
Season Periods
Season Products
Price Ladder
Currency
Holidays
Pricing Product Groups
Budget
Strategy and Business Rules
Custom Columns
Optional Interfaces
Offer Optimization Integration with Retail Pricing Cloud Service
Configuration and Expected Levels for Interfaces
Offer Optimization Forecasting
Parameter Estimation
Overview of the Parameter Estimation Stages
Data Preparation
Data Aggregation
Promotion Data Processing Level
Data Validation
Preprocessing
Data Filters Week Level
Partition Filters
Elasticity
Data Filters Weekly
Hierarchy Levels Selection
Markdowns
Promotion One Week
Promotion Two Week
Reliability Settings
Transformation
Seasonality
Seasonality Curve Setup
Reliability Filters
Promotion
Output and Review of Parameters
Day Level and Returns Metrics
Day of the Week Profiles
Returns Metrics
Demand Forecasting
New Stores
Offer Optimization Science
Business Rules
Optimization Algorithm Overview
Offer Optimization Forecasting
Objective Function
Constraints
12
Inventory Optimization
Overview
Inventory Optimization Runs
Inventory Optimization UI Workflow
Security
Data Input Requirements
Hierarchy Data
Retail Sales Data
Inventory Data
Product Attributes
Replenishment Attributes
Price and Cost
Season
Optional Interfaces
Global Configurations
Inventory Optimization Integration with RMS
FAQs
General
Workflow
Forecasting
13
Configuration
User Interface Authentication and Authorization
User Management Configuration: Configuring Users and Roles
User Roles
Configuration
RSE_CONFIG Table
Generic Configuration
Advanced Clustering Configurations
Assortment and Space Optimization Configurations
Customer Decision Tree Configurations
Demand Transference Configurations
Returns Logistics Configurations
Affinity Analysis Configurations
Resource Bundles
Manage Notebooks
Internationalization
Configuration Updates
Configuration Tables
Email Notification Configuration
14
Attribute Processing
Attribute Preprocessing
Process Overview
Product Attribute Loading
Introduce New Attribute
Determine the Attribute Source and Define in the Tables
W_PRODUCT_D or W_PRODUCT_ATTR_D
W_RTL_ITEM_GRP1_D or W_RTL_ITEM_GRP2_D
Populate RSE_PROD_ATTR_GRP_VALUE_STG Interface
Populate RSE_PROD_ATTR_VALUE_XREF_STG Interface
15
RASE Web Services
RASE Web Services
Authentication and Authorization
Summary of Web Services
Access to RSE_CONFIG Table
Access to RSE_CONFIG_CODE Table
Advanced Clustering Export
Customer Segment Export
ASO Exports
16
Batch Processing
Overview
Custom Batch Requests
Managing Custom Batch Requests
Handling Data Files
Supported PROCESS_QUEUE Trigger Values
Incremental Exports
Batch Process Flow
Configuring Additional Data Files
File Transmissions
17
Social Analytics
Data Inputs
18
Innovation Workbench
Components
Retailer Workspace Schema
Oracle APEX
Workspace
Users and Roles
Oracle Advanced Analytics
Oracle Data Mining
How to Invoke Oracle Data Mining
Oracle R
Notebooks
Scheduling Innovation Workbench Python Notebook
IDCS Setup
Scheduling Jobs
REST API Documentation
Example
Notebook and Paragraph IDs
Considerations
Weblogic REST API
Restful Service
Troubleshooting
DBMS Scheduler
Schema Objects
19
Oracle Digital Assistance
Transactional Digital Assistance
Q&A Digital Assistance
Oracle Chatbot - Bot Channel Setup
Oracle Chatbot - Bot Service Setup
RSE Chatbot - Register Bot
RSE Chatbot - Manage Credentials
ODA Roles
Train Model
20
Process Orchestration and Monitoring
Batch Administration
Batch Monitoring
Monitoring a Batch Cycle
Triggering Process from POAM
Running a Nightly Batch Process
Monitoring the Process Executions
Running AdHoc Batch Processes
Enabling AdHoc
Monitoring Process
Disabling AdHoc
Intraday Cycle
Error Handling
21
FAQs
General Questions
DT Questions
ASO Questions
Attribute Processing Questions
Offer Optimization Questions
A
Appendix: Innovation Workbench Workshop
Overview
Lab Innovation Workbench
Lab Analysis
Browse Existing Data
Using ReST to Load Data
Other Methods for Loading Data
Explore Data Analysis
Review customer behavior analysis using Text Mining
Oracle R Graphics
Innovate
Churn Analysis using ODM
Decision Tree Model Details
Display Decision Tree Rules
Lab Share Insights - ReST API
Lab Share Insights - Task Navigation
Database Tools
How to Allocate Privileges
How to Execute a Job using DBMS Scheduler
Custom Data Loads
Required Files
Zip File Contents
Context File Details
Data Load Feedback
Invoking the Data Load
Custom Data Exports
Required Configuration
Invoking the Export
Glossary of Acronyms