Oracle Retail Customer Decision Tree and Demand Transference Science
DATASHEET | Oracle Retail Customer Decision Tree and Demand Transference Science | Version
1.00 Copyright © 2022, Oracle and/or its affiliates
Oracle Retail Customer
Decision Tree and Demand
Transference Science Retailers today are looking for a more complete understanding of their
customers to retain loyalty, improve sales, and grow market share.
Customers are constantly communicating with retailers with their
purchase patterns, shopping preferences, and behaviors. Retailers must
go beyond the traditional analysis of SKU/Location sales history
patterns to leverage sophisticated data mining capabilities on granular
transaction-level data to gain deeper insights into customer behavior
patterns and product preferences.
GAINING CUSTOMER INSIGHT WITH DECISION TREE Historically, retailers have relied significantly on product manufacturers for consumer
insights and decision trees based on large geographies and varied methodologies.
The Oracle Retail Customer Decision Tree Science and Demand Transference Analytics
enables retailers to create customer segment-specific decision trees using available
transaction-level data. These customer decision trees are specific to their customer
segments and respective geographies. They provide retailers a true understanding of
the most important products and product attributes as customers see them. Armed
with this detail, the retailer is able to effectively analyze assortment coverage, identify
duplication of item types, and prevent the removal of core items that would cause a
loss of customers.
IDENTIFICATION OF CUSTOMER PURCHASE AND
SWITCHING PATTERNS
Oracle Retail Customer Decision Tree and Demand Transference Science mines
customer purchase history to identify shopping and switching patterns. This helps
retailers understand what attributes are driving customer purchases, when would
they walk away without making a purchase, and when are they willing to switch
products? These provide key insights to retailers as they make assortment, pricing,
and promotion decisions.
IDENTIFICATION/ELIMINATION OF NATIONAL
INFLUENCES AND MANUFACTURER BIASES
By leveraging retailer-specific transaction-level data, a true view of the customer is
provided during the customer decision tree creation process. This eliminates any bias
that may be present within an externally provided consumer decision tree. Using the
Oracle Retail Customer Decision Tree and Demand Transference Science, retailers are
able to generate customer decision trees with their data and compare to externally
provided consumer decision trees. Based on this unbiased comparison, retailers
make adjustments and edits to confirm and approve usage within their assortment
processes.
Key Benefits
Improve customer
satisfaction through the
creation of customer-centric
and targeted assortments
Understand customer-
specific purchase patterns
and trade-offs by customer
segments and by channels
Leverage Customer Decision
Trees built from a retailer’s
own data to remove any
market bias that may exist
Eliminate similar items within
an assortment while
preventing the removal of key
items
Identify the incremental value
of each item in an assortment
Recognize the shift in item
demand within a particular
assortment as items are
added or dropped
ORACLE CLOUD INFRASTRUCTURE
All Oracle Retail Analytics and Planning cloud services are deployed as cloud-native
Software-as-a-Service solutions within Oracle Cloud Infrastructure (OCI) upon Oracle’s
Autonomous Data Warehouse, and are based upon an architecture and technology
stack that is optimally engineered for rapid, low-cost deployments and exceptional
performance and scalability, and the highest levels of system availability and security -
from storage to scorecard.
ORACLE RETAIL AI FOUNDATION
Core retail AI and machine learning (ML) powers all Oracle Retail Analytics and
Planning cloud services. For example:
Forecasting Engine - Provide an intelligent starting point for your planners, increasing
automation and accuracy. Move to a more touchless and exception management
planning process.
Customer Segmentation - Group customers based on attributes, behaviors, and
transactions to tailor offers, pricing, and assortments accordingly, incorporating
previously hidden patterns in your data.
Advanced Clustering - Cluster your stores based upon traditional approaches of
volume, square footage, region, etc., or leverage machine learning techniques to cluster
stores based upon similar selling patterns, truly creating a customer-centric assortment.
Profile Science - Determine the best size ratio for your buys by understanding the true
demand of your sizes while considering stock-outs.
Attribute Extraction and Binning - Extract item attributes from free-form descriptions,
correcting short forms, misspellings, and other inconsistencies, and apply them to
Demand Transference, Customer Decision Trees, Advanced Clustering, and more.
Customer Decision Trees - Understand how your customers are shopping your
assortments to drive attribute-based alternate hierarchies and effectively plan your
assortment the way your customer shops.
Demand Transference - Understand how unique your items are and the incremental
revenue that item brings to determine the most optimal assortment for your customer.
Affinity Analysis - Determine how items interact with each other to drive a more
effective promotional strategy within your financial planning process.
Innovation Workbench - Leverage open source along with your data science team to
create your own AI and ML models. Utilize the language of your choice with
Jupyter/Zeppelin notebooks.
Key Features
Embedded Retail AI Foundation,
powering Oracle Retail Demand
Forecasting Cloud Service with:
o Forecasting Engine
o Customer Segmentation
o Advanced Clustering
o Profile Science
o Attribute Extraction &
Binning
o Customer Decision Trees
o Demand Transference
o Affinity Analysis
o Innovation Workbench
Further extensibility with:
o Oracle Retail Home
o Oracle Analytics
o Oracle Application Express
o Oracle REST Data Services
o Oracle Machine Learning
The Oracle Retail Analytics and Planning
family of cloud services includes:
Oracle Retail AI Foundation
Oracle Retail Insights
Oracle Retail Assortment and Space
Optimization
Oracle Retail Promotion and
Markdown Optimization
Oracle Retail Offer Optimization
Oracle Retail Merchandise Financial
Planning
Oracle Retail Assortment Planning
Oracle Retail Demand Forecasting
Oracle Retail Inventory Optimization
DATASHEET | Oracle Retail Customer Decision Tree and Demand Transference Science | Version
1.00 Copyright © 2022, Oracle and/or its affiliates
ORACLE RETAIL HOME
Oracle Retail Home is a single access point, to simplify a user’s interactions with the data
and applications that are most relevant to their roles, and to better empower them to
anticipate informed
actions, and to
inspire engagement.
Based on a robust
and flexible portal
framework, Retail
Home is intended
first to provide
timely and role-
specific high-level
insights, and second
to enable selectively
drilling into relevant
applications for
more details.
ORACLE ANALYTICS
Oracle Analytics can be used to generate and consume analytics from Oracle Retail AI
Foundation data, and in turn can also surface dashboards to Oracle Retail Home.
Oracle Analytics is a comprehensive platform that parlays data into information to
provide business insights, federating a broad array of features to suit business users,
power users and data scientists:
Governed Self-Service Augmented
Corporate Dashboards Data Preparation Natural Language Processing
Pixel Perfect Report Data Visualization Voice and Chatbot
Semantic Models Storytelling Data Enrichment
Role-based Access Control Sharing and Collaboration One-Click “Explain”
Query Federation Mobile Apps Adaptive Personalization
Beyond the extensibility afforded by the Oracle Retail AI Foundation’s Innovation
Workbench, Oracle Analytics, and Oracle Retail Home, also included are Oracle Data
Store, Oracle APEX, and Oracle REST Data Services.
ORACLE DATA STORE AND APPLICATION EXPRESS
Oracle Retail Data Store can supply data for Oracle Application Express (APEX) apps
and Oracle REST Data Services, which both are included. APEX is a low-code
development platform that enables you to build scalable, secure enterprise apps with
world-class features that can be deployed anywhere.
Developers can quickly develop and deploy compelling apps that solve real problems
and provide immediate value using APEX. You won't need to be an expert in a vast
array of technologies to deliver sophisticated solutions. Focus on solving the problem
and let APEX take care of the rest.
https://video.oracle.com/detail/video/5828487828001
DATASHEET | Oracle Retail Customer Decision Tree and Demand Transference Science | Version
1.00 Copyright © 2022, Oracle and/or its affiliates
ORACLE REST DATA SERVICES
Oracle REST Data Services bridges HTTPS and your Oracle Database, providing,
among other things, a REST API, SQL Developer Web, a PL/SQL Gateway, SODA for
REST, and the ability to publish RESTful Web Services for interacting with the data and
stored procedures in your Oracle Database.
ORACLE MACHINE LEARNING
Oracle Machine Learning supports data exploration, preparation, and machine
learning modeling at scale using SQL, R, Python, REST, AutoML, and no-code
interfaces. It includes more than 30 high-performance in-database algorithms
producing models for immediate use in applications.
By keeping data inside the database, organizations can simplify their overall
architecture and maintain data synchronization and security. It enables data scientists
and other data professionals to build models quickly by simplifying and automating
key elements of the machine learning lifecycle.
Learn more or request 1:1 demo
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