Oracle Retail Data Model (ORDM) Overview -...
Transcript of Oracle Retail Data Model (ORDM) Overview -...
Oracle Retail Data Model (ORDM) Overview
May , 2014
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Content
• Retail Business Intelligence Key Trends
• Retail Industry Findings
• Foundation for Business Information Flows
• Retail is being Redefined
• Challengers for Getting Actionable Answers
• Oracle Retail Data Model (ORDM)
Overview
• Typical Issues Addressed by ORDM
• ORDM Subject Areas
• Oracle Retail Business Intelligence Space
• Complete Oracle Data Warehouse / BI
Solution
• ORDM Technical Architecture
• ORDM Components Relationship
• Sample Analytical Reports
• Sample Data Mining Packages and Data
Model Type
• Q & A
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Retail Business Intelligence Key Trends
Based on Industry Research, Retail will be an area of stronger than average
growth compared to other industries
Retail is the second largest growing segment for Analytics and Information
Management
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Retail Industry Findings
Within the trends of today’s Retail Market, there are opportunities for
growth in Retail Business Intelligence
Trend Details BI Opportunity
Customer Interaction Growth in the number of vehicles to
interact with customers, including in-store
kiosk displays, e-commerce, call centers,
catalogs sales, and mobile devices
Intelligence to monitor customer
interactions with a retailer through
promotion tracking and segmentation
Customer Social
Networking
Widespread adoption of social
networking to allow customers to provide
product reviews and feedback
Analytical insight into highly regarded
products and services from customers
Brand loyalty Need to increase brand loyalty and
customer retention at a time of increased
competition
Identify specific customer segments
and tailor marketing and brand
strategy to serve those customers
Customer Behavior Capturing relevant, timely, and granular data on
customer buying behavior and to provide one
view of the customer
Identify customer activity across
multiple channels and brands and
create a total, 360 view of each
customer, identify cross-selling
opportunities
Retail IT Budgets Tight margins may inhibit investment in new
technologies and processes.
Develop methodology accelerators to
reduce implementation costs and
total cost of ownership..
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Foundation for Business Information Flows
Manufacturing/Sourcing
Sales Forecasting
Inventory Tracking
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Retail is being Redefined
Business Areas Insight Requirements
Loyalty
Multi-Channel Sales
Marketing
Prospects & Customer
Category
Store Labor & Operations
Item Price
Forecasting & Scoring
• What are the characteristics of my most loyal customers?
Least loyal?
• How do customers feel about our company and products?
• Which items drive sales? Which items are frequently
purchased together?
• If I discount an item, will impact will it have on sales and
revenue?
• How do my internet sales compare to brick and mortar in
terms of revenue and cost?
• Which prospects should I target to convert into loyal
customers? What products or offers would be most
effective?
• Which combination of employees maximized store
performance?
• Will my inventory levels meet sales forecast? When will we
run out of stock?
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Challengers for getting Actionable Answers
• Data Latency
From event to action, too many hoops
Moving from batch oriented to event oriented
• Shared Semantic Understanding
Common vs. Canonical Foundation
Operational vs. Analytical
• Volume and Velocity
Acquire and Filter
Organize and Analyze
Retention
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Oracle Retail Data Model (ORDM) Overview
- Introduction -
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Oracle Retail Data Model (ORDM) Overview
It speeds the development of a data warehouse solution by providing a foundation data warehouse and analytic infrastructure for the reporting needs of a retail operation
The Oracle Retail Data Model (ORDM) is a start-up kit for implementing a retail data warehouse solution
Based on ARTS 6.0
Extraction of detailed
and summary data
Summaries, trends, and forecasts Knowledge discovery of hidden
patterns and insights
“Information” “Analysis” “Insight and Prediction”
Who purchased
cigarettes in the last 3
months?
What is the average income of
cigarettes buyers by region, by year?
Who will buy cigarettes and beer in
the next 6 months and why?
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Typical Issues Addressed by ORDM (1)
How will a business benefit from using ORDM?
ORDM enables business users to turn retail business data into information based on which users make decisions.
ORDM uses pre-built mining models to detect hidden
information in data repositories, which helps businesses in:
Determining how are my product and Point-Of-Sale performing?
Determining what is my gross margin return on space?
Determining how is the business doing compared to last year? Compared to plan?
Determining what are my potential out-of-stock situations?
Determining if the product assortment is optimal for all my regions
Retaining customers and avoiding churn
Profiling customers to understand behavior
Finding rare events
Targeting customers with the right offer and thus reducing customer acquisition costs
Maintaining and improving profit margins
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Typical Issues Addressed by ORDM (2)
• Market basket analysis
• Frequent shopper analysis
• Identify and predict best customers and undesirable customers
• Calculate the probability of a customer buying Product X
• Detect churn or the probability of a customer switching from brand A to brand B
• Determine best location traits for stores
• Shrinkage analysis
• Associate items in a market basket
• Sales performance prediction
• Employee performance
How will a business benefit from using ORDM data mining functionality?
ORDM data mining packages can be used for:
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ORDM Subject Areas
Store Operations Traffic Patterns, Comparative Store
Performance, What Sells vs. Doesn’t
Point of Sale POS Flow, Shopper Conversion, Transaction
Value, Time of day, Entry Method, Time Series,
Trend
Loss Prevention “Sweetheart” Deals, Outliers for Return-
Discount, Shrinkage, Employee-Basket
Analysis, Trend
Merchandising Item-Basket, Product ‘Stars’ & ‘Dogs’, Frequent
Item Mix, GMROS, Cluster item Traits, Trend
Inventory Sales Anomalies – out-of-stock, zero selling,
Forecast & Score, Time Series, Supplier
Scorecard
Workforce
Management
Measures – AUS, AVS, UPT, Prescriptive
Deployment, SPIFF and Split, Plan vs. Actual
Order
Management
Channel Volume, Web commerce and
interactions, Fulfillment Performance,
Customer Order Analysis
Customer Segment - Formation, Migration, Analysis,
Price Elasticity, RFMP, Churn Model, Trend
Category
Management
Assortment/Product Mix, Clustering , Plan-o-
gram, Customer Purchase vs. Syndicated data,
Trend
Promotion Causal Factor, Lift, Halo Impact, Predictive
Response Model, Predictive cross-sell
Subject Areas Example - Analytic
Database EE
Pa
rtit
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OL
AP
RA
C
Min
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Sp
ati
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Co
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Oracle Retail Data Model
• Data Model with 650+ Tables and 10500+
• Data Model with 650+ Tables and 10500+
attributes
• Industry specific 1200+ Measures & KPI
• Pre-built 15+ OLAP cubes
• Pre-built 12+ Mining Models
• Complete Intra ETL Database Packages
• Well Defined Interfaces
• Sample Reports & Dashboard using OBIEE
• Sample IBM 4690 POS Adapter
Oracle Retail Data Model Includes
DB
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Oracle Retail Business Intelligence Space
Oracle Retail Data Model
Oracle Retail Analytics
Oracle Retail Workspace
Oracle Retail Planning Applications
Oracle Performance Management Applications
Oracle ERP & CRM Business Intelligence Applications
Oracle Business Intelligence Technology
Oracle Retail Operational Applications
Oracle ERP & CRM Operational Applications
Oracle Database
Oracle Fusion Middleware
Oracle Database Appliance Machine - organize and discover data
Oracle Big Data Appliance Machine - stream and acquire data
Oracle Analytics Appliance Machine - visualize, analyze and decide data
Major Components of the application / technical stack
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Complete Oracle Data Warehouse / BI Solution
Oracle
Exadata
Oracle
Exalytics
Oracle Big Data
Appliance
Oracle
Big Data
Connectors Optimized for
Analytics & In-Memory Workloads
“System of Record”
Optimized for DW/OLTP
Optimized for Hadoop,
R, and NoSQL Processing
Oracle BI Applications
Oracle BI Tools
Oracle Enterprise
Performance Management
Oracle Endeca
Information Discovery
Oracle Real-time Decisions
Times Ten
Hadoop
Open Source R
Oracle Event
Processor
Oracle NoSQL
Database
Oracle Big Data Connectors
Oracle Data Integrator
In-D
ata
ba
se
An
aly
tics
Oracle
Advanced
Analytics
Oracle
Database
Retail Data
Model
Stream Acquire Organize Discover & Analyze
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ORDM Technical Architecture
Data Sources
Point of Sale
E-Commerce
Partner
Channels
Merchandising
Loyalty/CRM
Financial ERP
Allocations
Social Media
Competitive
Supply Chain
Derived Tables
Foundation Layer
Analytic Layer
Presentation Layer
Data
Co
llecti
on
an
d T
ran
sfo
rmati
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Exadata
Big Data Appliance Exalytics
Exalogic Exalogic
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ORDM Components Relationship
Relationship between each component of the ORDM product
Oracle Retail Data Model
ORDM Foundation & Reporting Layers Staging Area -Data Quality
-ETL Rules
- Interface, etc.
Source Data -OLTP System
-Data Marts
-MDM, etc.
Landing Zone -Tables / Views
-CSV Files, etc.
• Master data is stored in Reference and Lookup tables
• Base tables stores only transactional data (3NF)
– Transactional Reporting
• Fact data is stored in Derived and Aggregated tables
– Analytical Reporting
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Sample Analytical Report (Forecasting)
Sales Trends vs Stock predict Out of Stock
• Demonstrates predictive analysis on sales forecast and inventory stock
• Oracle Retail Data Model provides many embedded forecast algorithms
• Oracle Exadata provides extreme performance for daily POS transactions
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Sample Analytical Report (Product Category Mix Analysis)
• Shows Market Basket Analysis using Key Performance Indicators (KPIs)
• Oracle Retail Data Model provides flexibility to identify correlations and
their strength
• Report contains additional qualifying Basket/Component KPIs to identify
“interesting”/”useful” rules
• Oracle Exadata provides extreme performance for ultra-fast cross sell
analysis
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Sample Analytical Report (Price Elasticity Analysis)
• Retail: Price Elasticity helps determine the effect of applying a discount on a
particular Item/SKU and analyze the impact on the bottom line (Revenue)
• This report allows the analyst to interact with the Mining Model via a
dynamic application of the Discount %
Can fine tune the discount % (not just steps of 1 but arbitrary value
keyed in textbox by the analyst)
Can apply it to a specific product and interactively see the impact on
Revenue
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Sample Data Mining Packages and Data Model Types
Model Model ETL Package Model Creation Package Rule
Associate Basket Analysis Model PKG_POP_DM_ASSBAS PKG_RBIW_DM_ASSBAS ABN, DT
Associate Loss Analysis Model PKG_POP_DM_ASSLOSS PKG_RBIW_DM_ASSLOSS ABN, DT
Associate Sales Analysis Model PKG_POP_DM_ASSSLS PKG_RBIW_DM_ASSSLS ABN, DT
Customer Category Mix Analysis
Model
PKG_POP_DM_CUSTCATGMIX PKG_RBIW_DM_CUSTCATGMIX APASS
Customer Loyalty Analysis Model PKG_POP_DM_CUSTLTY PKG_RBIW_DM_CUSTLTY ABN, DT
Frequent Shopper Category Mix
Analysis Model
PKG_POP_DM_FSCATGMIX PKG_RBIW_DM_FSCATGMIX APASS
Item Basket Analysis Model PKG_POP_DM_ITMBAS PKG_RBIW_DM_ITMBAS ABN, DT
Item POS Loss Analysis Model PKG_POP_DM_ITMPOSLOSS PKG_RBIW_DM_ITMPOSLOSS ABN, DT
POS Flow Analysis Model PKG_POP_DM_POSFLOW PKG_RBIW_DM_POSFLOW ABN, DT
Store Loss Analysis Model PKG_POP_DM_STRLOSS PKG_RBIW_DM_STRLOSS ABN, DT
ABN = Adaptive Bayes Network DT = Decision Tree A = Apass (Market Basket Analysis)
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Sample Analytical Report (Customer Loyalty Analysis)
• Identifies attributes that have significance in predicting loyalty
• Segment Customers and determine loyalty
• Can apply findings to identify prospects who fit the loyalty profile
• Oracle Exadata quickly finds transactions of customers in a given loyalty
category
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Q & A