Oracle Retail Data Model (ORDM) Overview

May , 2014

1

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

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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

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an

d T

ran

sfo

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on

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