Recom Retail Solution

Introduction

• Retail Model is centered around manufacturer in countries like India

• This model is designed for forward integration and backward integration

• Model driver is supply and demand• Major Control is in hands of

Government

Business Intelligence

• Financial Parameters

• Economic Parameters

• Customer Parameters

• Sale Parameters

• Human Resource Parameters

• Internal Processes Parameters

Customized Solutions

• Solutions for Quick response to changing market conditions

• Critical Reports available at Intranet, email and desktop applications

• Integration of Corporate strategy and performance

• Increases overall efficiency, profitability and market share

• Information Security maintained over the value chain

Critical Business Information Processing

• Critical business information is processed quickly which otherwise can take days and even months to consolidate

• Large amount of data analyzed and processed

• Forecasting tools using Artificial Intelligence have uncanny predicting abilities

Analyzing Dimensions

• Business information is analyzed on country-wise, region-wise, state-wise or city-wise

• Analysis using Branch wise on hundreds of parameters• Branch-wise and year-wise, quarter-wise, month-wise

or date-wise• Customer-wise sales and trends• Forecasting trends on multiple parameters and

dimensions• Associating Customers for sale trends• Creating Cluster • Up-Selling and Cross-selling• Arranging Display Racks of Products using AI Tools • Creating Sales Campaign using AI tools

Resource Optimization

• Optimizing Manpower Deployment• Optimizing Sales• Optimizing Cash Flow• Optimizing Machinery Investments and

Deployment• Controlling Waste and Rejects• Optimization of Quality Checks• Optimization of Time Delays

Dimensional Measure and Aggregation

Naïve Bayes

Sales Vs Geography Vs Time

Customer Vs Geographical Dimension

Customer Dimension

Geographical Dimension

Employee VS Sales Hierarchical Dimension

Employee Vs Sales Dimension

Setting BucketProperty

Product Dimension

Time Dimension

Relationship Diagram

Dimensions and Measure Groups

Many to Many Relationships

Applying Calculations to Dimensions

Yearly Gross Profit Margin on Sales

Expanding Product Category

Creating Sub Cubes within Multi Dimensional Cubes

Key Performance Indicators

Time Series Prediction

Input column content typesContinuous, Cyclical, Discrete, Discretized, Key, Table, and Ordered

Predictable column content typesContinuous, Cyclical, Discrete, Discretized, Table, and Ordered

Modeling flagsMODEL_EXISTENCE_ONLY, NOT NULL, and REGRESSOR

IsDescendant PredictNodeId

IsInNode PredictProbability

PredictAdjustedProbability PredictStdev

Decision Trees

Clustering

Market Basket Analysis

• Consider shopping cart filled with severalitems

• Market basket analysis tries to answer thefollowing questions:

– Who makes purchases?– What do customers buy together?

– In what order do customers purchase items?• Given a database of customer transactions,each transaction is a set of items – deduce

association rules.

Examples of Market BasketAnalysis

• Co- ocurrences – 80% of all customerspurchase items a, b, and c together.

• Association Rules – 60% of all customerswho purchase X and Y also buy Z.

• Sequential Patterns – 60% of customerswho first buy X also purchase Y within two

weeks.

Confidence and Support• We prune the set of all possible association

rules using two measures of interest:– Confidence of a rule: X -> Y has confidence c if

P( Y| X)= c.– Support of a rule: X-> Y has support s ifP( XY)= s. Also, support of an itemset XY.

• Direct Marketing• Fraud Detection for Medical Insurance• Floor/ Shelf Planning• Web Site Layout• Cross- selling

Applications

Frequent Itemsets Applications

– Classification– Seeds for constructing Bayesian networks– Web log analysis– Collaborative filtering

Association Rules Approaches• Problem Reduction• Breadth- First Search• Depth- First Search

EnvironmentInteroperability: key components (client/network/server) work together.Salability: any of the key elements may be replaced when the need to either grow or reduce processing for that element dictates, without major impact on the other elements.Adaptability: new technology (multi-media, broad band networks, distributed database, etc.) may be incorporated into the system.Affordability: using less expensive insures cost effectiveness MISs which available on each platform.Data Integrity: entity, domain and referential integrity are maintained on the database server.Accessibility: data may be accessed from WANs and multiple client applications.Perform: performance may optimize by hardware and process.Security: data security is centralized on the server.

Data Warehouse

One or more tools to extract fields from any kind of data structure (flat, hierarchical, relational, or object) including external data.

The synthesis of the data into a nonvolatile, integrated, subject oriented database with a metadata “catalog.”

All AI applications on Data Warehouse

Data Warehouse Advantages

• Improves product inventory turns • Costs of product introduction are decreased, with

improved selection of target markets• Separating query processing from running against

operational databases enables most cost-effective decision making

• Improved productivity, by keeping all required data in single location and eliminates rekeying of data

• Reduced redundant processing, support, and software to support overlapping decision support applications

• Enhanced customer relations through improved knowledge of individual requirements and trends, through customization, improved communications

Data Ware House Design Considerations

• Heterogeneity of data sources, which affects data conversion, equality, timeless

• Use of historical data, which implies that data may be “old”

• Tendency of databases to grow very large • The database management system that supports the

warehouse database • The communications infrastructure that connects the

ware- house, data marks, operational system, and end users

• The systems management framework that enables centralized management and administration of the entire environment

Data Mining

• Identification of Patterns to guide marketing• Discovering meaningful new correlation, patterns, and

trends by digging into (mining) large amounts of data • Using artificial intelligence (AI) and statistical and

mathematical techniques • Data mining uses well-established statistical and

machine learning techniques to build model that predict customer behavior

• Automating the mining process, integrating it with commercial data warehouses, and presenting it in relevant way for business users

Data Mining Continued

• Data mining techniques can yield the benefits of automation when implemented on existing software and hardware platform and can be implemented on new systems as existing upgraded and new products developed

• When data mining tools are implemented on high performance parallel processing systems, they can analyze massive database in minutes

• Data mining software can help to find the high-profits gems buried in mountains of information

Database Selection and Preparation

• This step includes the identification of databases and factors to be explored

• Whenever possible, required records can be retrieved using a live data dictionary

• Data preparation includes filling in missing values and removing errors

Analysis

• The large database groups defined during the preparation phase are further divided using clustering techniques

• Determine which factors are involved in the maximization of particular goals

Row Labels Internet Order Count Internet Average Sales Amount Internet Average Unit Price Internet Extended Amount Internet Freight Cost

CY Q1 6,984 1072.287903 490.9439303 $7,488,858.71 $187,222.15

Accessories 4,619 37.57320416 19.11140073 $173,550.63 $4,339.16

Accessories 4,619 37.57320416 19.11140073 $173,550.63 $4,339.16

Bikes 3,853 1876.266344 1876.266344 $7,229,254.22 $180,731.53

Bikes 3,853 1876.266344 1876.266344 $7,229,254.22 $180,731.53

Clothing 1,930 44.58749223 37.09218103 $86,053.86 $2,151.45

Clothing 1,930 44.58749223 37.09218103 $86,053.86 $2,151.45

CY Q2 8,021 1131.331797 511.7246006 $9,074,412.34 $226,861.10

Accessories 5,171 38.62985496 19.55889357 $199,754.98 $4,994.32

Accessories 5,171 38.62985496 19.55889357 $199,754.98 $4,994.32

Bikes 4,883 1797.313654 1797.313654 $8,776,282.57 $219,407.29

Bikes 4,883 1797.313654 1797.313654 $8,776,282.57 $219,407.29

Clothing 2,170 45.33400461 37.30557072 $98,374.79 $2,459.49

Clothing 2,170 45.33400461 37.30557072 $98,374.79 $2,459.49

CY Q3 5,851 964.884227 451.4985295 $5,645,537.61 $141,138.99

Accessories 3,906 39.02180492 19.56851586 $152,419.17 $3,810.81

Accessories 3,906 39.02180492 19.56851586 $152,419.17 $3,810.81

Bikes 2,774 1953.86977 1953.86977 $5,420,034.74 $135,501.00

Bikes 2,774 1953.86977 1953.86977 $5,420,034.74 $135,501.00

Clothing 1,564 46.72870844 37.65260175 $73,083.70 $1,827.18

Clothing 1,564 46.72870844 37.65260175 $73,083.70 $1,827.18

CY Q4 6,803 1050.987587 479.6316196 $7,149,868.55 $178,747.38

Accessories 4,512 38.79325798 19.42892441 $175,035.18 $4,376.27

Accessories 4,512 38.79325798 19.42892441 $175,035.18 $4,376.27

Bikes 3,695 1865.37838 1865.37838 $6,892,573.11 $172,314.50

Bikes 3,695 1865.37838 1865.37838 $6,892,573.11 $172,314.50

Clothing 1,797 45.77643851 37.34010894 $82,260.26 $2,056.61

Clothing 1,797 45.77643851 37.34010894 $82,260.26 $2,056.61

Grand Total 27,659 1061.451145 486.0869105 $29,358,677.22 $733,969.61

Typical Sales Report for Internet Sales

Data Mining as an Data Mining as an Application PlatformApplication Platform

What is Data Mining Anyway?

• Machine learning of patterns in data

• Application of patterns to new data

What is Data Mining Anyway?

• Machine learning of patterns in data

• Application of patterns to new data

Comparative BenefitsPredictive Projects versus Nonpredictive

Projects

“Data Mining is Hard”• “White-coats” only need apply• How do you

– … define problem?– … select data?– … choose inputs?– … choose outputs?– … interpret results?– … validate results?

What Does Data Mining Do?

Explores Explores Your DataYour Data

Finds Finds PatternsPatterns

Performs Performs PredictionPrediction

ss

Mining Model

What does Data Mining do?Illustrated

DMEngine

Data To Predict

DMEngine

Predicted Data

Training Data

Mining Model

Mining Model

DB dataDB dataClient dataClient dataApplication dataApplication data

DB dataDB dataClient dataClient dataApplication dataApplication data““Just one rowJust one row””

Analysis ServicesAnalysis ServicesServerServer

Mining ModelMining Model

Data Mining AlgorithmData Mining Algorithm DataDataSourceSource

Server Mining Architecture

Your ApplicationYour Application

OLE DB/ ADOMD/ XMLAOLE DB/ ADOMD/ XMLA

DeployDeploy

BI Dev BI Dev StudioStudio (Visual (Visual Studio)Studio)

AppAppDataData

““Putting Data Putting Data Mining to Work”Mining to Work”

““Doing Data Doing Data Mining”Mining”

Business Business UnderstandingUnderstanding

Data Data UnderstandingUnderstanding

Data Data PreparationPreparation

ModelingModeling

EvaluationEvaluation

DeploymentDeployment

DataData

Data Mining ProcessCRISP-DM

www.crisp-dm.orgwww.crisp-dm.org

SSASSSAS(Data(Data

Mining)Mining)

SSAS SSAS (OLAP)(OLAP)DSVDSV

SSISSSISSSAS(OLAP)SSAS(OLAP)SSRSSSRSFlexible APIsFlexible APIs

SSISSSISSSASSSAS(OLAP(OLAP))

Business Business UnderstandingUnderstanding

Data Data UnderstandingUnderstanding

Data Data PreparationPreparation

ModelingModeling

EvaluationEvaluation

DeploymentDeployment

DataData

Data Mining Process in SQLCRISP-DM

www.crisp-dm.orgwww.crisp-dm.org

DataData

What Do Data Mining Applications Do?

Explores Explores Your DataYour Data

Finds Finds PatternsPatterns

Performs Performs PredictionPrediction

ss

Automatic Automatic MiningMining

Pattern Pattern ExplorationExploration

Perform Perform PredictionPrediction

ss

Algorithm Training

Algorithm Module

Case Processor

(generates and prepares all

training cases)

StartCases

Process One Case

Converged/complete?NoYes

Done!

Persist patterns

DM data flow

Cube

HistoricalDataset

NewDataset

Data Transform (DTS)Reporting

Mining Models

ModelBrowsing

Prediction

LOBApplication

Cube

PredictionParser Validation-I &

InitializationAST

Binding &Validation-II

DMX treeExecution Planning

DMX tree

Input data

Read / Evaluate one row

Push response

Untokenize results

Income Gender

$50,000 F

1 2

50000 2

1 2 3

50000 2 1

Income Gender Plan

$50,000 F Attend