Business IntelligenceSolutions

for theRetail Industry

Syscon Infotech Pvt.Ltd.# 250,5B – Sanjay Building, Mittal Industrial Estate

Marol Naka, Andheri –Kurla RoadAndheri –East

Mumbai -400 059.Tel:0091-22-40622400Fax:0091-22-40622410

E-mail: sales@sysconinfotech.com

About Us

A professionally managed company with over 15 years of experience spanning Consulting, customized technology development, implementation and training.

Syscon Differentiators as a Source of Competitive Advantage

Ability to Recommend

Power to Implement

Speed, Quality & Flexibility

• Focus on Customer needs & Business values

• Multi-Vendor Skills

• 15 Years Experience

• Strong Manpower resources and capabilities.

• Broad portfolio of solutions and services.

• Proven Methodology and Tools.

• Full Cycle of Implementation experience.

• Flat and flexible structure.

What is Business Intelligence ?

Business intelligence (BI) is a process for increasing the competitive advantage of a business by intelligent use of available data in decision making.

Business intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information and sometimes to the information itself.

BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support the use of this information by assisting in the extraction, analysis, and reporting of information.

BI is a set of concepts and methods to improve business decision making by using fact based support systems. It refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information.

BI systems provide historical, current, and predictive views of the business operations. They combine data management (consolidating, organizing, cleansing huge amounts of disparate data from varying systems and platforms) with predictive analytics (data mining, forecasting, and data optimization).

Relevance of BI in Retail Industry

Globalization, deflation, diversification of sales channels and, most of all, changing customer demands have merged to create a cutthroat environment in which retailers struggle to turn a profit.  Sales remain flat as many companies don't understand customer behavior and buying habits well enough to make the right decisions about product, price, promotion and placement. And without the ability to explore every facet of the organization across business units and geographies, it can be a struggle to understand and manage the costs and other drivers required to do business.

Syscon BI Solutions for retail turn data about customers, merchandise and operations into knowledge that provides greater insight into performance and empowers retailers to make more informed decisions, gain a competitive advantage, strengthen customer and vendor loyalty, and improve profitability.

The Gap

Technology plays an important role in supporting the backbone of retail businesses. Typically, in a retail environment, operational and transaction systems, such as Point of Sales (POS) systems are efficient in what they are intended to do – record and retrieve large volumes of transactions and operations. Embedded in the POS is a “treasure trove” of dormant often unused information about what has happened in the business in the last week, last month, last year, etc. Traditional reporting systems present historical information in standard static layouts. These reports can neither be viewed from different perspectives at deferent times nor can they provide critical insight for retailers to help them make basic operational decisions.

Realizable Value

Real value comes from systems that go beyondthe limitations of operational software alone, andtake the operational data to create enterpriseintelligence and predictive insights.With this information retailers can make sense ofcustomer, product, supplier, and operational dataand draw insights that will help them run theirbusinesses better and more profitably. This isexactly where Business Intelligence comes intoplay.

Data Sources:Papers, Files, Databases

Data Warehouses / Data Marts:Analyzed, Processed, Aggregated, Organized data

Data Exploration:Querying and Reporting the Organized data

Data Mining:Discovery of Information from the Data

Making Decisions

Data Presentation:Visual, Tabular, Graphical views

Of the Information

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Syscon Retail BI solutions

Large to medium size retail organizations have adopted ERPs successfully, resulting in automation of all their transaction processing. This has now created a good foundation (and opportunity) for Business Intelligence applications in terms of:

Businesses possess huge and rich data resource.

Businesses have seen the benefits of huge investments in IT.

Businesses are keen to have insights into their own performance and discover opportunities for improvement on continuous basis.

Businesses would like to discover new business opportunities from their existing customer base, market reach and so on.

Communications Gap in Business Intelligence

Though retail houses implemented sophisticated systems for each functional points, most of the cases that do not communicate with each other not effectively integrated into a common “analytical layer” that utilizes common databases and information delivery mechanisms. As a result, even at the biggest retail chains, the larger dimensions of Business Intelligence — analytics, applications and platforms — can be surprisingly archaic .

Our Offerings

Syscon Retail BI Solutions

Customer Intelligence

Merchandise Intelligence

Operations Intelligence

Supplier Relationship Management

Financial Intelligence

Human Capital

Management

Performance Management

Solutions

Activity Based

Management

Strategic Performance Management

Customer Intelligence

Helps retailers identify, acquire, activate, serve and retain the most profitable customers.

Merchandise Intelligence

Helps retailers drive revenue, protect margins and earn customer loyalty with optimized merchandise plans, assortments, pricing, promotions – all driven by unparalleled demand forecasting and predictive analytics.

We provide complete planning capabilities for the merchandising process, including performance analysis, financial planning, assortment planning and more.

Operations Intelligence

Helps retailers leverage organizational assets to trade with vendors and serve customers more efficiently and profitably.

Supplier Relationship Management Helps establish sound supplier evaluation

practices and reduce enterprise spend by consolidating and prioritizing your supplier base and reducing supplier risk. This solution offers strategy alignment ,commodity classification, opportunity exploration, detailed analysis and decision support.

Financial Intelligence

Helps retailers focus on specific financial business processes – planning, reporting, budgeting, consolidation, risk assessment, forecasting, strategy development, the audit process – and develop more predictive, accurate, relevant and timely results.

Human Capital Management

offers the organizational insights that enable retail organisations to plan effective human capital strategies and then measure and compare their company's best practices.

Performance Management SolutionsProvide the ability to analyze, forecast and

maximize profits across the entire retail enterprise by monitoring cost and performance, helping retailers drive disparate functional units toward common goals.

Activity Based Management

provides accurate financial information in a form that mirrors the day-to-day activities of the people, equipment and processes that directly impact a retailer's bottom line. This solution provides profitability analysis and forecasting to help retailers look to the future with a reliable picture of operating costs.

Strategic Performance Management allows executives to track key

performance indicators (KPIs) across the entire retail enterprise, from merchandising and marketing to distribution and store operations, to analyze, learn and plan strategically. Executives can then quickly communicate goals and strategies throughout the organization.

Skill Set

Data Management• Manage large volume of data • Building Data Warehouse or Data Mart• Building OLAP Cubes• Building ETL processes for extracting and transforming data from

independent systems• Working on multiple platforms – MS SQL, Oracle, SAS

OLAP • Building data ‘cubes’• Studying data patterns by slicing, dicing and drilling• Making inference

Statistical Analysis• Exploratory analysis• Confirmatory analysis• Model building• Simulations• Making inference

Data Mining• Sampling• Building valid models • Making predictions – scoring

Analytics Scope - Illustrated

What sells where and how Programs Promotion Membership Channel Market Product Location Time

Key Data Elements Sales and Growth (Targets if Any) Frequency of Purchases Avg. Sales Value per Transaction Avg. Sales Value per Customer

per Month No. of Items per Transaction

Analysis Techniques (Illustrative)

Data Mining Clustering Market Basket Analysis

Statistical Distribution Analysis Pareto Analysis Trend Analysis Correlations

OLAP High performers Low performers Outliers

People

Team of 150 people consisting of: Statisticians (Ph.D. and Masters in Statistics) Statistical software developers (Masters in Statistics) Microsoft SAS Data Analysts and Business Intelligence solution

designers and developers (MBAs and Masters in Statistics) Data Managers (MCAs) Information Technology managers (Engineers and

MCAs)

Execution Approach

Set Up BI Platform Build Data Warehouse, including Data Cleansing Data Updated – Weekly / Monthly Provide on-line access to Client Managers and Agency Experts Theme based Analytics Services Results to be Published on BI Platform

Critical Success Factors

Executive sponsorship is key for corporate support

Decisive project management Proactive management of scope Meeting deliverables Understanding the solution is evolutionary Dedicated project team resources Data quality extracted from source systems

Primary factors impacting the length of a DW&BI project

In general, a DW & BI Project will be of shorter duration and will more likely be successful if:

A predefined data model, specific to the industry, is used,

the team is skilled and committed, the team includes end users who understand the

business processes and their data, there is a clear, valuable objective of the project, executive level support is strong, the source system(s) is well-defined; and the technical support team is strong (data integration,

data modeling)

Typical BI & DW projects risks

Project scope not defined well Bad communicationNo decisions & decision escalation

processesLack of or little management supportCustomer team availabilityIncomplete or missing data sources

Syscon Experience

Syscon brings rich experience in BI-DW space with several man-years of design and development experience. Important projects executed

Target, Profit Logic – End to End BI Consulting and solution delivery.

Bharat Petroleum, India – monitoring or refinery production and inventory movement.

One of the largest news papers in India – monitoring of advertisement share of different media / publishers.

Large hotel in India – monitoring of occupancy, customer acquisition / churn and profitability.

HR Management for a large Software company in India – monitoring manpower addition, churn, deployment and movement.

Current Projects in India

Aditya Birla Retail Ltd.- Creation of a integrated BI platform and portal.

Shoppers Stop Ltd.- BI Analytical tool for the study of re-order behavior.

Planet M – End to End to BI Platform.

Fast Growing Retailer in India

Case Study

Fast Growing Retailer in India

Case Study

Goals of Loyalty Program

Increase MembershipsIncrease Sale Value Per Member per

MonthIncrease Realization per BillIncrease in Basket SizePromote purchase of higher value itemsPromote sales of Private Label products

Service Level EstablishedService (Measure) Level Established

Upload Sales Data (Days from Receipt)

1 (Monthly – should be weekly)

New Report (Days from Request)

1

On-line Access to BI Platform

24x7

Performance Analysis Reports

Weekly

Progress Report Weekly

Contribution by Top 25 Cities/Stores to Membership

No of Members

42%

0%

0%4%

0%1%1%0%2%0%0%0%0%1%5%0%

4%

6%

3%

0%

5%

19%

5%

0%

1%

Ahamedabad

Ahmdabad

Ahmedabad

Baroda

Delhi

Faridabad

Gaziabad

Ghaziabad

Gurgaon

Jaipur

KALYAN

Kamothe

KOLKATA

Mumbai

Nangloi

Navi Mumbai

New Delhi

New Mumbai

Noida

NULL

Pune

THANE

ULHASNAGAR

VADODARA

Drop Page Fields Here

Customer City

Data

Sales Value

27%

0%

0%

6%

0%1%1%0%

2%0%0%0%0%1%

5%0%

5%7%2%

0%

5%

31%

5%

0%

1%

Ahamedabad

Ahmdabad

Ahmedabad

Baroda

Delhi

Faridabad

Gaziabad

Ghaziabad

Gurgaon

Jaipur

KALYAN

Kamothe

KOLKATA

Mumbai

Nangloi

Navi Mumbai

New Delhi

New Mumbai

Noida

NULL

Pune

THANE

ULHASNAGAR

VADODARA

Drop Page Fields Here

Sales (in Lacs)

Customer City

Drop Series Fields Here

Which Cities/Stores are High Performing?Customer City Sales (in Lacs) % to Total No of Members % to Total Average Bill Value (INR)

Pune 1012 31.31% 59749 19.42% 27866 26.80% 128087 41.63% 36

New Delhi 218 6.76% 18876 6.13% 35Ahmedabad 195 6.05% 13182 4.28% 29Mumbai 168 5.21% 15349 4.99% 30THANE 158 4.87% 13959 4.54% 33Navi Mumbai 157 4.85% 10882 3.54% 27NULL 154 4.78% 13996 4.55% 30Ghaziabad 78 2.40% 7133 2.32% 41New Mumbai 51 1.57% 8783 2.85% 28Delhi 39 1.20% 2866 0.93% 34VADODARA 33 1.01% 3281 1.07% 32KOLKATA 32 0.97% 4111 1.34% 37Faridabad 19 0.58% 1953 0.63% 42Ahmdabad 10 0.30% 660 0.21% 34Jaipur 8 0.24% 1108 0.36% 33Gurgaon 7 0.22% 761 0.25% 37Baroda 6 0.19% 457 0.15% 33Gaziabad 6 0.18% 249 0.08% 57ULHASNAGAR 5 0.16% 1221 0.40% 32Noida 4 0.11% 245 0.08% 37KALYAN 3 0.08% 458 0.15% 39Nangloi 2 0.06% 104 0.03% 47Ahamedabad 2 0.05% 84 0.03% 27Kamothe 1 0.02% 133 0.04% 23

3231 307687 34

Which Cities/Stores Give High Value per Bill?

Customer City Sales (in Lacs) % to Total No of Members % to Total Average Bill Value (INR)Gaziabad 6 0.18% 249 0.08% 57Nangloi 2 0.06% 104 0.03% 47Faridabad 19 0.58% 1953 0.63% 42Ghaziabad 78 2.40% 7133 2.32% 41KALYAN 3 0.08% 458 0.15% 39Gurgaon 7 0.22% 761 0.25% 37KOLKATA 32 0.97% 4111 1.34% 37Noida 4 0.11% 245 0.08% 37

866 26.80% 128087 41.63% 36New Delhi 218 6.76% 18876 6.13% 35Ahmdabad 10 0.30% 660 0.21% 34Delhi 39 1.20% 2866 0.93% 34Jaipur 8 0.24% 1108 0.36% 33Baroda 6 0.19% 457 0.15% 33THANE 158 4.87% 13959 4.54% 33VADODARA 33 1.01% 3281 1.07% 32ULHASNAGAR 5 0.16% 1221 0.40% 32NULL 154 4.78% 13996 4.55% 30Mumbai 168 5.21% 15349 4.99% 30Ahmedabad 195 6.05% 13182 4.28% 29New Mumbai 51 1.57% 8783 2.85% 28Ahamedabad 2 0.05% 84 0.03% 27Navi Mumbai 157 4.85% 10882 3.54% 27Pune 1012 31.31% 59749 19.42% 27Kamothe 1 0.02% 133 0.04% 23

3231 307687 34

Note High Sales Value Cities do not

give High Value per Bill

City A: Membership has stopped Increasing but Sales to Members is Increasing

Membership - Pune

0

20

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160

2007-07(Jul)

2007-08(Aug)

2007-09(Sep)

2007-10(Oct)

2007-11(Nov)

2007-12(Dec)

2008-01(Jan)

2008-02(Feb)

2008-03(Mar)

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25000

30000

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Sales (in Lacs)

No of Members

Customer City (Multiple Items)

Time Year Month

Data

Data Sales MembershipTime Year Month Sales (in Lacs) No of Members Avg Growth Rate Avg Growth Rate2007-07(Jul) 22 67662007-08(Aug) 105 220692007-09(Sep) 120 236022007-10(Oct) 126 246742007-11(Nov) 125 226542007-12(Dec) 150 23991 20.30% 5.90%2008-01(Jan) 94 18242 -37.19% -23.96%2008-02(Feb) 128 20487 35.75% 12.31%2008-03(Mar) 143 19913 11.54% -2.80%Average 7.60% -2.14%

Average Sales Value per Member

0

100

200

300

400

500

600

700

2007-07(Jul)

2007-08(Aug)

2007-09(Sep)

2007-10(Oct)

2007-11(Nov)

2007-12(Dec)

2008-01(Jan)

2008-02(Feb)

2008-03(Mar)

Month

Sal

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Total

Linear (Total)

Customer City All

Average Sales Amount Per Member (INR)

Time Year Month

Average Sales Value per MemberAverage Sales Amount Per Member (INR)Time Year Month Total % Change2007-07(Jul) 3352007-08(Aug) 453 35.34%2007-09(Sep) 482 6.31%2007-10(Oct) 475 -1.42%2007-11(Nov) 529 11.37%2007-12(Dec) 572 8.15%2008-01(Jan) 550 -3.90%2008-02(Feb) 540 -1.84%2008-03(Mar) 591 9.39%Average Growth 7.92%

Delhi: Average Sales Value per Member

Average Sales Value per Member

0

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2007-07(Jul) 2007-08(Aug)

2007-09(Sep)

2007-10(Oct)

2007-11(Nov)

2007-12(Dec)

2008-01(Jan)

2008-02(Feb)

2008-03(Mar)

Month

Sal

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Total

Customer City Delhi

Average Sales Amount Per Member (INR)

Time Year Month

Customer City Delhi

Average Sales Amount Per Member (INR)Time Year Month Total % Change2007-07(Jul) 3362007-08(Aug) 273 -18.70%2007-09(Sep) 245 -10.12%2007-10(Oct) 403 64.18%2007-11(Nov) 524 30.04%2007-12(Dec) 642 22.56%2008-01(Jan) 738 14.85%2008-02(Feb) 768 4.05%2008-03(Mar) 785 2.23%Average Growth 13.64%

Trend in Each Category Sales

0

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300

2007-07(Jul)

2007-08(Aug)

2007-09(Sep)

2007-10(Oct)

2007-11(Nov)

2007-12(Dec)

2008-01(Jan)

2008-02(Feb)

2008-03(Mar)

Accessories

Books, Music, Movies, Toys & Stationery

Boy's Apparels

Girls Apparel

Grocery

Home

Home & Personal Care

Home Products

Infants(0 -2 yrs)

Kids/Infants Footwear

Leisure

Men's Apparel

Men's Footwear

NA

Perishables

Processed Food

Small Appliances

Sports wear

Women's Apparel

Women's Footwear

Sales (in Lacs)

Time Year Month

Category

Males are More Likely to Buy Own Label

Sales

0

0.05

0.1

0.15

0.2

0.25

2007-12(Dec) 2008-01(Jan) 2008-02(Feb) 2008-03(Mar)

Female

Male

Label Own

Sales (in Lacs)

Time Year Month

Gender

Growth in Sales by Division and Top Selling Sub Categories

Sales

0.00

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Hom

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and

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HPC Perishables & grocery Processed Food

2007-07(Jul)

2007-08(Aug)

2007-09(Sep)

2007-10(Oct)

2007-11(Nov)

2007-12(Dec)

2008-01(Jan)

2008-02(Feb)

2008-03(Mar)

Brand All

Sales (in Lacs)

Division Sub Category

Time Year Month

Some examples of report generations

Report Name : Store Classification Report

Frequency : Weekly / Monthly

Report Structure :

  Total No of stores Total Existing Stores Total New stores Stores > = 6 

monthsStores < 6 months

Zone / Region Budget Actual Variance Budget Actual Variance Budget Actual Variance Actual Actual

Total                      

ROI                      

South                      

Thank You

Contact

Mr. Nilay Jhaveri

Mobile:+91 - 9820036140

E-mail: nilay@sysconinfotech.com

Mr.Anish Pillai

Mobile:+91 - 9820081957

E-Mail: anish@sysconinfotech.com