business intelligence solutions for the retail industry
DESCRIPTION
Business Intelligence Solutions for the Retail Industry. Syscon Infotech Pvt.Ltd. # 250,5B – Sanjay Building, Mittal Industrial Estate Marol Naka, Andheri –Kurla Road Andheri –East Mumbai -400 059. Tel:0091-22-40622400 Fax:0091-22-40622410 E-mail: [email protected]. About Us. - PowerPoint PPT PresentationTRANSCRIPT
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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: [email protected]
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About Us
A professionally managed company with over 15 years of experience spanning Consulting, customized technology development, implementation and training.
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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.
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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.
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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).
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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.
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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.
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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.
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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
Incr
easi
ng
Po
ten
tial
to
su
pp
ort
Bu
sin
ess
Dec
isio
ns
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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.
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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 .
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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
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Customer Intelligence
Helps retailers identify, acquire, activate, serve and retain the most profitable customers.
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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.
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Operations Intelligence
Helps retailers leverage organizational assets to trade with vendors and serve customers more efficiently and profitably.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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
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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)
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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
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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
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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)
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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
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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.
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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.
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Fast Growing Retailer in India
Case Study
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Fast Growing Retailer in India
Case Study
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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
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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
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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
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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
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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
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City A: Membership has stopped Increasing but Sales to Members is Increasing
Membership - Pune
0
20
40
60
80
100
120
140
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)
Month
Sal
es V
alu
e (R
s. in
Lac
s)
0
5000
10000
15000
20000
25000
30000
No
. of
Mem
ber
s
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%
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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
es V
alu
e
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%
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Delhi: Average Sales Value per Member
Average Sales Value per Member
0
100
200
300
400
500
600
700
800
900
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
es V
alu
e
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%
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Trend in Each Category Sales
0
50
100
150
200
250
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
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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
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Growth in Sales by Division and Top Selling Sub Categories
Sales
0.00
50.00
100.00
150.00
200.00
250.00
300.00
Hom
e C
are
Per
sona
l Car
e
Dai
ry
Fre
sh
Fro
zen
Pre
pare
dF
oods
Sta
ples
Bab
y F
ood
Bre
akfa
stF
ood
Col
dB
ever
ages
Cul
inar
yP
rodu
cts
Hea
lth D
iet
Foo
d
Hot
Bev
erag
es
Inst
ant F
ood
Sna
cks
and
Con
fect
iona
ry
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
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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
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Thank You
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Contact
Mr. Nilay Jhaveri
Mobile:+91 - 9820036140
E-mail: [email protected]
Mr.Anish Pillai
Mobile:+91 - 9820081957
E-Mail: [email protected]