Business Intelligence Solutions for the Retail Industry

1. Business IntelligenceSolutionsfor theRetail 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: sales@sysconinfotech.com

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

3. Syscon Differentiators as a Source of Competitive Advantage Ability to Recommend • Focus on Customer needs & Business values • Multi-Vendor Skills • 15 Years Experience • Strong Manpower resources and capabilities. • Broad portfolio of solutions and services. Power to Implement Speed, Quality & Flexibility • Proven Methodology and Tools. • Full Cycle of Implementation experience. • Flat and flexible structure.

4. 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.

5. 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).

6. 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.

7. 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.

8. Realizable Value • Real value comes from systems that go beyond the limitations of operational software alone, and take the operational data to create enterprise intelligence and predictive insights. With this information retailers can make sense of customer, product, supplier, and operational data and draw insights that will help them run their businesses better and more profitably. This is exactly where Business Intelligence comes into play.

9. Making Decisions Data Presentation: Visual, Tabular, Graphical views Of the Information Increasing Potential to support Business Decisions Data Mining: Discovery of Information from the Data Data Exploration: Querying and Reporting the Organized data Data Warehouses / Data Marts: Analyzed, Processed, Aggregated, Organized data Data Sources: Papers, Files, Databases

10. 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.

11. 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 .

12. Our Offerings

13. Customer Intelligence • Helps retailers identify, acquire, activate, serve and retain the most profitable customers.

14. 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.

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

16. 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.

17. 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.

18. 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.

19. Performance Management Solutions • Provide 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.

20. 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.

21. 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.

22. 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 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

23. 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 Analytics Scope - Illustrated

24. 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)

25. 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

26. 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

27. 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)

28. Typical BI & DW projects risks • Project scope not defined well • Bad communication • No decisions & decision escalation processes • Lack of or little management support • Customer team availability • Incomplete or missing data sources

29. 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.

30. 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.

31. Fast Growing Retailer in India Case Study

32. Fast Growing Retailer in India Case Study

33. Goals of Loyalty Program • Increase Memberships • Increase Sale Value Per Member per Month • Increase Realization per Bill • Increase in Basket Size • Promote purchase of higher value items • Promote sales of Private Label products

34. Service Level Established

35. Contribution by Top 25 Cities/Stores to Membership

36. Which Cities/Stores are High Performing?

37. Which Cities/Stores Give High Value per Bill? Note High Sales Value Cities do not give High Value per Bill

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

39. Average Sales Amount Per Member (INR) Time Year Month Total % Change 2007-07(Jul) 335 2007-08(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% 7.92% Average Growth Average Sales Value per Member

40. Delhi: Average Sales Value per Member

41. Trend in Each Category

42. Males are More Likely to Buy Own Label

43. Growth in Sales by Division and Top Selling Sub Categories

44. Some examples of report generations

45. Thank You

46. Contact • Mr. Nilay Jhaveri Mobile:+91 - 9820036140 E-mail: nilay@sysconinfotech.com • Mr.Anish Pillai Mobile:+91 - 9820081957 E-Mail: anish@sysconinfotech.com