Make Impactful Real-Time Decisions with Capgemini’s Retail Apps

1. Make Impactful Real-Time Decisions with Capgemini’s Retail Apps May 15, 2013 – Orlando, FL

2. Introductions Mike Price Capgemini, North America Practice Leader, SAP Business Analytics and Technology e. michael.r.price@capgemini.com m. +1 (770) 601 6471 Erik de Veer Capgemini, Global Sector Consumer Products & Retail Global Leader, SAP Retail and Fashion Solutions e. erik.de.veer@capgemini.com m. +31 6 1502 7845 @erikdeveer

3. Agenda • Background and Introductions • Retail Business Challenges • What are Extreme Applications for Retail? • Examples: • Market Basket Analysis • Mark Down Management • Next Best Action, Targeted Promotions • Q&A

4. Retail Business Challenges

5. The challenge of combining customer demand, product availability, and margin…to remain competitive

6. Mega Trends drivingTransformation in the RetailIndustry… Increased Importance of Health and Wellbeing Increased Impact of Consumer Technology Adoption Increase in Consumer Service Demands Increased Urbanization Increasing Spread of Wealth Aging Population Impact of Next-Generation Information Technologies Growing Consumer Concern about Sustainability Shifting of Economic Power Scarcity of Natural Resources Increase in Regulatory Pressure Rapid Adoption of Supply Chain Technology Capabilities

7. New customer behaviors require real-time insight New heartbeat in category management and merchandising Customers are ‘competitor savvy’ and other offers are just a click away Personalized offers require dynamic promotions Retail is always on, 24/7 Social and mobile commerce are the new norm Maintaining margin and profitability Omni-channel requirements and capabilities (i.e. click/collect) Digital Data growth and the combination of structured/unstructured data BYOD Marketing paves the way for IT

8. What are Extreme Applications for Retail?

9. General product overview Market Basket Analysis Next Best Action Markdown Management • Extreme Applications for Retail • Powered by SAP HANA • Real Time Sales Analysis / Extreme POS Analysis

10. How it all comes together: Extreme Applications module interactions Articles in current basket/ loyalty ID of customer Mr. X Articles Margin Time POS MARKET BASKET ANALYSIS Article affinity analysis Buying patterns Nextbest promotion for customer Mr X Promotion rules EXTREME POS NEXT BEST ACTION Articles Prices Inventory Tim/Season POS Current and past sales trends MARKDOWN MGMT Next Best Article for customer Mr X Forecasted stock end of season Improve sales, profitability, and customer loyalty Optimize combined sales and promotions to increase profitability Improve markdown strategy to optimize stock and profitability IN REAL TIME

11. High-level solution architecture Users PC, Mobile, Tablet Usage Retail Xtreme Apps SAP BI4 BI, Reporting HTML5 Integration to websites BI HANA Database Database & Analytics R on HANA Statistics SLT SAP Replication Data Services Non-SAP data load Loading SAP systems non-SAP systems Social Media Sources Addresses both SAP and non SAP data sources No constraints with SAP legacy systems: technical neutrality

12. Extreme Retail Applications - Examples

13. Market Basket Analysis Do you want to grow basket size and overall profitability by identifying products that drive drag-along sales? … intoactionableresults … turn Terabytes of POS data … How to … As a merchandiser, I want to gain insight into which products are often purchased together and with which combined margin to help stores with up-selling and cross-selling Cross-selling and up-selling by placing products with high affinity together Optimized promotion management • Affinity analysis based on POS data provides insight in which products are often sold together • Margin and sales analysis support optimized promotion planning for associated products

14. Example of Market Basket Analysis 437 USA 2013 01 West 01 Belts 05

15. Markdown Management Do you want to ensure your markdown strategies are meeting your financial goals? … turn Terabytes of POS data … … intoactionableresults How to … Markdowns are planned more efficiently and more profitable As a merchandiser I want to gain insight in which articles will have surplus stock at the end of the season given rates of sale at different prices • Store inventory data and forecasting algorithms predict surplus quantities at season’s end • Historical sales data provides insight into previous markdown effects • Price elasticity information and margin analysis supports markdown planning

16. Markdown Management Example West SS13 USA Belts USD USD USD USD

17. Next Best Action,Customer-targeted promotion How to make sure you increase your customers’ loyalty so that they continue to buy in your stores rather than at your competitors’? … turn Terabytes of POS data … … intoactionableresults How to… Customer loyalty number is entered in one of the customer contact channels, e.g. smartphone app, webchannel, store clerk The customer gets a promotion that fits his or her needs • Based on buying patterns promotion rules are created. • Based on customer loyalty information, a buying pattern is checked against promotion rules to see if there are suitable promotions available

18. Make Impactful Real-Time Decisions… • To Better Engage with Technology-Enabled Consumers • - The Consumer in the Driver’s Seat • To Help Optimize a Shared Supply Chain • - Collaborate Differently, Compete Differently

19. Q&A

20. Thank you! Mike Price Capgemini, North America Practice Leader, SAP Business Analytics and Technology e. michael.r.price@capgemini.com m. +1 (770) 601 6471 Erik de Veer Capgemini, Global Sector Consumer Products & Retail Global Leader, SAP Retail and Fashion Solutions e. erik.de.veer@capgemini.com m. +31 6 1502 7845 @erikdeveer