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

Introductions Mike Price Erik de Veer 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

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

Retail Business Challenges

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

Mega Trends driving Transformation in the Retail Industry… Increased Impact of Consumer Technology Adoption Increased Importance of Health and Wellbeing Increased Urbanization Increasing Spread of Wealth Increase in Consumer Service Demands 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

New customer behaviors require real-time insight Customers are ‘competitor savvy’ and other offers are just a click away New heartbeat in category management and merchandising 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

What are Extreme Applications for Retail?

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

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 Next best promotion for customer Mr X EXTREME POS Promotion rules 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 Optimize combined sales and promotions to increase profitability Improve markdown strategy to optimize stock and profitability Improve sales, profitability, and customer loyalty IN REAL TIME

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

Extreme Retail Applications - Examples

Market Basket Analysis Do you want to grow basket size and overall profitability by identifying products that drive drag-along sales? … turn Terabytes of POS data … … into actionable results 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

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

Historical sales data provides insight into previous markdown effects Markdown Management Do you want to ensure your markdown strategies are meeting your financial goals? … turn Terabytes of POS data … … into actionable results How to … 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 Markdowns are planned more efficiently and more profitable 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

Markdown Management Example USD USA West Belts SS13

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 … How to… … into actionable results 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

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

Q&A

Thank you! Mike Price Erik de Veer 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