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

2. Introductions Mike Price Erik de Veer Capgemini, North America
Practice Leader, SAP Business Analytics and Technology e.
m. +1 (770)
Erik de Veer
Capgemini, Global Sector Consumer Products & Retail
Global Leader, SAP Retail and Fashion Solutions e. m
@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 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

7. 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

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
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

11. 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

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?
… 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

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

15. 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

16. Markdown Management Example
USD
USA
West
Belts
SS13

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 …
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

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 Erik de Veer Capgemini, North America
Practice Leader, SAP Business Analytics and Technology e.
m. +1 (770)
Erik de Veer
Capgemini, Global Sector Consumer Products & Retail
Global Leader, SAP Retail and Fashion Solutions e. m
@erikdeveer