The document discusses how Perficient aids mobile restaurant operations through Oracle Business Intelligence and analytics, emphasizing metrics like food costs and labor productivity. It outlines strategies for restaurant management, advanced data analysis techniques, and the importance of understanding guest experiences to drive profitability. The document also highlights current industry practices, such as Darden's use of analytics to enhance customer understanding and performance metrics.

1. Empower Mobile Restaurant Operations Analytics
with Oracle Business Intelligence and Endeca
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2. Perficient is a leading information technology consulting firm serving clients throughout
North America.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.
About Perficient

3. About Patrick Abram
Photo
Passion: Finding not only the right answers,
but also which are the right questions.
Skills: Planning and delivery of Oracle CRM
and Oracle Business Intelligence solutions.
Oracle University instructor.
Education: Bachelors of Science in
Mathematics from Purdue University
Personal notes:
• Black belt in Tae Kwon Do
• Favorite Quote: “Truth suffers from
too much analysis.” – Frank Herbert

4. Agenda
1. Restaurant Management 101
2. Advanced Analysis
3. Multi-Concept Approach
4. Solution Architecture
5. The Data Warehouse
6. Core Report Examples
7. Why Act Now?
8. Next Steps
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5. Restaurant Management 101

6. Restaurant Science – Two Points of View
Kitchen Management
Food Quality & Costs
Gordon Ramsay
Kitchen Nightmares
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House Management
Process & Profitability
Jon Taffer
Bar Rescue

7. Kitchen Metrics
Food Quality
Food Costs
Popularity
• # of times an item is ordered
Remake %
• # of times an item is remade / Popularity
Comp %
• # of times an item is “comped” / Popularity
Total Food Cost
• Sum of all food costs
Food Cost %
• Total Food Cost / Total Food Revenue
Food Cost per Head
• Total Food Cost / # of customers
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8. House Metrics
Process
Profitability
Covers per Man Hour
• # of times a table is seated / total hours
Labor Cost per Cover
• Labor cost / # of times a table is seated
Upsell $ per Man Hour
• Upsell (after initial order) $ / server hours
Gross Profit
• Total Sales Revenue – Food & Bev Cost
Available Seat Hour
• # of seats * length of service (in hours)
Revenue per Available Seat Hour
• Total Sales Revenue / Available Seat Hours
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9. Restaurant Science – Common Ground
Yelling?
…no, it’s the People!
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10. Staff & Labor Metrics
Staff & Labor Wage Cost %
• Wage $ / Total Sales Revenue
Labor Hours
• # of hours worked
Turnover
• # of people employed / # of positions
Average Length of Employment
• # of days worked / # of positions
Average Hourly Pay
• Wage $ / # of hours worked
Each of the metrics above should be role-typed
• Kitchen
• House
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11. Advanced Analysis

12. Affinity Analysis – Product Basket
Data analysis technique that identifies co-occurrence relationships
between items sold on the same transaction.
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Identifying popular item
combinations empowers
greater cross-selling &
upselling.
Identifying “mood setting”
items empowers staff to
make recommendations that
increase ticket totals.

13. Growth-Share Matrix
Invented in 1970 by Bruce D. Henderson for Boston Consulting Group
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Stars Question Marks
Cash Cows Dogs
Relative Market Share
 High Low 
MarketGrowthRate
LowHigh
Scatter graph ranking items on
the basis of their relative
market shares and growth
rates.
• Stars: Menu items generating strong
sales which cost a lot to produce.
• Question Marks: Menu items gaining
popularity which require tweaking or
investment to make profitable
• Cash Cows: Menu items that are easy
to make, low-cost, and are responsible
for a larger share of profits.
• Dogs: Menu items that don’t sell well,
but are also low cost.

14. Using the Growth-Share Matrix
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Stars Question Marks
Cash Cows Dogs
Relative Market Share
 High Low 
MarketGrowthRate
LowHigh
invest divest
mature reinvest
liquidateleft
unattended

15. Putting Data to Work: Analytic Pathways
Scenario
Pricing increase on selected
items amounting to a weighted
average 2.44% based on the
current mix.
• The increase of 2.44% in Price is offset
by a decline of 1.56% in Product Mix.
• This results in an increase of 0.88% in
the Average Ticket.
• A decline of 0.33% in Traffic further
reduces Same Store Sales to a net
increase of 0.55%.
• Having the relationships of leading
indicators defined and the information
visible enables proactive management.
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16. Multi-Concept Approach

17. Data Across Concepts
Apples to Apples
Apples to Oranges
Strong cross-concept correlation
Food Cost %
• Total Food Cost / Total Food Revenue
Covers per Man Hour
• # of times a table is seated / total hours
Wage Cost %
• Wage $ / Total Sales Revenue
Weak or no cross-concept correlation
Food Cost per Head
• Total Food Cost / # of customers
Labor Cost per Cover
• Labor cost / # of times a table is seated
Average Hourly Pay
• Wage $ / # of hours worked
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18. Identifying Correlations
A tale of two restaurants
side-by-side in the suburbs
For one, it was the best of times.
For one, it was the worst of times.
…but why?
What do they have in common?
• Location & demographics
• Age of building and equipment
• Technology infrastructure
• Marketing support
What are the differences?
• Management team
• Product mix
• Labor turnover
What is beneath the numbers?
• Food waste from poor FIFO rotation?
• Tip % outliers from “comp” fraud?
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19. Solution Architecture

20. Accelerator vs. Application
is a project accelerator, not a pre-built application
Why can’t retail analytics be pre-packaged?
• Multiple disparate data sources that vary by client
• Multiple reporting and planning environments supported
• Multiple hardware environments supported
Challenges
• Data inconsistencies (e.g., sales data does not tie out to inventory data)
• Educating data consumers
• Drilling down to the correct level of detail
We do pre-package standard reporting components
• Framework and best practice collected from multiple implementations
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21. Data Integration
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22. Core and Optional Oracle Modules
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23. The Data Warehouse

24. Sales Detail Fact
Lowest granularity fact table
• Store
• Ticket
• Table
• Seat
• Menu Item
Dimensions are Concept-specific
• Revenue Center
• Order Type
• Discount
• Tender
• Void
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25. Ticket Fact
Next granularity level up
• Lose Menu Item
• Gain Customer
Customer dimension derived
from credit card transactions
• Full Name
• Last 4 of credit card number
Summarizes Tip, Tax, & Discount
• RP_TIP_F
• RP_TAX_F
• RP_DISCOUNT_F
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26. Labor Fact
Pay summary for all employees
• Kitchen
• House
• Bar
Not intended to replace reports
generated from the HR system
Vital to overall performance
metrics comparing daily revenue
to daily labor costs
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27. Purchase Order & Usage Facts
Summary of raw item purchases
• Need not actually be “raw”
• Can optionally link to GL
Standard order quantity can be
sourced from ERP or historical
Summary of raw item usage
• Assumes FIFO usage, but
this can be adjusted
• Can be used to record
spoilage
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28. Cost Facts
Ideal vs. Actual usage drives
metrics related to waste / scrap
• Waste can be identified early
• Unexplained waste can be a
sign of direct or indirect theft
Menu Item costs are vital
• Contribution is fundamental to
Growth-Share analysis
• Void quantity and amounts
provide management insights
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29. Core Report Examples

30. Market Basket & Sales by Weekday
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31. Market Basket Prompts
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32. Market Basket Refined
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33. Product Mix
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34. Labor Productivity
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35. Why Act Now?

36. Who Is Doing It Now?
Darden Restaurants, Inc. is the world’s largest full-service restaurant
operator with over 2,100 locations and 200,000 employees 1
1 http://www.darden.com/ and http://en.wikipedia.org/wiki/Darden_Restaurants
Darden Restaurant, Inc. example used for illustrative purposes only. No endorsement of or Perficient is implied.
All logos © and ® Darden Restaurants, Inc.
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37. Who Is Doing It Now?
On 04/02/2014 “Darden Uses Analytics to Understand Restaurant
Customers” was #5 on InformationWeek’s Elite 100: Winning Digital
Strategies. 1
CIO Patti Reilly White said, “We can capture when you enter the
restaurant and either get seated right away or quoted a wait
time. ... Now we can understand the total guest experience
within the four walls.”
When discussing future plans, CIO White added, “We're still on
a multiyear journey to understand our specific guests. We want
to be able to see that this guest has come in this many times to
this restaurant or this brand -- or to all eight of our brands. All of
our initiatives in the analytics space and the digital space are
aimed at how to capture and understand information about the
specific guest.”
1 http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/informationweek-elite-100-winning-digital-strategies/d/d-id/1127886
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38. Monitoring Reduces Fraud
Washington University in St. Louis published a study “IT monitoring
effective in deterring fraud by restaurant employees” on 09/13/2013. 1
Pierce and his team found that after installing the monitoring software,
revenue per restaurant increased an average of $2,982 per week,
about 7 percent. Restaurants also experienced a 22 percent drop in
theft.
“Our results suggest a counterintuitive and hopeful pattern in human
behavior,” the researchers write. “Employee theft is a remediable
problem at the individual employee level. While individual differences
in moral preferences may indeed exist, realigning incentives through
organizational design can have a powerful effect in reducing corrupt
behaviors in a way that benefits both the firm and its workers.”
1 http://www.sciencedaily.com/releases/2013/09/130903123050.htm
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39. Next Steps

40. Planned Development
Oracle BI Mobile App Designer
The use of Oracle BI Mobile App Designer eliminates the need for an App
Store download or additional software. The report pages created will run on
any HTML5-compliant device. This unlocks to a greater range of
restaurant operators regardless of mobile platform.
Leverage Upcoming Cloud Service Connectors for Oracle BI
• Oracle RightNow Social Monitor Cloud Service
– As it stands alone data from Oracle RightNow is highly valuable for
customer satisfaction data down to the store level.
– As a source for Oracle Endeca Information Discovery the data from Oracle
RightNow can aid the divest / invest and product mix selection decisions.
• Oracle Eloqua Marketing Cloud Service
– Marketing campaign effectiveness as well as actual lift and cannibalization
data can help drive decisions on trade spend decisions at all levels.
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