Empower Mobile Restaurant Operations Analytics

with Oracle Business Intelligence and Endeca

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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

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

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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Restaurant Management 101

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

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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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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Restaurant Science – Common Ground

Yelling?

…no, it’s the People!

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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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Advanced Analysis

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.

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

Mar

ket

Gro

wth

Rat

e

Low

Hig

h

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.

Using the Growth-Share Matrix

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Stars Question Marks

Cash Cows Dogs

Relative Market Share High Low

Mar

ket

Gro

wth

Rat

e

Low

Hig

h

invest divest

mature reinvest

liquidateleft unattended

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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Multi-Concept Approach

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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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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Solution Architecture

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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Data Integration

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Core and Optional Oracle Modules

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The Data Warehouse

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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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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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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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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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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Core Report Examples

Market Basket & Sales by Weekday

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Market Basket Prompts

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Market Basket Refined

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Product Mix

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Labor Productivity

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Why Act Now?

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.

6/23/2014

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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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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Next Steps

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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