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ACHIEVING BIG DATA MATURITY
THINKING LIKE A DATA SCIENTIST
BILL SCHMARZO, CTO, EMC GLOBAL SERVICES
FOLLOW ME ON TWITTER: @SCHMARZO
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ROADMAP INFORMATION DISCLAIMER
• EMC makes no representation and undertakes no obligations with
regard to product planning information, anticipated product
characteristics, performance specifications, or anticipated release
dates (collectively, “Roadmap Information”).
• Roadmap Information is provided by EMC as an accommodation to the
recipient solely for purposes of discussion and without intending to be
bound thereby.
• Roadmap information is EMC Restricted Confidential and is provided
under the terms, conditions and restrictions defined in the EMC NonDisclosure Agreement in place with your organization.
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BIG DATA BUSINESS MODEL
MATURITY INDEX
Measures the degree to which the
organization has integrated data and
analytics into their business models
Business
Metamorphosis
Data
Monetization
Business
Optimization
Business
Monitoring
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Business
Insights
4
THE BIG DATA
MBA
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WHAT’S IMPORTANT TO CHIPOTLE?
Chipotle Business Strategy
• Continue to build a people culture that
attracts and empowers top performers
• Continue to grow revenues (up 20.3% in
2012) by opening new stores (opened 183
in 2012)…
• …and increase comparable restaurant
sales growth (7.1% in 2012)
• Marketing focused on building the Chipotle
brand and engaging with our customers in
ways that create stronger, deeper bonds
Chipotle 2012 Annual Report
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Chipotle 2012 Business Strategy
• Initiative #1: Continue to grow revenues (up 20.3% in 2012) by opening new stores (opened 183 in 2012)
• Initiative #2: Increase comparable restaurant sales (by 7.1% in 2012)
• Initiative #3: Marketing building Chipotle brand and engaging customers to create stronger, deeper bonds
Key Entities & Critical Success Factors
Entity 1:
Entity 2
Financial
Drivers
• Expand in-store customer data collection at POS (leverage credit card information)
• Capture, validate, score and integrate local events data
• i
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Chipotle 2012 Business Strategy
• Initiative #1: Continue to grow revenues (up 20.3% in 2012) by opening new stores (opened 183 in 2012)
• Initiative #2: Increase comparable restaurant sales (by 7.1% in 2012)
• Initiative #3: Marketing building Chipotle brand and engaging customers to create stronger, deeper bonds
Key Entities & Critical Success Factors
Entity 1: Develop individual store profiles including
traffic patterns, customer demographics and
product preferences by time of day/day of week
Entity 2: Leverage local sporting and entertainment
events to drive store traffic at slow stores and
during slow times
Financial
Drivers
• Expand in-store customer data collection at POS (leverage credit card information)
• Capture, validate, score and integrate local events data
• Integrate local events insights into local marketing and email promotional communications
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Chipotle 2012 Business Strategy
• Initiative #1: Continue to grow revenues (up 20.3% in 2012) by opening new stores (opened 183 in 2012)
• Initiative #2: Increase comparable restaurant sales (by 7.1% in 2012)
• Initiative #3: Marketing building Chipotle brand and engaging customers to create stronger, deeper bonds
Key Entities & Critical Success Factors
Entity 1: Develop individual store profiles including
traffic patterns, customer demographics and
product preferences by time of day/day of week
Entity 2: Leverage local sporting and entertainment
events to drive store traffic at slow stores and
during slow times
• Expand in-store customer data collection at POS (leverage credit card information)
• Capture, validate, score and integrate local events data
• Integrate local events insights into local marketing and email promotional communications
Financial
Drivers
Increase store traffic (acquire new customers, frequency of repeat customers)
Increase shopping bag revenue and margins (cross-sell complementary products, up-sell)
Increase number of corporate events (catering, repeat catering events)
Improve promotional effectiveness (Halloween Boo-ritto, Christmas gift cards, graduation gift cards)
Improve new product introduction effectiveness (seasonal, holiday)
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POTENTIAL CHIPOTLE DATA SOURCES
• Point of Sales Transactions
• Traffic Patterns
• Market Baskets
• Yelp
• Product Master
• Zillow
• Store Demographics
• Twitter / Facebook / Instagram
• Competitive Stores Sales
• Twellow /Twellowhood
• Store Manager Notes
• Zip Code Demographics
• Employee Demographics
• EventBrite
• Store Manager Demographics
• MaxPreps
• Consumer Comments
• Mobile App
• Weather
• …
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DATA VALUE ASSESSMENT
Increase
Store Traffic
Increase
Shopping
Bag Revenue
Increase #
Corporate
Events
Increase
Promotional
Effectiveness
Improve NPI
Effectiveness
Point of Sales Transactions
4
4
4
4
4
Market Baskets
4
4
2
4
4
Store Demographics (Zip Code)
3
3
1
3
3
Local Competitive Stores
2
2
2
2
2
Store Manager Demographics
1
1
3
1
1
Consumer Comments
3
3
3
3
2
Social Media
2
1
1
3
3
Weather
3
1
1
1
1
Local Events
4
2
1
2
1
Traffic
3
1
1
2
1
Zillow
1
2
2
2
2
Data Source
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IMPLEMENTATION ASSESSMENT
Ease of
Acquiring
Cleanliness
Accuracy
Granularity
Cost
Point of Sales Transactions
4
4
4
4
4
Market Baskets
4
4
4
4
4
Store Demographics (Zip Codes)
4
4
4
4
4
Competitive Stores Sales
2
2
2
2
1
Store Manager Demographics
4
4
4
4
4
Consumer Comments
2
2
1
2
3
Social Media
1
1
1
1
1
Weather
3
3
2
2
2
Local Events
1
2
2
2
1
Traffic
2
2
1
3
2
Zillow
1
1
2
2
1
Data Source
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PRIORITIZATION MATRIX
Increase Same Store Sales
Use Cases
Hi
Business Value
F
A.
A Increase Store Traffic
A
E
B Increase Shopping Basket Size
B.
(Up-sell, Cross-sell)
C
B
D
C.
C Increase Corporate Events
D Improve New Product
D.
Introductions
E Improve Promotional Effectiveness
E.
F Increase Special Events (birthdays,
F.
parties)
Lo
Implementation Feasibility
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Hi
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THINKING LIKE A
DATA SCIENTIST
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THINKING LIKE A DATA SCIENTIST
Thinking Like a Data Scientist involves:
• Identifying the right questions to ask, decisions to make or hypotheses to test
• Brainstorming different data sources and metrics that might yield more
predictive results
• Grouping insights into actionable “scores” that can be used to support
decision-making and process optimization
Thinking Like A Data Scientist process:
•
•
•
•
•
Step
Step
Step
Step
Step
1:
2:
3:
4:
5:
Identify organization’s key business initiatives
Identify “Strategic Nouns” and brainstorm questions
Leverage “By Analysis” to uncover variables and metrics
Group variables into potential Scores
Close the loop
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WHAT IS KEY BUSINESS INITIATIVE?
A Key Business Initiative is:
•
•
•
•
•
•
Documented (communicated either internally or publicly)
Cross-functional (involves more than one business function)
Owned/championed by a senior business executive
Has a measurable financial goal
Has a well-defined delivery timeframe (9 to 12 months)
Undertaken to delivery significant, compelling and/or distinguishable financial
or competitive advantage
Wireless provider example:
• Reduce attrition rate amongst our most profitable customers by 20% over the
next 12 months
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STEP 1: WHAT’S IMPORTANT TO CHIPOTLE?
Chipotle Business Strategy
• Continue to build a people culture that
attracts and empowers top performers
• Continue to grow revenues (up 20.3% in
2012) by opening new stores (opened 183
in 2012)…
• …and increase comparable restaurant
sales growth (7.1% in 2012)
• Marketing focused on building the Chipotle
brand and engaging with our customers in
ways that create stronger, deeper bonds
Chipotle 2012 Annual Report
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STEP 2: CHIPOTLE STRATEGIC NOUNS
• Strategic Nouns are business entities that are impacted by or
that impact the key business initiative
• For the Chipotle “Increase Same Store Sales” business initiative,
strategic nouns could include:
–
–
–
–
Stores
Events
Customers
Products
• Brainstorm the “strategic noun” questions from the perspectives of:
– Descriptive Analytics: Understanding what happened
– Predictive Analytics: Predicting what is likely to happen
– Prescriptive Analytics: Recommending what to do next
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CHIPOTLE “STORE” STRATEGIC NOUN
INCREASE SAME STORE SALES: “STORE” QUESTIONS
Descriptive Analytics
•
•
•
•
What
What
What
What
are most popular products/product combinations sold at Store X?
product and basket are sold at Store X during/after nearby football game?
are the demographics of the customers living near Store X?
businesses, schools and shopping malls reside near Store X?
Predictive Analytics
• How many customers will visit Store X during Farmer’s Market down the street?
• How many customers will visit Store X during Saturday’s college football game?
• How many customers will visit Store X for the Boo-ritto promotion?
Prescriptive Analytics
• What promotions should Store X run to bring in more college students?
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STEP 3: “BY” ANALYSIS TECHNIQUE
“By” Analysis technique leverages a business stakeholders
natural question and query process to uncover:
• Additional data sources and metrics
• Additional dimensional characteristics
• Additional areas of analytics exploration
“By” Analysis examples:
• “I want to see sales and product margin by product category, store, store
remodel date, day of week, store demographics, and customer demographics”
• “I want to trend hospital admissions by disease category, zip code, patient
demographics, hospital size, area demographics and day of week“
• “I want to compare current versus previous maintenance issues by turbine,
turbine manufacturer, maintenance person, date last serviced and weather
conditions”
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CHIPOTLE STORE “BY” ANALYSIS
Improve Merchandising Effectiveness: STORE Questions
What are most popular products sold at Stores by…
• Store location
• Day of week (weekends)
• Store size
• Holidays
• Store open date
• Seasonality
• Store last remodel date
• Weather conditions
• Local demographics
• Traffic patterns
• Local house values
• Miles from high school
• Products sold
• Miles from mall
• Product add-on items
• Miles from business park
• Product configuration
• Local sporting events
• Number of items in bag
• Local entertainment events
• Time of day
• …
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ROLE OF PREDICTIVE “SCORES”
• “Score” is a dynamic/changing rating or grade (e.g., IQ score) normalized to aid
in comparisons, performance tracking and decision-making; predicts likelihood
of certain actions or outcomes
• Scores support: What decision are you trying to make? What actions or
outcomes are trying to predict?
Financial
Credit Cards
Manufacturing
Casino
• FICO
• Retirement Readiness
• Investment Risk
• Attrition
• Fraud
• Product Category Preferences
• Equipment Maintenance
• Supplier Reliability
• Supplier Quality
• Customer LTV
• Lifestage
• Gaming Preferences
Education
Healthcare
Utilities
Pro Sports
• Graduation Readiness
• Cohorts
• Wellness
• Stress
• Energy Efficiency
• Conservation
Effectiveness
• Fatigue Factor
• Motivation Factor
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FICO EXAMPLE
Business Intelligence
Predictive Analytics
• What are your credit card balances?
• FICO Score is used by lenders to
predict your ability to repay a loan
including:
• What is your credit card payment
history?
• How many car loans do you have?
• What is your home mortgage
payment?
• What are your student loan
payments?
• What is your checking balance?
• Your credit worthiness in applying
for credit or a loan
• The interest rate
and loan terms
that you receive
for a home
mortgage or
car loan
• What is your savings balance?
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FOOT LOCKER CUSTOMER SCORES
Improve Merchandising Effectiveness: Customer Questions
What customers are most receptive to what types of merchandising campaigns by …
• Age
• Favorite sports
• Gender
• Favorite teams
• Marital Status
• High school sports
• Number of children
• College sports
• Length of marriage
• Weekend sports
• Income level
• Education level
• Loyalty card member
• Own or rent residence
• Tenure in current home
• Value of current home
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Sports Team
Passion
• Active athlete?
• Exercise minutes/week
• Types of exercise
Athletic
Activity
• Level of athletic effort
• …
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FOOT LOCKER “CUSTOMER” STRATEGIC NOUN
IMPROVE MERCHANDISING EFFECTIVENESS: “CUSTOMER” QUESTIONS
Descriptive Analytics
• What customers are most receptive to what types of merchandising campaigns?
• What are the characteristics of customers (e.g., age, gender, customer tenure, life stage,
favorite sports) who are most responsive to merchandising offers?
• Are there certain times of year where certain customers are more responsive?
Predictive Analytics
• Which customers are most likely to respond to a Back to School event?
• Which customers are most likely to respond to a BOGOF offer?
• Which customers are most likely to respond to a 50% off in-store markdown?
Prescriptive Analytics
• What personalized offers (recommendations) should I deliver to Anne Smith to get her to
come into the store?
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