discovering new consumer insights using partition … · using partition analysis using jmp...
TRANSCRIPT
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DISCOVERING NEW CONSUMER INSIGHTS
USING PARTITION ANALYSIS
Using JMP Partitioning as a treasure map of your data
Diane Navin, Mike Creed, Amy Phillips, Jen Schutte 9/21/16
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AGENDA
About P&G
What is JMP Partition?
When to use Partitioning
How to run JMP Partition
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2015 Company Overview
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Our Purpose We will provide branded products and services of superior quality and value that improve the lives of the world’s consumers, now and for generations to come.
As a result, consumers will reward us with leadership sales, profit and value creation, allowing our people, our shareholders, and the communities in which we live and work to prosper.
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About P&G … Founded in 1837
William Procter James Gamble
1879 - Introduced Ivory Soap
…Started with STAR
Candles P&G is the 4th oldest
Entity of the Fortune 50
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Our Values
P&G is its people and the values by which we live.
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Countries of Operations ~70
Countries Where Our
Brands Are Sold 180+
P&G At A Glance
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2015 Net Sales BY BUSINESS SEGMENT
Beauty
Grooming
Health Care
Fabric Care and Home Care
Baby, Feminine and Family Care
18%
11%
10%
32%
29%
*Results exclude net sales in Corporate. Results for the Beauty segment exclude sales for several Beauty categories P&G plans to exit, as the Company announced on July
9, 2015. P&G referred readers to the informational 8-K furnished on September 8, 2015 and the revised Form 10-K for FY15 furnished on October 26, 2015, which provide more details of the impacts to its financial results due to this change.
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2015 Net Sales BY GEOGRAPHIC REGION
North America
Europe
Asia Pacific
Latin America
IMEA
Greater China
41%
10%
24%
8%
8%
9%
Results exclude sales for several Beauty categories P&G plans to exit, as the Company announced on July 9, 2015. P&G referred readers to the informational 8-K furnished
on September 8, 2015 and the revised Form 10-K for FY15 furnished on October 26, 2015, which provide more details of the impacts to its financial results due to this change.
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A Company of Leading Brands
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Innovation – The Lifeblood of Our Business • P&G invents brands and products that create and
transform categories.
• We’re rededicating ourselves to product innovation that “wins from the top” – offering:
– the best-performing products in the category,
– with the highest quality,
– at a modest price premium,
– yielding superior consumer value.
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JMP AT P&G:
BUILDING A MODELING CULTURE
Easy to Use for the non-Expert
Easy to Understand and Communicate
JMP Categorical
Power of JMP
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WHAT IS JMP PARTITION?
A version of classification and regression tree analysis
A treasure map that shows you the best places in your data to hunt first for treasure! It helps you find key relationships like:
Which variables, or combination of variables, best differentiate
likers vs dislikers Which demographics describe them?
Which performance variables did they answer
most differently from each other?
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HOW DOES JMP PARTITION WORK?
It is like running many breakouts at once and identifying the variable that results in the largest swing (high….low) in the dependent variable
That variable becomes the first node in the tree
This is repeated for each branch finding the next variable and split that will result in the largest impact on each subsequent node
It can handle variables of different types (nominal, ordinal, or continuous) and it can handle data on different scales (agreement scales, demographics, segmentation, dipolar, yes/no, etc)
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WHEN TO USE JMP PARTITION ?
• Interactive Data Mining
• Find new insight in mined data – What pops!
• Early in data analysis to identify key areas to dig deeper
• Understand impact of habits / practices / attitudes / verbatims / attribute evaluation
• Find combinations of variables that predict another variable
• Quickly testing ideas
• It handles large problems easily
• The results are very interpretable
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PARTITIONING EXAMPLE
What are different variables we could measure to help us differentiate men from women? (in a work-appropriate way)
Height?
Hair length?
Facial hair?
Pierced ears?
Shoe size?
Wearing heels?
But which of our variables, or combinations of variables, are the BEST at differentiating men from women?
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PARTITIONING EXAMPLE
We have created a model of gender.
The model says that:
Women tend to …
Men tend to…
Gender
Long Hair
No Facial Hair
Facial Hair
Short Hair
No Facial Hair
Wearing heels
Not Wearing Heels
Facial Hair
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USE JMP PARTITION
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POKÉMON GO
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OVERNIGHT SUCCESS!
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BUT, THERE WERE CONERNS…
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HYPOTHETICAL RESEARCH QUESTION:
Should I advertise Tide on Pokémon Go?
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FIND THE SURVEY DATA
“Privacy and Information Sharing” Jan/Feb 2015
Publically Available: Pew Research Center
• 87 Questions
• 461 People
• Attitudes & Demographics
The Pew Research Center bears no responsibility for the interpretations presented
or conclusions reached based on analysis of the data.*
Note: Survey data was transformed to Triple-S prior to import to JMP.
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“PRIVACY AND INFORMATION SHARING”
9 Attitudes about Privacy
7 Scenerios: Grocery Store Free Loyalty Card
Health Information Website
Social Media for High School Reunion
Gaming App Insurance Company Device in Car
Workplace Cameras
Thermostat Sensor
Demographics
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JMP PARTITIONING: HOW TO
Before you begin
Clean and organize
your data
Left Click
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JMP PARTITIONING: HOW TO
Before you begin
Clean and organize
your data
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JMP PARTITIONING: HOW TO
Before you begin
Clean and organize your data
Select: Rows Delete Rows
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JMP PARTITIONING: HOW TO
Before you begin
Clean and organize your data
Make sure variables are categorized correctly
(nominal, ordinal, or continuous)
Make sure your dependent variable is coded
according to the levels you want it to split on.
Right Click
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Select: Analyze Modeling Partition
SELECT PARTITION
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SELECT VARIABLES AND OPTIONS
Select your Dependent variable (the variable you want to model) and click “Y, Response” button
Select the Independent variables you want your Partition to use and click the “X, Factor” button
Uncheck Informative missing to prevent JMP from treating missing as a separate category
Check Ordinal Restricts Order to maintain the ordinality of the responses when determining splits
Select Decision Tree as Method
Click OK button to run
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Select Color Points button to color
code your data
SELECT COLOR POINTS
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SELECT COLOR POINTS
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SELECT VALUE COLORS
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VALUE COLORS = STOPLIGHT
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SELECT: ANALYZE MODELING PARTITION
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Select the red triangle for the Partition
window to set Display Options.
Check Show Split Counts
Now the data we are most interested in will
show up inside each node
SET DISPLAY OPTIONS
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HOW TO READ THE PARTITION
• This Count shows the total number of panelists in this branch of the tree
• The color of the bars are the same color coding as the panelists in the partition.
• The size of the bars represents the fraction of that
type of panelist in that branch
• Rate shows the percentage of panelists in each level of that branch
• This Count shows the number of individual panelists in each level in that branch
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HOW TO GROW THE TREE
• To grow the tree one level
click “Split”
• To reduce the tree one level
click “Prune”
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GROWING THE TREE
JMP looks at all your
independent variables and
determines which one
variable is best at
differentiating between
people who said Yes or No.
In this case that variable is
Not having someone watch or
listen to you...
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GROWING THE TREE
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GROWING THE TREE: CANDIDATES
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GROWING THE TREE: SPLIT SPECIFIC
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LET’S LOOK AT DEMOGRAPHICS
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DEMOGRAPHICS TREE
Who is the group who finds the Gaming App Scenerio acceptable?
• Younger
• Not Head of Household
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RECAP WHEN TO USE JMP PARTITION
• Interactive Data Mining
• Find new insight in mined data – What pops!
• Early in data analysis to identify key areas to dig deeper
• Understand impact of habits / practices / attitudes / verbatims / attribute evaluation
• Find combinations of variables that predict another variable
• Quickly testing ideas
• Who are Likers / Dislikers?
• Which performance variable was most correlated to liking
• Are there trends in demographics/segments
• Trouble shooting problems
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KEY TAKEAWAYS ON JMP PARTITION
JMP Partition helps you find key relationships in your data that are a good
place to start your exploration
It builds a classification tree model that helps you understand your data better
It’s especially good when you need to explore the relative impact of variables
that are different types or variables on different scales
Now you have another tool in your toolbox!
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QUESTIONS?