Predicting Enrollment in Health & Wellness Programs
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Transcript of Predicting Enrollment in Health & Wellness Programs
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shapeup.com Copyright 2015 ShapeUp Inc.All Rights Reserved. Proprietary & Confidential
Samir A. BatlaDirector, BI & Data AnalyticsJohanna KincaidData Scientist
PREDICTING ENROLLMENT IN HEALTH & WELLNESS PROGRAMS
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OUR MISSION UNITE EMPLOYEES TO MAKE WORKPLACES HEALTHIER, HAPPIER, AND MORE SUCCESSFUL
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OUR HISTORY
Drs. Rajiv Kumar and Brad Weinberg founded ShapeUp in 2006 while studying at Brown Medical School
Launched initially as a non-profit community challenge called Shape Up RI, their social wellness idea went viral and reached over 10% of the adult population in Rhode Island
Six outcome-based studies and 600 customers later, ShapeUp now serves 10 Fortune 50 and more than twenty Fortune 500 companies worldwide
ShapeUp operates in 138 countries and is translated into 25 languages, reaching 1.6 million participants globally
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WHAT IS PREDICTIVE ANALYTICSMany definitions, that include:
• Data Mining
• Machine Learning
• Advanced Analytics
• Artificial Intelligence
• Etc.
The practice of analyzing existing data to make predictions about the future.
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INSIGHTS FROM ANALYTICS• Predict enrollment, engagement,
activity
• Gain deeper population insight• identify trends• influencing attributes
• Drive strategic decision making• Address under-performing areas• Reward above average performance
• Score participants to assess their likelihood of meeting milestonesThe things we can learn from predictive analytics will allow us to design
better competitions and programs to meet our mission of making healthier, happier and more successful workplaces
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We have just embarked on our journey – we did an experiment to predict future competition enrollment.
We focused on three predictive attributes:
•Postal Codes•Divisions•Labor Codes (Unions, non-Unions)Why these three variables? Answer is simple – completeness. Next: were any missing attributes significant?
Trained model on previous enrollments to predict enrollment in 2015
PREDICTIVE ANALYTICS AT SHAPEUP
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TRAINING DATA
Labor Code
Division
Postal Code
2013 2014
100 XYZ 90210 1 0100 ABC 02903 1 1200 XYZ 60601 0 1300 123 94102 0 0100 321 90210 1 1
2015?????
Trai
ning
Dat
a
Variables Classifications
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PROCESS
Training Data2 yrs; ~ 350k
Classifications
New Data~ 200k
New Classification
Exploratory Data Analysis / Algorithm Development
Predictive Model
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ENROLLMENT BY LABOR CODECo
unts
Non-union
What’s happening here?
Labor Code
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ENROLLMENT BY LABOR CODE
Zero Enrollment
Non-union
Log(
Coun
t)
Labor Code
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ENROLLMENT BY POSTAL CODELo
g(Co
unt)
Postal Codes (first two)
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ENROLLMENT BY POSTAL CODE YOY
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ENROLLMENT BY DIVISIONLo
g(Co
unt)
Divisions
Zero Enrollment100% Enrollment
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MODEL PERFORMANCE – UNTIL THINGS CHANGEYears of Training Data Training Predicted Actual Error %
2
1
.5549871
.5354515
.6206801
.6510264
.6192993
.6192993
0.14%
3.17%
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BEWAREUnknowns unknowns.
But predictive analytics is not a dubious endeavor. It’s flawed when it’s overly-complex. What we don’t want to do is make guarantees; however, we can, in fact, use it to understand what’s happening, what might happen and drive decision making.
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IN CLOSING…Predictive Analytics is helping ShapeUp:
Help our customers understand their population and prepare for future programs and competitions
Better understand book-of-business customer populations to design and offer new products that aren’t obvious
Enrich the relationship between Account Managers and customers – become consultative
More questions are coming to light
Understand Data
Prepare Data
Model Data
Evaluate
Deploy
Monitor
Business Goal
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PARTICIPANT SENTIMENT
“It help shift a focus of care for our employees. Also, when our employees are healthier they function better at their jobs.”“I like that we do this as a company...working with others is always easier....Thank you!”“Company giving those employees, who wish to participate, an opportunity to improve their health. Thank You!”
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THANK YOU!shapeup.com