A More Transparent Interpretation of Health Club Surveys (YMCA)

Copyright © 2004-2012, Abbott Analytics, Inc. and Seer Analytics, Inc. All rights reserved.

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Abbott - A More Transparent Interpretation of Health Club Surveys

Dean AbbottAbbott Analytics, Inc.

URL: http://www.abbottanalytics.comBlog: http://abbottanalytics.blogspot.comTwitter: @deanabb

Salford Analytics and Data Mining Conference 2012

San Diego, CAMay 24, 2012

http://www.abbottanalytics.com/



http://abbottanalytics.blogspot.com/


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About Seer Analytics

Seer Analytics, LLC– Founded in 2001, based in Tampa, FL– Produce actionable intelligence to help clients

make smarter decision and drive business performance.

– Reports embed sophisticated analytics yet are designed to be accessible and meaningful to a non-technical audience.

Bill Lazarus, Founder, President and CEO– BA from University of Wisconsin– MA from University of Toronto, – SM and PhD from Massachusetts Institute of

Technology.


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About Abbott Analytics

Abbott Analytics– Founded in 1999, based in San Diego, CA– Dedicated to data mining consulting and training

Principal: Dean Abbott– Applied Data Mining for 22+ years in

Direct Marketing, CRM, Survey Analysis, Tax Compliance, Fraud Detection, Predictive Toxicology, Biological Risk Assessment

– Course Instruction Public 1-, 2-, and 3-day Data Mining Courses Conference Tutorials and Workshops (next: ACM Data Mining

Bootcamp, November 13th in San Francisco)– Customized Training and Knowledge Transfer

Data mining methodology (CRISP-DM) Training services for software products, including CART,

Clementine, Affinium Model, Insightful Miner


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Talk Outline

Health Club Survey Analysis Problem Description– Overview of Survey Analysis and

Approaches Solution 1: traditional approach

– Statistical approach, fancy visualization Solution 2: solution aligning models to

business objectives Results and conclusions


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Problem Setup: Member Survey

Question:– What are the characteristics of members who

indicated the highest overall satisfaction with their Y?

Data:– 32,811 records containing survey answers

– No demographic data except what was on survey (marital status, children, age, gender)

Approach:– Create supervised learning models with target

variable “overall_satisfaction = 1”


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Some Notes

It is very unusual to have so many records– The 31K responses were for one year– Responses are collected from across the

country Seer tracks survey responses

longitudinally as well (not discussed in this talk)– Began collecting survey responses and

storing in a database in 2001 => 10 years of data

– Seer has moved beyond modeling satisfaction to include a more complete view of the YMCA member experience


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Data Preparation

Begin with 57 candidate inputs to model– All survey questions are multiple choice

Treated as categories, not numbers Typically 6 categories per question (1-5) Unknown initially coded as “0”

– No text comments fields included as inputs to model

Create new column for target variable– If overall_satisfaction = 1, variable value = 1,

otherwise, variable value = 0 Data very clean with respect to NULLs

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Member Survey Question Categories


Microsoft Excel Worksheet


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Sampling and Target Populations

Begin with 32,811 responses Set aside about half for validation (not used during

modeling): 16,379 records– These records will be used to provide final summaries of

the segments Q1 - Satisfaction = 1: 31%

– 86% have Recommend to friends = 1 Q48 - Recommend to Friend = 1: 54%

– 49% have Overall Satisfaction = 1– 26.0% have both overall satisfaction and recommend to

friends both equal to 1 Q32 - Likelihood to Renew = 1: 46% Implications

– All three are interesting, but Recommend is so high already, not much room for growth


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Objective and Data Challenges

Project Objective– Interpret results of survey for YMCA

Challenges– Missing data (some questions either N/A or blank)

Solution: Impute values that least effect information communicated by question (not a mean or median!)

– Question responses highly correlated with one another

Multi-collinearity and interpretation of results problematic

Must reduce dimensionality without losing interpretation of results

Solution: Factor analysis


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Objective and Data Challenges

Challenges, cont’d– Target variable

Three questions pointed to the important actionable information (related to how satisfied members were)

No one question fully characterized the value of a member

Solution: combine all three into a new “index of excellence” (IOE)

– IOE = additive weighted sum of Q1, Q32, Q48

– Reverse scale so higher IOE is better


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Why Factor Analysis?

“Traditional approach to survey analysis involves the use of frequency counts, t-test, correlation, and measures of central tendency. “

“Factor analysis is a variable-reduction statistical technique capable of probing underlying relationships in variables”– Santos, J.R.A., Clegg, M.D. (1999), "Factor

analysis adds new dimension to extension surveys", Journal of Extension, http://www.joe.org/joe/1999october/rb6.php

Our use of Factor Analysis– Traditional view: there is an underlying “truth” that

exist, and the survey is a redundant measure of that truth.

– Just a derived variable that reduces dimensionality

http://www.joe.org/joe/1999october/rb6.php


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Factor Analysis: Key Factors

Factor 1

0.00

0.20

0.40

0.60

0.80

1.00

Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Top Question Loadings

Lo

ad

ing

Va

lue

Factor 2

0.00

0.20

0.40

0.60

0.80

Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q23

Top Question Loadings

Lo

ad

ing

Va

lue

s


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Member Survey Factor Analysis Loadings

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Reduce Variables using Regression

Already beginning with only 13 variables

Question: how many of these are useful predictors?

Decided to retain 5 factors for final model

Regression Rankings of Questions/Factors

0

0.1

0.2

0.3

0.4

0.5

0.6

Q44 Q22 Q25

facto

r3.2

facto

r3.9

facto

r3.1

facto

r3.4

facto

r3.3

facto

r3.8

facto

r3.1

0

facto

r3.6

facto

r3.5

facto

r3.7

Question/Factor

Reg

ress

ion

Co

effi

cien

t



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Predictive Modeling Approach

Identify Key Questions

Factor Analysis: 10 factors

Regression Model: Find Significant

Variables

Regression Model: Find Significant

Variables

3 questions with high association with target

10 factors, or variables that loaded highest on each factor

13 fields down to 7

Variable ranks

50+

Sur

vey

Que

stio

ns

3 key questions


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One Further Note on Final Regression Models

Empirical comparison: Factors as inputs vs. Top-loading question in factor as input

– Top-loading or most interesting question on factor as representative of that factor produced slightly better models

– Use of top-loading question makes final model more easily understood

– This flies in the face of traditional theory, but worked better operationally

Final regression model contained these fields:

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Key: Explaining Results

Visualization shows key variables in survey associated with “excellence”, and performance metrics for each Y

– How well did this Y do?

– What is the change over last year’s result?

– This is a 45-dimensional visualization (don’t ask me to name them all!)

Shows which attributes does the Y need to improve to improve customer satisfaction.


relationships

facility

equipment

Staff 2Staff 1

goals

value

Drivers ofSatisfaction

Current Year vs. Last Year

Prior year


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Interpreting Index/Drivers of Excellence Analysis

All factors listed are important

Position on ‘x’ axis indicates relative importance

of factors in driving IOE

Above the line = “better than peers”; below the

line = “worse than peers”

Small dot indicates position last year

R-Y-G indicates magnitude of change from

previous year

Size of bubble indicates magnitude of score on

factor


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Peer Average

drivers of excellence

May 2003

Gardena YMCA

© 2003 Seer Analytics, LLCTampa, FL 33602

Same

Importance

Rela

tive P

erform

ance

Worse

Better

2002

2003 vs.2002Staff cares

Staff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome

Peer Average


May 2003

Gardena YMCA


Same

Importance

Rela

tive P

erform

ance

Worse

Better

2002


Staff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome


May 2003

Gardena YMCA


Same

Importance

Rela

tive P

erform

ance

Worse

Better

2002


Staff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome

Prior year


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Feel welcome

Meet fitness goals

Facilities

Value

Equipment

Staff competence

Staff caresdrivers of excellence

May 2003

Montebello YMCA


Same

Importance

Rel

ativ

e Per

form

ance

Worse

Better

2002

2003 vs.2002

Peer Average

Feel welcome

Meet fitness goals

Facilities

Value

Equipment

Staff competence

Staff caresdrivers of excellence

May 2003

Montebello YMCA


Same

Importance

Rel

ativ

e Per

form

ance

Worse

Better

2002

2003 vs.2002

Peer Average

Prior year


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May 2003

Torrance South YMCA


Same

Importance

Rel

ative

Per

form

ance

Worse

Better

2002

2003 vs.2002

Peer Average

Staff caresStaff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome

Prior year


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I love this visualization!

Staff cares

Staff competence

Equipment

Value Facilities

Meet fitness goals

Feel welcome


May 2003

Culver YMCA


Same

Importance

Rel

ativ

e Per

form

ance

Worse

Better

2002

2003 vs.2002

Peer Average

Staff cares

Staff competence

Equipment

Value Facilities

Meet fitness goals

Feel welcome


May 2003

Culver YMCA


Same

Importance

Rel

ativ

e Per

form

ance

Worse

Better

2002

2003 vs.2002

Peer Average

Prior year


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What’s the Problem with That?

Customer was not interested in “techno” solutions

Customer was interested in what actions could be taken as a result of the data mining models– Which characteristics are most correlated with

best customers? What do they like and dislike about the Y? Is it equipment? relationships? facility? staff?

– Show key contributors, how each Y compared with other Y locations, and if Y is improving


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So What’s The Problem with That? (cont’d)

Regression, Neural Networks are “global” estimators– The operate over the entire data space– Descriptors of Regression represent average

influence– Neither technique provides explicit localized

characteristics Customer would like actionable analytics

– Clear characteristics of subgroups – Different strategies for subgroups

Conclusion: In Round 2, use another approach


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Who cares about “satisfaction”?

Issue: The YMCA is a cause-driven charity It’s not about running “satisfactory” gyms It’s about improving lives and building communities

Question: How can the member survey data help Ys achieve mission goals?

Answer: Develop a tool that is: Grounded in solid social science Accessible/understandable Diagnostic/predictive A driver of performance and change


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Satisfaction Model Performance

0

20

40

60

80

100

0 20 40 60 80 100

% C

lass

% Population

0

20

40

60

80

100


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Satisfaction Model from CART®

Q31

Q34 $

Q36

Q19

Q13

Q22

Q13

Q31

Q6

Q36

Q31

Q2

Q13

Q25

1

2

3

9

10

15

8

• Q25: Feel Welcome– Surrogate: Q24 (can relate to other

members)– Q13: Facilities are clean

– Surrogate: Q14 (Facilities safe and secure)

• Q22: Value for Money– Surrogates: Q21 (convenient

schedule) and Q23 (quality classes/programs)

– Q6: Staff Competent– Surrogates: Q5 (friendly staff) and

Q7 (enough staff)


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Member Satisfaction Model: Key Rules

Terminal Node 1

• 10,014 surveys (20.8%),

• 7,289 highly satisfied (72.8%),

• 49% of all highly satisfied

RULE:If strongly agree that facilities are clean and strongly agree that member feels welcome, then highly satisfied

Terminal Node 2

• 4.578 surveys (9.5%),

• 2,317 highly satisfied (50.6%),

• 15.6% of all highly satisfied

RULE:If strongly agree that feel welcome and strongly agree Y is value for money, even if don’t strongly agree facilities are clean, then highly satisfied

Terminal Node 3 • 1,739 surveys

(3.6%), • 904 highly

satisfied (52.0%),• 6.1% of all highly

satisfied

RULE:If strongly agree that Y has the right equipment and strongly agree that feel welcome, and somewhat agree that facilities are clean, even though don’t strongly feel Y is good value for the money, then highly satisfied


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Member Satisfaction Model: Other Rules

Terminal Node 10

• 998 surveys (2.1%), • 431 highly satisfied

(43.2%),• 2.9% of all highly

satisfied

RULE: weakest of top 5If strongly agree that loyal to Y and strongly agree that facilities are clean, even though don’t strongly agree that feel welcome nor strongly agree that staff is competent, then highly satisfied

Terminal Node 9

• 1,739 surveys (3.6%), • 904 highly satisfied

(52.0%),• 6.1% of all highly

satisfied

RULEIf strongly agree that facilities are clean, and strongly agree that staff is competent, even if don’t strongly agree feel welcome, then highly satisfied


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Member Satisfaction Model: Unsatisfied Rules

Terminal Node 15

• 19,323 surveys (40.2%), • 1,231 highly satisfied

(6.4%),• 8.3% of highly satisfied• 58.2% of all not highly

satisfied

RULE:If don’t strongly agree that staff is efficient and don’t strongly agree that feel welcome, and don’t strongly agree that the facilities are clean, then member isn’t highly satisfied

Terminal Node 8 • 1,364 surveys (2.8%), • 141 highly satisfied

(10.3%),• 1.0% of all highly satisfied

RULEIf don’t strongly agree that facilities are clean and don’t strongly agree that the Y is good value for the money, even though strongly agree that feel welcome, member isn’t highly satisfied.

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Recommend to Friend Model from CART®

Q31: Loyal– Surrogates: Q25,

Q44, Q22, Q24 (can relate to other members)

Q25: Feel Welcome– Surrogates: Q24, Q5

(friendly staff) Q22: Value for

Money– Surrogates: Q23

(quality classes/programs)

Q44: Helps meet fitness goals


1

2 4

7

5

Q22

Q25

Q44

Q25

Q22

Q31


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Recommend to Friend Model: Key Rules

Terminal Node 1

• 13,678 surveys (28.5%),

• 12,122 recommend (88.6%),

• 47.0% of all strong recommends

RULE: If strongly agree that loyal to Y and strongly agree that feel welcome, then strongly agree that will recommend to friend

Terminal Node 2

• 6,637 surveys (13.8%),

• 4,744 recommend (71.5%),


RULE:If strongly agree that loyal to Y and agree that Y is a good value for the money, even though don’t strongly agree feel welcome, strongly agree will recommend to friend.

Terminal Node 4

• 2,628 surveys (5.5%),

• 1,932 recommend (73.5%),


RULEIf strongly agree that Y is a good value for the money and strongly agree that feel welcome, even though not strongly loyal to Y, strongly agree will recommend to friend


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Recommend to Friend Model:

Other Rules

Terminal Node 7

• 21,865 surveys (45.5%), • 5,461 highly recommend

(25.0%),• 21.2% of all highly

recommend

RULE:If don’t strongly agree that loyal to Y and don’t strongly agree that Y is value for the money, then will not highly recommend to a friend

Terminal Node 5

• 814 surveys (1.7%), • 509 highly recommend

(62.5%),• 2.0% of all highly

recommend

RULEIf strongly agree that Y is good value for the money, and strongly agree that Y helps meet fitness goals, even though not strongly loyal to the Y and don’t strongly feel welcome, will highly recommend to a friend


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Intend to Renew Model from CART®

Q47

Q22

Q44

Q27

Q22

Q44

Q25

• Q25: Feel Welcome– Surrogate: Q24 (can relate to other

members)– Q44: Helps meet fitness goals

– Surrogate: Q51 (visit frequency)• Q22: Value for Money

– Left split Surrogates: Q21 (convenient schedule) and Q23 (quality classes/programs)

– Right split surrogates: Q25 (feel welcome=2 or 3)

– Q47: Would be donor– Surrogate: Q45A (have been donor)

– Q27: Feel sense of belonging– Surrogates: Q25, Q24

1

2

3 5

8

7


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Intend to Renew Model: Key Rules

Terminal Node 1

• 13,397 surveys (27.9%),

• 9,903 renew (73.9%),

• 48.4% of all intend to renew

RULE:If strongly agree that feel welcome and strongly agree that Y helps meet fitness goals, then strongly agree that intend to renew

Terminal Node 2

• 3,051 surveys (6.3%),

• 1,823 renew (59.8%),


RULE:If strongly agree Y is good value for the money and strongly agree that feel welcome, even if don’t strongly agree that Y helps meet fitness goals, then strongly agree that intend to renew

Terminal Node 5

• 5,704 surveys (11.9%),

• 3,201 recommend (56.1%),


RULEIf strongly agree that feel sense of belonging, and agree that Y is value for the money, and strongly agree that Y helps meet fitness goals, even if don’t feel welcome, then strongly agree intend to renew.


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Intend to Renew Model: Other Rules

Terminal Node 8

18,547 surveys (38.6%), • 3,130 strongly intend to

renew (16.9%),• 15.3% of all strongly intend

to renew

RULE:If don’t strongly agree that feel welcome and don’t strongly agree that Y helps meet fitness goals, then don’t strongly agree that intend to renew

Terminal Node 7

2,178 surveys (4.5%), • 578 strongly intend to

renew (26.5%),• 2.8% of all strongly intend

to renew

RULEIf don’t strongly agree that Y is good value for money and don’t strongly agree that feel welcome, even if strongly agree Y helps meet fitness goals, don’t strongly agree that intend to renew.

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Summary of Key Questions in Models

Feel Welcome was root splitter (or surrogate) for each model

Satisfaction is different than Recommend and Renew in other respects– Helps meet fitness goals was in Recommend

and Renew models, but not satisfaction– Facilities clean only in satisfaction model


3


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Key Differences Between Targets, Put Another Way

Satisfaction– Feel Welcome– Clean Facility

Renewal– Feel Welcome– Y Helps Meet Fitness Goals, Value for $$

Recommend to Friend– Feel Welcome– Loyal to Y, Value for $$


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Top Terminal Nodes Comprise More than 70% of

Hits

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Subsequent Results

Rules from Models are still in use today Trees and Factors can help reduce #

questions in survey– Employee ruleset (using same methodology)

resulted in a new “short-form” survey using only questions in the splits

– Not yet implemented in Member survey

Measure 2002 2009Percent

Improvement

Satisfaction 31% 41% 32%

Recommend to Friend 54% 57% 6%


Index Construction and Scaling

Begin with Factor Analysis

Cluster attribute groupings to be managerially meaningful

Z-normalize the variables, cast all in units of variance

Run tests for deviation from Standard Normal by variable and

factor

Create z-index for each factor

Re-scale to nation-wide percentile

Analysis of Hierarchy Claim

Facilities Support Value Engagement Impact InvolvementFacilities 1.00 0.53 0.65 0.37 0.25 0.04Support 1.00 0.72 0.78 0.46 0.16Value 1.00 0.65 0.55 0.25Engagement 1.00 0.61 0.53Impact 1.00 0.53Involvement 1.00n=425

Pearson Correlations Existing Order

Facilities Value Support Engagement Impact InvolvementFacilities 1.00 0.65 0.53 0.37 0.25 0.04Value 1.00 0.72 0.65 0.55 0.25Support 1.00 0.78 0.46 0.16Engagement 1.00 0.61 0.53Impact 1.00 0.53Involvement 1.00

Pearson Correlations Reverse Value and Support


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Summary from “Power of Habit”

In 2000, for instance, two statisticians were hired by the YMCA—one of the nation’s largest nonprofit organizations—to use the powers of data-driven fortune-telling to make the world a healthier place. The YMCA has more than 2,600 branches in the United States, most of them gyms and community centers. About a decade ago, the organization’s leaders began worrying about how to stay competitive. They asked a social scientist and a mathematician—Bill Lazarus and Dean Abbott—for help.

The two men gathered data from more than 150,000 YMCA member satisfaction surveys that had been collected over the years and started looking for patterns. At that point, the accepted wisdom among YMCA executives was that people wanted fancy exercise equipment and sparkling, modern facilities. The YMCA had spent millions of dollars .


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Summary from “Power of Habit”: YMCA Satisfaction

Retention, the data said, was driven by emotional factors, such as whether employees knew members’ names or said hello when they walked in. People, it turns out, often go to the gym looking for a human connection, not a treadmill. If a member made a friend at the YMCA, they were much more likely to show up for workout sessions. In other words, people who join the YMCA have certain social habits. If the YMCA satisfied them, members were happy. So if the YMCA wanted to encourage people to exercise, it needed to take advantage of patterns that already existed, and teach employees to remember visitors’ names. It’s a variation of the lesson learned by Target and radio DJs: to sell a new habit—in this case exercise—wrap it in something that people already know and like, such as the instinct to go places where it’s easy to make friends.

“We’re cracking the code on how to keep people at the gym,” Lazarus told me. “People want to visit places that satisfy their


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Conclusions

The “best” solutions are not always “good” solutions– There is often more than one way to approach a

solution– It is often unclear even to the end customer what

solution is best until the solution exists on paper Interactions are the Key (or why trees

improve regression models)– Main effects are interesting, but deeper insights

gained from subgroups Don’t give up

– Matching data to decisions is difficult business– Get feedback; make sure the story themodel tells is

understood by decision-makers

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