Abbott a more transparent interpretation of health club surveys

Abbott - A More Transparent Interpretation of Health Club Surveys

Dean Abbott Abbott Analytics, Inc.

URL: http://www.abbottanalytics.com Blog: http://abbottanalytics.blogspot.com Twitter: @deanabb

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

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Salford Analytics and Data Mining Conference 2012

San Diego, CA May 24, 2012

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.

Copyright © 2004-2012, Abbott Analytics, Inc. and Seer Analytics, Inc. All rights reserved. 2

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


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


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”


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


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


Member Survey Question Categories


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


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


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


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


Factor Analysis: Key Factors


Factor 1

0.000.200.400.600.801.00

Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Top Question Loadings

Load

ing

Valu

e

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

Load

ing

Valu

es

Member Survey Factor Analysis Loadings


Factor Description Staff Cares

Facilities clean/safe Equipment Registration

Condition of Specific

Equipment

Friendly / Competent

StaffFinancial

Assistance ParkingFactor Number !"#$%&' !"#$%&( !"#$%&) !"#$%&* !"#$%&+ !"#$%&, !"#$%&- !"#$%&.

Q2 0.295 0.238 0.115 0.458 0.054 0.380 (0.016) 0.095Q3 0.217 0.143 0.093 0.708 0.094 0.077 0.033 0.048Q4 0.298 0.174 0.106 0.601 0.068 0.266 0.002 0.062Q5 0.442 0.198 0.087 0.173 0.025 0.613 (0.021) 0.053Q6 0.417 0.254 0.142 0.318 0.044 0.584 (0.008) 0.058Q7 0.406 0.277 0.167 0.252 0.045 0.461 0.003 0.092Q8 0.774 0.058 0.041 0.093 0.052 0.113 0.036 0.061Q9 0.733 0.175 0.108 0.145 0.052 0.260 0.024 0.052Q10 0.786 0.139 0.079 0.110 0.060 0.218 0.029 0.046Q11 0.765 0.120 0.101 0.132 0.089 0.015 0.038 0.047Q12 0.776 0.090 0.049 0.087 0.041 0.014 0.042 0.053Q13 0.145 0.728 0.174 0.112 0.106 0.110 0.006 0.018Q14 0.191 0.683 0.163 0.151 0.053 0.124 0.013 0.089Q15 0.102 0.598 0.141 0.090 0.162 0.070 0.029 0.152Q16 0.100 0.370 0.133 0.082 0.028 0.035 0.009 0.843Q17 0.128 0.567 0.229 0.102 0.116 0.080 0.018 0.224Q18 0.148 0.449 0.562 0.116 0.132 0.114 0.010 0.042Q19 0.129 0.315 0.811 0.101 0.102 0.103 0.002 0.063Q20 0.171 0.250 0.702 0.086 0.145 0.078 0.016 0.149Q23 0.271 0.220 0.188 0.316 0.121 0.046 0.069 0.019Q24 0.363 0.165 0.128 0.140 0.080 0.095 0.035 0.076

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

Q44Q22Q25

factor3.2

factor3.9

factor3.1

factor3.4

factor3.3

factor3.8

factor3.10

factor3.6

factor3.5

factor3.7

Question/Factor

Regr

essi

on C

oeffi

cien

t


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+

Surv

ey Q

uest

ions

3 key questions

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:


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 2 Staff 1

goals

value

Drivers of Satisfaction

Current Year vs. Last Year

Prior year

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



Peer Average

drivers of excellence

May 2003

Gardena YMC A

© 2003 S eer Analytics , L LCTampa, F L 33602

S ame

Importance

Relative Pe

rform

ance

Worse

Better

2002

2003 vs .2002Staff cares

Staff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome

Peer Average


May 2003

Gardena YMC A


S ame

Importance

Relative Pe

rform

ance

Worse

Better

2002


Staff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome


May 2003

Gardena YMC A


S ame

Importance

Relative Pe

rform

ance

Worse

Better

2002


Staff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcomePrior year


Feel welcome

Meet fitness goals

Facilities

Value

Equipment

Staff competence

Staff caresdrivers of excellence

May 2003

Montebello YMC A


S ame

Importance

Relative Perform

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 YMC A


S ame

Importance

Relative Perform

ance

Worse

Better

2002

2003 vs .2002

Peer Average

Prior year



May 2003

Torrance S outh YMC A


S ame

Importance

Relative Perform

ance

Worse

Better

2002

2003 vs .2002

Peer Average

Staff caresStaff competence

Equipment

Value

Facilities

Meet fitness goals

Feel welcome

Prior year

I love this visualization!

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

Staff cares

Staff competence

Equipment

Value Facilities

Meet fitness goals

Feel welcome


May 2003

C ulver YMC A


S ame

Importance

Relative Perform

ance

Worse

Better

2002

2003 vs .2002

Peer Average

Staff cares

Staff competence

Equipment

Value Facilities

Meet fitness goals

Feel welcome


May 2003

C ulver YMC A


S ame

Importance

Relative Perform

ance

Worse

Better

2002

2003 vs .2002

Peer Average

Prior year

23

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


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


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


Satisfaction Model Performance

0

20

40

60

80

100

0 20 40 60 80 100

% C

lass

% Population

0

20

40

60

80

100

Misclassification for Learn Data

N N Mis- PctCases Classed Error

0 33,220 8,178 24.62 0.251 14,845 2,622 17.66 0.18

Class Cost

Cases % of Node % Cum % Cum % % Cases Cum LiftTgt. Class Tgt. Class Tgt. Class Tgt. Class Pop Pop in Node lift Pop

1 7,289 72.788 49.101 49.101 20.834 20.834 10,014 2.357 2.3579 904 51.984 6.09 55.19 24.452 3.618 1,739 2.257 1.6832 2,317 50.612 15.608 70.798 33.977 9.525 4,578 2.084 1.6393 471 46.45 3.173 73.971 36.087 2.11 1,014 2.05 1.50410 431 43.186 2.903 76.874 38.163 2.076 998 2.014 1.3984 349 40.819 2.351 79.225 39.942 1.779 855 1.984 1.32212 462 38.404 3.112 82.337 42.445 2.503 1,203 1.94 1.243

Node



Satisfaction Model from CART®

Q 31

Q 34 $

Q 36

Q 19

Q 13

Q 22

Q 13

Q 31

Q 6

Q 36

Q 31

Q 2

Q 13

Q 25

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)

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

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

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 RULE If 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.

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

Q 22

Q 25

Q 44

Q 25

Q 22

Q 31

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%), •  18.4% of all strong recommends

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%), •  6.1% of all strong recommends RULE If 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

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 RULE If 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


Intend to Renew Model from CART®

Q 47

Q 22

Q 44

Q 27

Q 22

Q 44

Q 25 •  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

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%), •  8.9% of all intend to renew

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%), •  15.6% of all intend to renew RULE If 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.

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 RULE If 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.

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


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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 $$


Top Terminal Nodes Comprise More than 70% of Hits


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 2009 Percent

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

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 .

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

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
