Post Hoc Segmentation

Market SegmentationMarket Segmentation

Procedures for effectively segmenting markets...

Routes to Segmenting MarketsRoutes to Segmenting Markets

A Priori Post Hoc

Marketer-Driven

Customer-Driven

Post Hoc SegmentationPost Hoc Segmentation

Let customers tell you how the market is segmented

Profile Segments

Create Segments

Collect Data

Identify Segmentation

Variables

Focus GroupsJudgment

Past Research

Hierarchical Cluster Analysis

&K-Means Cluster

Analysis

Cross TabsDiscriminant

AnalysisCHAID

Survey

The QuestionnaireThe Questionnaire

Sections of The QuestionnaireSections of The Questionnaire

Desired Facilities & ServicesDesired Facilities & Services Reasons for Using Health ClubsReasons for Using Health Clubs Attitudes Toward Self, Exercise, NutritionAttitudes Toward Self, Exercise, Nutrition Problems Encountered in Health ClubsProblems Encountered in Health Clubs Facility Usage PatternsFacility Usage Patterns Frequency & Duration of ExercisingFrequency & Duration of Exercising DemographicsDemographics

Facilities & ServicesFacilities & Services

Very Important 1 2 3 4 5 6 7

Un- Important

Availability of professional trainers 1 2 3 4 5 6 7Full range of free weights 1 2 3 4 5 6 7Full range of nautilus type exercise machines

1 2 3 4 5 6 7

Indoor track 1 2 3 4 5 6 7Racquet ball facilities 1 2 3 4 5 6 7Day care facilities available during all hours of operation

1 2 3 4 5 6 7

Wet area including sauna, steam rooms, whirlpool

1 2 3 4 5 6 7

Tanning faciliites 1 2 3 4 5 6 7Low Price 1 2 3 4 5 6 7Full range of aerobic equipment, including treadmills, stairstepping machines, bicycles, etc.

1 2 3 4 5 6 7

Cleanliness of facilities 1 2 3 4 5 6 7Exercise classes such as aerobics, dance, etc.

1 2 3 4 5 6 7

Very Important 1 2 3 4 5 6 7

Un- Important

Swimming pool 1 2 3 4 5 6 7Special sponsored events, such as 10K runs, league softball

1 2 3 4 5 6 7

Organized children's events 1 2 3 4 5 6 7Separate men's & womens' wet area facilities 1 2 3 4 5 6 7Separate men's & womens' exercise facilities 1 2 3 4 5 6 7Health, nutrition counceling & programs available

1 2 3 4 5 6 7

Extended operating hours, I.e. past 10:pm at night, prior to 6:00am

1 2 3 4 5 6 7

Dress code for men & women identifying minimum standards for clothing in exercise aeas

1 2 3 4 5 6 7

Staff personnel available on workout floor at all times for assistance.

1 2 3 4 5 6 7

Structured workout programs with regular staff review of progress

1 2 3 4 5 6 7

Tiered pricing scheme that provides discounts for family memberships

1 2 3 4 5 6 7

Convenient location 1 2 3 4 5 6 7

Health Club Usage PatternsHealth Club Usage Patterns

Exercise Facility UsageFrom the following list of equipment & facilities, If your club has the facility, please rate how often you use that facility. If not available at your club, indicate "not available."

I always use this

itemI never use this item

Free Weights (Olymbic bars, dumbells, cables, etc.

1 2 3 4 5 6 7

Weight machinges (Universal, Body Master, Nautilus, etc.)

1 2 3 4 5 6 7

Treadmills (power or manual) 1 2 3 4 5 6 7Indoor track 1 2 3 4 5 6 7In-place bicycles 1 2 3 4 5 6 7Stair-step machines 1 2 3 4 5 6 7Rowing machines (Traditional rowing machines, cardioglide, etc.)

1 2 3 4 5 6 7

Racquet ball 1 2 3 4 5 6 7Swimming pools 1 2 3 4 5 6 7Other _______________________ 1 2 3 4 5 6 7

Services & Class UsageFrom the following list of services and classes, If your club has the service or class, please rate how often you use that item. If not available at your club, indicate "not available."

I always use this


Personal trainers 1 2 3 4 5 6 7Nutrition counceling 1 2 3 4 5 6 7Aerobics classes 1 2 3 4 5 6 7Water aerobics 1 2 3 4 5 6 7Self defense classes 1 2 3 4 5 6 7Other _______________________ 1 2 3 4 5 6 7

Support Facility UsageFrom the following list of support facilities, if your club has the facility, please rate how often you use that item. If not available at your club, indicate "not available."

I always use this


Sauna 1 2 3 4 5 6 7Steam room 1 2 3 4 5 6 7Whirlpool 1 2 3 4 5 6 7Tanning 1 2 3 4 5 6 7Showers 1 2 3 4 5 6 7Locker area 1 2 3 4 5 6 7Health or snack bar 1 2 3 4 5 6 7Day care facilities 1 2 3 4 5 6 7Massage 1 2 3 4 5 6 7

Motives for Working Out

I work out because I want to …Strongly

Agree 1 2 3 4 5 6 7StronglyDisagree

Gain muscle mass i.e. Body build 1 2 3 4 5 6 7Improve my level of cardiovascular fitness 1 2 3 4 5 6 7Lose weight 1 2 3 4 5 6 7Gain weight 1 2 3 4 5 6 7Improve my general level of fitness 1 2 3 4 5 6 7My doctor recommended it 1 2 3 4 5 6 7Improve physical appearance 1 2 3 4 5 6 7Improve body shape 1 2 3 4 5 6 7Improve muscle tone 1 2 3 4 5 6 7Relieve stress 1 2 3 4 5 6 7

Other usage patterns...Other usage patterns...

Health club visits per week Work out duration Types of outside exercise Preferred club

Creating the SegmentsCreating the Segments

The statistical procedure to use is “cluster analysis”...

The Idea….The Idea….

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1 2 3 4 5 6 7

Importance of Free Weights

Impo

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Importance of Weight Machines

Impo

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How to do the analysis...How to do the analysis...

SPSS makes it very easy!

The data from the questionnaire….

Select K Means cluster analysis -- the most popular

tool for this application!

Enter variables that want to base clusters on -- “importances”

Tell SPSS to save cluster membership

for later use in profiling…a must!

Select ANOVA to get a summary of

univariate F’s for determining statistical

significance of each clustering variable...

Current iteration is 7

Minimum distance between initial centers is 4.8040

Iteration Change in Cluster Centers 1 2 3 4 1 .1834 .0767 .6135 .0718 2 .1639 .0286 .5144 .0000 3 .1647 .0286 .5027 .0000 4 .3036 .0245 .6448 .0000 5 .3916 .0241 .5189 .0000 6 .3383 .0250 .3502 .0000 7 .1019 .0268 .1147 .0000

How the solution is generated...How the solution is generated...

Number of Cases in each Cluster.

Cluster unweighted cases weighted cases

1 156.0 156.0 2 201.0 201.0 3 143.0 143.0 4 75.0 75.0

Missing 0 Valid cases 575.0 575.0

The final cluster assignments...The final cluster assignments...

New variable that holds

cluster membership

Analysis of Variance.

Variable Cluster MS DF Error MS DF F Prob

TRAINERS 46.5255 3 .966 571.0 48.1288 .000 WEIGHTS 353.1055 3 .856 571.0 412.4877 .000 MACHINES 10.5160 3 .576 571.0 18.2388 .000 TRACK 74.6566 3 1.184 571.0 63.0428 .000 RBALL 80.3346 3 1.430 571.0 56.1644 .000 DAYCARE 208.0311 3 .923 571.0 225.3721 .000 WETAREA 98.4000 3 .692 571.0 142.1097 .000 TANNING_ 326.4709 3 .677 571.0 482.2300 .000 PRICE 8.2987 3 1.404 571.0 5.9090 .001 AEROBIC 62.4064 3 .819 571.0 76.1927 .000 CLEAN 55.3557 3 1.187 571.0 46.6031 .000 CLASSES 279.0349 3 .765 571.0 364.5776 .000 POOL .8872 3 1.562 571.0 .5679 .636 EVENTS 5.1712 3 .522 571.0 9.9031 .000 CHILD 191.8856 3 .786 571.0 244.0231 .000 SEPWET 256.8322 3 1.055 571.0 243.4078 .000 SEPEX 210.8816 3 .546 571.0 386.1357 .000 COUNCEL 40.2332 3 1.248 571.0 32.2218 .000 HOURS 159.8774 3 .738 571.0 216.4185 .000 DRESS 225.6544 3 .694 571.0 324.8095 .000 STAFF 75.6334 3 1.309 571.0 57.7527 .000 PROGRAMS 345.2663 3 1.003 571.0 344.0172 .000 TIERPRIC 64.7893 3 1.475 571.0 43.9161 .000 LOCATION 34.2467 3 1.475 571.0 23.2127 .000

Significance of each Significance of each variable...variable...

Limitations...Limitations...

Must tell program how many clusters to generate…

How initial cluster centers are selected may bias final assignment of individual to clusters

Limitations overcome by – Starting with hierarchical clustering

• Determine how many clusters probably exist• Determining initial cluster centers

– Multiple K Means runs using 3 to 10 clusters

Hierarchical Cluster Hierarchical Cluster AnalysisAnalysis

Select Hierarchical Cluster in same

menu as K Means...

The most important part of the analysis

is the “dendrogram”

Stick with the default values on

method unless you are very experienced

with this type of analysis...

Rescaled Distance Cluster Combine

C A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+

Case 11 11 Case 50 50 Case 81 81 Case 4 4 Case 67 67 Case 84 84 Case 89 89 Case 99 99 Case 90 90 Case 135 135 Case 110 110 Case 117 117 Case 72 72 Case 131 131 Case 51 51

The dendrogram...The dendrogram...

Cases are “clustered” based

on proximity measure that combines all

identified clustering

variables. These cases combined

first..

Additional cases are added to

existing clusters based on how

“close” they are to that cluster…

clusters are combined to

clusters in the same way.

Once dendrogram is analyzed and

number of clusters is determined, can

rerun the analysis specifying the

number of clusters. Program will then

save cluster membership

information in data file.

Variable with cluster membership added

to data file. Use this variable to compute “cluster means” on

each clustering variable for

“seeding” K Means analysis...

Seeds of K Means analysis...Seeds of K Means analysis...

Data file containing “seed” for K Means cluster run…These seeds are the means

for each clustering variable in each cluster group from the Hierarchical

cluster analysis...

Profiling the ClustersProfiling the Clusters

Characterizing market segments via cross tabs

Clustering Variables

Clusters Defined by KMEANS Group TotalCluster Variables Psuedo Body Social Fitness Middleaged Builders Exercisers Purists Must Do's

TRAINERS Mean 4.08 2.95 3.28 2.76 3.32 StdDev .88 .84 1.25 .80 1.08 Count 157 201 142 75 575 WEIGHTS Mean 1.69 4.00 2.51 5.73 3.23 StdDev .49 1.22 .72 .56 1.60 Count 157 201 142 75 575 MACHINES Mean 2.08 2.60 2.46 2.67 2.43 StdDev .43 .80 .71 .86 .74 Count 157 201 142 75 575 TRACK Mean 3.65 4.09 2.56 4.11 3.59 StdDev 1.08 1.22 .83 1.17 1.25 Count 157 201 142 75 575 RBALL Mean 3.85 4.11 3.39 5.53 4.05 StdDev 1.28 1.24 1.18 .61 1.33 Count 157 201 142 75 575 DAYCARE Mean 5.64 4.73 5.60 2.47 4.90 StdDev .64 1.29 .55 .63 1.37 Count 157 201 142 75 575 WETAREA Mean 4.62 2.98 3.01 3.06 3.45 StdDev .89 .77 .82 .87 1.10 Count 157 201 142 75 575 Tanning faciliites Mean 5.42 2.62 5.40 5.07 4.39 StdDev .83 .79 .86 .82 1.54 Count 157 201 142 75 575 PRICE Mean 3.45 3.91 3.85 4.02 3.78 StdDev 1.19 1.18 1.15 1.25 1.20 Count 157 201 142 75 575 AEROBIC

How the segments differ on the variables used to create

the clusters

Count Row Pct Pseudo B Social E Fitness Middle-A ody Buil xerciser Fanatics ges Must Row 1 2 3 4 TotalSEX 1 138 11 94 243 Male 56.8 4.5 38.7 42.3 2 18 190 49 75 332 Female 5.4 57.2 14.8 22.6 57.7 Column 156 201 143 75 575 Total 27.1 35.0 24.9 13.0 100.0

Demographic profiling...Demographic profiling...

Simple cross tabs showing gender differences by

segment

Row Pct Pseudo B Social E Fitness Middle-A ody Buil xerciser Fanatics ges Must Row 1 2 3 4 TotalMARRIED 1 22 19 71 47 159 Currently marrie 13.8 11.9 44.7 29.6 27.7 2 122 115 42 6 285 Never married 42.8 40.4 14.7 2.1 49.6 3 12 67 30 22 131 Divorced 9.2 51.1 22.9 16.8 22.8 Column 156 201 143 75 575 Total 27.1 35.0 24.9 13.0 100.0

Demographic profiling...Demographic profiling...

Marital status by segment

Count Row Pct Pseudo B Social E Fitness Middle-A ody Buil xerciser Fanatics ges Must Row 1 2 3 4 TotalINCOME 1 6 2 3 11 0-10,000 54.5 18.2 27.3 1.9 2 2 73 21 26 122 10,000-20,000 1.6 59.8 17.2 21.3 21.2 3 49 99 19 36 203 20,000-30,000 24.1 48.8 9.4 17.7 35.3 4 38 22 11 10 81 30,000-40,000 46.9 27.2 13.6 12.3 14.1 5 26 1 24 51 40,000-50,000 51.0 2.0 47.1 8.9 6 27 33 60 50,000-60,000 45.0 55.0 10.4 7 11 31 42 60,000-70,000 26.2 73.8 7.3 8 3 2 5 >70,000 60.0 40.0 .9 Column 156 201 143 75 575 Total 27.1 35.0 24.9 13.0 100.0

Income by segment

KMEANS Segments Pseudo Body Social Fitness Middle-Ages Builders Exercisers Fanatics Must Do's Count 156 201 143 75 OVEREAT Mean 3.54 3.51 4.03 2.32 StdDev .80 1.21 1.36 .57 Count 156 201 143 75 Intersting Mean 3.97 2.62 4.03 4.07 StdDev 1.16 .85 1.31 1.28 Count 156 201 143 75 BYSELF Mean 4.18 5.31 3.81 5.11 StdDev 1.35 .92 1.23 .81 Count 156 201 143 75 WEIGHT Mean 4.40 4.00 3.64 2.77 StdDev 1.27 1.23 1.31 .83 Count 156 201 143 75 FAT Mean 4.16 4.12 3.69 2.36 StdDev 1.27 1.19 1.25 .59 Count 156 201 143 75 EATRITE Mean 3.93 3.99 3.32 3.03 StdDev 1.27 1.18 1.41 .69 Count 156 201 143 75 FORCE Mean 4.91 3.88 4.10 1.93 StdDev 1.18 1.24 1.16 .42 Count 156 201 143 75 WTSUP Mean 4.05 4.01 3.17 1.69 StdDev 1.21 1.25 1.42 .21 Count 156 201 143 75 FAMILY Mean 3.78 4.18 3.44 1.96 StdDev 1.19 1.21 1.34 .41 Count 156 201 143 75 TOOMUCH

Attitudes about

health & fitness by segment

Profiling segments with Profiling segments with discriminant analysisdiscriminant analysis

Determining which variables are most important for

differentiating between segments...

Action Vars Wilks'Step Entered Removed in Lambda Sig. Label

1 TANNING_ 1 .28300 .0000 Tanning faciliites 2 WEIGHTS 2 .10332 .0000 3 SEPEX 3 .05584 .0000 4 PROGRAMS 4 .03266 .0000 5 DAYCARE 5 .02352 .0000 6 WETAREA 6 .01802 .0000 7 SEPWET 7 .01427 .0000 8 CLASSES 8 .01152 .0000 9 DRESS 9 .00980 .0000 10 HOURS 10 .00839 .0000 11 TRACK 11 .00745 .0000 12 STAFF 12 .00694 .0000 13 CLEAN 13 .00651 .0000 14 COUNCEL 14 .00614 .0000 15 RBALL 15 .00579 .0000 16 TIERPRIC 16 .00550 .0000 Tierprice 17 AEROBIC 17 .00527 .0000 18 TRAINERS 18 .00507 .0000 19 CHILD 19 .00494 .0000

Variables are listed in order of importance for predicting

segment membership...

---------------- Variables not in the Analysis after Step 19 ----------------

MinimumVariable Tolerance Tolerance F to Enter Wilks' Lambda

MACHINES .9341766 .7346654 2.8795581 .0048653PRICE .9578358 .7348780 .3783368 .0049313POOL .9638854 .7379141 .6952304 .0049229EVENTS .9611853 .7420389 1.6797645 .0048968LOCATION .9654727 .7418773 1.2001232 .0049094

These variables are not very important for predicting segment membership.

Either not important to any group (e.g. “events”), or equally important (e.g.

“machines”).

Territorial Map * indicates a group centroid (Assuming all functions but the first two are zero)

Canonical Discriminant Function 1 -12.0 -8.0 -4.0 .0 4.0 8.0 12.0 C 12.0 21 a 21 n 21 o 21 n 21 i 21 c 8.0 21 a 21 l 21 21 D 21 i 21 s 4.0 21 c 21 r * 21 i 221 m 2 223331 i 4222222 2233 31 n .0 444444222222 2233 31 * a 444444222222 233 * 31 n 44444422222223 31 t 4444433 31 443 31 F 43 31 u -4.0 43 31 n 43 31 c * 433 31 t 443 31 i 43 31 o 43 31 n -8.0 43 31 433 31 2 443 31 43 31 4331 431 -12.0 431 -12.0 -8.0 -4.0 .0 4.0 8.0 12.0

A “territorial map”

showing the relative

locations of segment

centers as predicted from the

“important variable” set.

Predicted & actual group Predicted & actual group membership...membership...

No. of Predicted Group Membership Actual Group Cases 1 2 3 4-------------------- ------ -------- -------- -------- --------

Group 1 156 155 0 1 0 99.4% .0% .6% .0%

Group 2 201 0 201 0 0 .0% 100.0% .0% .0%

Group 3 143 0 0 143 0 .0% .0% 100.0% .0%

Group 4 75 0 0 0 75 .0% .0% .0% 100.0%

Percent of "grouped" cases correctly classified: 99.83%

Using demographics….Using demographics….


Group 1 156 121 15 20 0 77.6% 9.6% 12.8% .0%

Group 2 201 11 189 1 0 5.5% 94.0% .5% .0%

Group 3 143 12 26 105 0 8.4% 18.2% 73.4% .0%

Group 4 75 0 0 0 75 .0% .0% .0% 100.0%

Importance of demographics...Importance of demographics...

Action Vars Wilks'Step Entered Removed in Lambda Sig. Label

1 AGE 1 .40727 .0000 Age of respondent 2 INCOME 2 .10853 .0000 Income Category 3 EDUCATIO 3 .06476 .0000 Educational Attainment 4 SEX 4 .03816 .0000 Gender 5 RESTYPE 5 .03635 .0000 Residence Type 6 MARRIED 6 .03511 .0000 Marital Status 7 OCCUPATI 7 .03412 .0000 Occupation 8 FAMSIZE 8 .03338 .0000 Household Size

Attitudinal variables….Attitudinal variables….


Group 1 156 148 0 8 0 94.9% .0% 5.1% .0%

Group 2 201 0 200 1 0 .0% 99.5% .5% .0%

Group 3 143 4 0 139 0 2.8% .0% 97.2% .0%

Group 4 75 0 0 0 75 .0% .0% .0% 100.0%

Percent of "grouped" cases correctly classified: 97.74%

Important attitudes are...Important attitudes are... Action Vars Wilks'Step Entered Removed in Lambda Sig. Label

1 SHY 1 .28029 .0000 2 QUIET 2 .11801 .0000 3 CONFIDE 3 .08239 .0000 4 VITAMINS 4 .05787 .0000 5 TALK 5 .04330 .0000 6 APPEAR 6 .03364 .0000 7 OTHERS 7 .02734 .0000 8 GUILTY 8 .02283 .0000 9 LIFE 9 .01945 .0000 10 OVEREAT 10 .01708 .0000 11 PROBLEMS 11 .01523 .0000 12 TIME 12 .01385 .0000 13 SIT 13 .01271 .0000

Profile Segments

A Priori SegmentationA Priori Segmentation

Create Segments

Collect Data

Focus GroupsJudgment

Past Research

Cross TabsDiscriminant

Analysis

Select Segmentation Base(s)

Documents

Post Hoc Segmentation