Tim bock training day - 2012

Tim Bock, Q, Australia Festival of NewMR 2012 – Training Day – Session 1

A Presenta*on from The Fes*val of NewMR – Training Day

3 December 2012

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An Introduc*on to Latent Class Analysis for Marke*ng Segmenta*on Tim Bock, Q


An Introduction to Latent Class Analysis for Marketing

Segmentation Tim Bock, Q

www.q-researchsoftware.com [email protected]

+61 425 241 989


Overview

•  Latent class analysis versus cluster analysis –  Theore:cal difference: probabili:es –  Prac:cal differences:

•  Non-‐numeric data (e.g., categorical data) •  Missing values

•  Applica:on: what do research buyer’s want? – Missing values –  Response bias


Latent class analysis turns data into segments

Worriers

Concerned

with decay

preven:on

Sociables Concerned with tooth colour

Sensory Concerned with

flavour

Independent Concerned with price

Adapted from: Haley, R. I. (1968). "Benefit Segmenta:on: A Decision Oriented Research Tool." Journal of Marke:ng 30(July): 30-‐35.


Cluster Analysis

Latent Class Analysis


Cluster Analysis versus Latent Class Analysis for segmenta*on

•  Latent class analysis is theore:cally superior –  Clearly-‐stated assump:ons –  Cluster analysis is inconsistent with elementary laws of probability (in par:cular, Bayes’ Theorem)

•  Latent class analysis so_ware is superior –  Any type of data (via distribu:onal assump:ons): Categorical, Conjoint, Choice, MaxDiff, Rankings, etc.

–  “Mixed” data (e.g., categorical and numeric) – Missing values –  Response biases


0 5 10 15 20 25 30 35

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Specify number of clusters (k)

k-‐Means Cluster Analysis


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Randomly allocate respondents to clusters


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Compute cluster means


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k-‐Means Cluster Analysis Specify number of

clusters (k)



Allocate respondents to most similar clusters

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k-‐Means Cluster Analysis Specify number of

clusters (k)










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Compute cluster means Repeat un:l changes in

cluster means are small or non-‐existent





Repeat un:l changes in

cluster means are small or non-‐existent



Specify number of classes (k)

Randomly allocate respondents to classes

Compute class parameters*

Compute probability of each respondent being

in each class


class parameters are small or non-‐existent


Allocate respondents classes with highest

probabili:es

This is a comparison of batch k-‐means and Latent Class Analysis with an EM Algorithm. See Celeux and Govaert (1991), “Clustering criteria for discrete data and latent class models”, Journal of Classifica:on, 8(2) for a more mathema:cal comparison. * The class parameters are computed as weighted averages of the segmenta:on variables, where the weights are the probabili:es of each respondent being in each segment.


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Cluster Analysis

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Cluster Analysis



missing values


How many clusters (or classes) can you see in this data?


Missing values and latent class analysis

A B C D

Cluster 1 1 2 3 4

Cluster 2 4 3 2 1

Cluster 3 1 2 2 1

Class means


Missing values and cluster analysis

A B C D

Cluster 1 1 2 3 3

Cluster 2 MISSING MISSING MISSING MISSING

Cluster 3 3 3 2 1

Cluster means


distribu*onal assump*ons


Distribu*onal assump*ons •  Basic idea: instruct a latent class models

about how to interpret the data •  Categorical assump:on:

look only at matches –  Example: respondent 1 is most similar to 2 and 3 (i.e., they match on

two variables)

•  Numeric assump:on: assign values and compute differences (e.g., Agree = 3, Neither = 2, Disagree = 1) –  Example: respondent 1 is most similar to respondent 3

•  Ranking assump:on: look at rela:ve order –  Respondent 1 is iden:cal to respondent 4

Variable

ID A B C

1 Agree Agree Neither

2 Agree Disagree Neither

3 Agree Neither Neither

4 Neither Neither Disagree


Example: Categorical data Data Shop Agree (A) or disagree (D) that “It is important to

shop around” Understand Agree (A) or disagree (D) that “I understand my

company's communica:on needs” Key Agree (A) or disagree (D) that “Communica:ons

technology is key to our business” Interested Agree (A) or disagree (D) that “I am interested in

communica:ons technology” Value Agree (A) or disagree (D) that “Value for money

is more important to us than gelng the best technology”

ID

Shop

Und

erstan

d

Key

Interest

Value

1 A A A A D 2 A A A D A 3 A A A A D 4 A A D A A 5 A D A D D 6 D A A A D 7 A D A D D 8 D D A A D 9 A A A A A 10 A A A A D 11 D A D D A 12 A A A A A 13 D D D D D … … … … … …


Specify number of classes (k)

Randomly allocate respondents to classes

Compute class parameters

Compute probability of each respondent being

in each class


class parameters are small or non-‐existent


Allocate respondents classes with highest

probabili:es


ID

Shop

Und

erstan

d

Key

Interest

Value

… … … … … … 6 D A A A D … … … … … …

Data Parameters

Looking at the parameters, which class do you think respondent 6 belongs to?

Size Shop Under-‐

stand Key Interest Value

Class 1 67% Agree 40% 40% 48% 16% 53%

Disagree 60% 60% 52% 84% 47%

Class 2 33% Agree 65% 90% 88% 100% 26%

Disagree 35% 10% 12% 0% 73%


Compu*ng the probability of each respondent being in each class

Size Shop Under-‐

stand Key Interest Value

Class 1 67% Agree 40% 40% 48% 16% 53%

Disagree 60% 60% 52% 84% 47%

Class 2 33% Agree 65% 90% 88% 100% 26%

Disagree 35% 10% 12% 0% 73%

ID

Shop

Und

erstan

d

Key

Interest

Value

… … … … … … 6 D A A A D … … … … … …

Data Parameters

Ini:al best guess of probabili:es is given by the class sizes: Class 1: 67% Class 2: 33%

Prior

Probability that somebody in each class would give answers: Class 1: 60%×40%×48%×16%×47% = 1% Class 2: 35%×90%×88%×100%×73% = 20%

Class condi:onal densi:es 67%×1% 67%×1% + 3%×20% 33%×20% 67%×1% + 33%×20%

Posterior probability (Probability of being in a class)

Class 1: = 9% Class 2: = 91%


Applica*on

n = 1,145 market researchers (GRIT2012/2013) “How important do you think each of the following atributes is to clients when they select a research provider?” 5 POINT SCALE RANDOMLY SHOW 15 OF 25 ATTRIBUTES TO EACH RESPONDENT


Cluster Analysis


Numeric Assump*on

Lowest price Previous experience with client/supplier

Rapid response to requests Listens well and understands client needs Flexibility on changing project parameters

Familiarity with client needs Completes research in an agreed-‐upon :me

Good rela:onship with client/supplier Breadth of experience in the target segment

Good reputa:on in the industry Familiarity with the industry or category Length of experience/:me in business

Has an access panel Company is financially stable

Has knowledgeable staff High quality analysis

Provides data analysis services Understands new consumer communica:on channels & technologies

Also does qualita:ve research Consulta:on on best prac:ces and methodology effec:veness

Uses sophis:cated research technology/strategies Provides highest data quality

Uses the latest sta:s:cal/analy:cal packages Offers unique methodology or approach

Uses the latest data collec:on technology

Segment 1 (45%)

%

Segment 2 (11%)

%

Segment 3 (45%)

% Segment 1 Segment 2 Segment 3

Numeric 3 class

Lowest pricePrevious experience with client/supplier

Rapid response to requestsLi s tens well and understands client needsFlexibi l i ty on changing project parameters

Fami l iarity with client needsCompletes research in an agreed-‐upontimeGood relationship with client/supplierBreadth of experience in the target

segmentGood reputation in the industryFami l iari ty wi th the industry or categoryLength of experience/time in business

Has an access panelCompany is financially stable

Has knowledgeable staffHigh quality analysis

Provides data analysis servicesUnderstands new consumercommunication channels & technologiesAl so does qualitative researchConsul tation on best practices and

methodology effectivenessUses sophis ti cated research technology/strategiesProvides highest data qualityUses the latest statistical/analyticalpackagesOffers unique methodology or approach

Uses the latest data collection technology

Impo

rtan

ce to

clients (Res

earch providers vie

wpoint): To

p 2 boxes (out of 5) -‐ reordered

50889598

8399979590939286

3671

97968684

6696

7596

577579

68736567

47506775

314033

1633

1358

302827

161827

151031

17

55878997

71959194

818285

512

369691

5945

2271

3769

739

14

Top 2 Box (%)

Percentages are Top 2 Box Scores. Where values are significantly higher than average the bars are shaded orange. Darker shades of orange correspond to smaller p-‐values.


Categorical Assump*on













Segment 1 (50%)

%

Segment 2 (50%)

% Segment 1 Segment 2

Al l categories










Impo

rtan

ce to

clients (Res

earch providers vie

wpoint): To

p 2 boxes (out of 5) -‐ reordered

41909698

871009698

899592

8124

679897

8277

5593

6989

486560

66818390

61868786

707173

431832

8574

5243

2758

3660

1343

27

Top 2 Box (%)

Percentages are Top 2 Box Scores. Where values are significantly higher than average the bars are shaded orange. Darker shades of orange correspond to smaller p-‐values.


Ranking Assump*on













Segment 1 (54%)

%

Segment 2 (46%)

% Segment 1 Segment 2

Ranking










Impo

rtan

ce to

clients (Res

earch providers vie

wpoint): To

p 2 boxes (out of 5) -‐ reordered86979898

80959495

808382

6119

4790

8162

5431

6740

6317

3526

23748191

68898890

788283

6322

539590

7571

5186

6988

4975

65

Top 2 Box (%)Percentages are Top 2 Box Scores. Where values are significantly higher than average the bars are shaded orange. Darker shades of orange correspond to smaller p-‐values.


Latent class analysis sobware Product Data/distribu*onal assump*ons Covariates* Complex

Sampling*

Sawtooth So_ware Regression (discrete choice, ranks), Max-‐Diff No No

Q Numeric, Binary, Categorical, Ranks, Par:al Ranks, Ranks with Ties, Max-‐Diff, Regression (linear, discrete choice, ranks, par:al ranks, ranks with :es, best-‐worst), Mixed data

No No

Limdep Regression (linear, discrete choice, censored, ranks, par:al ranks, counts, survival, etc.)

Yes No

SAS (PROC LCA/LTA/Mixed)

Numeric, Binary, Categorical, Growth, Regression (discrete choice, ranks, par:al ranks)

Yes Yes

MPlus Numeric, Binary, Categorical, Ordered, Categorical, Counts, Mixed data

Yes Yes

Latent gold/Latent Gold Choice

Numeric, Binary, Categorical, Growth, Ranks, Par:al Ranks, Counts, Regression (linear, discrete choice, censored, ranks, par:al ranks)

Yes Yes

* Covariates and the ability to handle complex sampling can be relevant when applying latent class analysis to non-‐segmenta:on problems (e.g., crea:ng predic:ve models).


Cluster Analysis



Thank you

Tim Bock Q


Tim Bock, Q www.q-researchsoftware.com

[email protected] +61 425 241 989

Tim bock training day - 2012

Marketing

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