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Y3S2 MKTG103 > Data Analysis IV: Cluster Analysis (Week 11) > Flashcards

Data Analysis IV: Cluster Analysis (Week 11) Flashcards

(50 cards)

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1
Q

What is the aim of cluster analysis?

A

To form clusters/segments

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2
Q

What is market segmentation?

A

Involves viewing a heterogeneous mkt as a number of smaller homogeneous mkts,

in response to differences b/w customers, and acting upon these diffs. b/w subgroups

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3
Q

What are the steps involved for segmentation?

A
  • Determine segmentation BASIS
  • Determine segmentation METHOD
  • CREATE segments
  • DESCRIBE segments
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4
Q

What are the steps involved for targeting?

A
  • SELECT one or more segments
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5
Q

What are the steps involved for positioning?

A
  • Develop STRATEGY & TACTICS for selected segments
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6
Q

What are the types of segmentation basis?

A

General (consumer-based) vs. product specific

Observable (objective) vs. unobservable (subjective)

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7
Q

What are general & observable variables to form a segmentation basis?

A

Cultural, geographic, demographic & socio-economic variables

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8
Q

What are product-specific & observable variables to form a segmentation basis?

A

User status, user frequency, store loyalty & patronage, situations

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9
Q

What are general & unobservable variables to form a segmentation basis?

A

Psychographics, values, personality & lifestyle

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10
Q

What are product-specific & unobservable variables to form a segmentation basis?

A

Psychographics, benefits, perceptions, elasticities, attributes, preferences, intentions

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11
Q

What are the criteria for effective segmentation?

A

Segments should be:

  • Identifiable
  • Substantial
  • Accessible
  • Stable
  • Responsive
  • Actionable
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12
Q

Why is a substantial segment necessary for effective segmentation?

A

Sizable segment: To maintain profitability

Very costly to create multiple mktg msgs for diff. small segments

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13
Q

Why is an accessible segment necessary for effective segmentation?

A

Able to reach individual segments separately (and target specific groups)

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14
Q

Why is a stable segment necessary for effective segmentation?

A

Stable over time, not switching from one to another

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15
Q

Why is a responsive segment necessary for effective segmentation?

A

Responsive - Diff. segments give diff. responses

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16
Q

Why is an actionable segment necessary for effective segmentation?

A

Able to distinguish between diff. segments, diff. platforms

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17
Q

How do we form clusters? (i.e. What are the segmentation methods?)

A
  1. A-priori

2. Post-hoc

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18
Q

What is the a-priori segmentation method?

A

Segments determined by researchers

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19
Q

What is the post-hoc segmentation method?

A

Based on analyses.
Hard clustering - e.g. cluster analysis
Soft clustering - e,g, latent class analysis

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20
Q

What is hard clustering?

A

Person CANNOT belong to >1 segment, can only belong to 1 cluster

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21
Q

What is soft clustering?

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A

Ppl have a certain PROBABILITY to be part of segment

22
Q

How do we want observations to be clustered?

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A

We want observations WITHIN a cluster to be CLOSE TOGETHER

And CLUSTERS to be FAR from each other

23
Q

Why is it impossible to try all options for clustering?

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A
  • Observations are closer together
  • Have more than 2 variables
  • Have many observations
  • How many clusters?
24
Q

What are the types of algorithms for cluster analysis?

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A
  1. Hierarchical

2. Iterative (e.g. k-means)

25
What is the hierarchical algorithm for cluster analysis?
Start with: all subjects separately (agglomerating) OR all in one cluster (divisive) Combine/separate until reaching the end
26
What is the iterative algorithm for cluster analysis?
Start with a solution | Move subjects b/w clusters until convergence
27
What are the types of hierarchical algorithms?
1. Agglomerating (e.g. Nearest Neighbour, Ward's) | 2. Divisive
28
What is the agglomerating hierarchical algorithm?
Start: Each subject separately Process: Join subjects/clusters together End: All subjects in 1 cluster
29
What is the divisive hierarchical algorithm?
Start: All subjects in 1 cluster Process: Split clusters End: Each subject separately
30
What are the steps involved in agglomerating hierarchical algorithm?
E.g. 15 observations Start: 15 clusters - Calculate distances b/w points - Combine points w/ smallest distance (homogeneous within clusters, heterogeneous b/w clusters) - Calculate distance b/w 14 clusters and 1 cluster - Form next cluster w/ smallest distance
31
What are the methods to calculate the distance b/w a subject and a cluster, or between 2 clusters?
1. Nearest Neighbour (Single Linkage) 2. Centroid Method 3. Furthest Neighbour (Complete Linkage) 4. Ward's Method
32
What are the steps involved for Nearest Neighbour (Single Linkage)?
Choose subject for which the distance to NEAREST subject is SHORTEST
33
What are the advantages of using Nearest Neighbour (Single Linkage) method?
Tendency to create chain-like clusters | Suitable for detecting outliers
34
What are the steps involved for the Centroid Method?
- Choose subject for which the distance to the MEAN of the cluster is SHORTEST
35
What is the advantage of using the Centroid Method?
Little influence of outliers
36
What are the steps involved for Furthest Neighbour (Complete Linkage) method?
Choose subject for which the distance to MOST FAR AWAY subject in cluster is SHORTEST
37
What is the disadvantage of using the Furthest Neighbour (Complete Linkage) method?
Very sensitive to outliers
38
What are the steps involved for Ward's Method?
Choose subject that MINIMISES WITHIN-CLUSTER VARIANCE
39
What is the advantage of using Ward's Method?
Creates cluster of SIMILAR SIZE that are relatively COMPACT
40
How do we determine whether there is an improvement if we move a subject to the other cluster?
Need to tell them: 1. No. of cluster 2. Exact composition - who's in what cluster
41
What is the 3-step approach that combines hierarchical and iterative algorithms?
1. Nearest Neighbour - To detect OUTLIERS 2. Ward's Method - To decide on NO. OF CLUSTERS and obtain initial solution for Step 3 3. K-Means - To obtain FINAL cluster solution
42
What is the output obtained from the Nearest Neighbour method? How do we identify outliers?
Output: DENDROGRAM Identify outliers: Dendrogram: - Indicates agglomeration order. Last subjects to be added may be outliers Agglomeration schedule
43
How do we decide on the number of clusters when executing Ward's Method?
- Manageable no. of clusters? - Size of clusters? - Interpretation of clusters - Large "horiz. jump" in dendrogram - Large jump in coefficients (agglomeration schedule)
44
What is the output obtained from Ward's Method?
Cluster membership in data columns - Frequency tables for e.g. 3- & 4-cluster solution Cluster means
45
What is the output obtained from K-Means Clustering?
Final cluster membership in data columns | Freq. table of final cluster size
46
What are the 2 operational issues for cluster analysis?
1. Distance measure | 2. Standardisation
47
What is the operational issue regarding distance measure?
- For continuous variables: Euclidean distance | - For binary: E.g. Simple matching coefficient
48
What is the operational issue regarding standardisation?
- Per variable: Use if variables are measured on diff. scales - Per subject: Use if subjects have very diff. means
49
What are the considerations to decide which clusters to target?
- Fit with cluster positioning? - Cluster size? - Cluster profitability
50
What are the considerations for positioning?
Diff strategy for diff. target groups? - One vs. multiple brand(s) - Diff. sales pitch

Y3S2 MKTG103 (9 decks)

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