Deck 8: Cluster Analysis

Question 1

Describe how American Express has achieved business success through market segmentation.

Answer 1

No Answer

Question 2

The Matching coefficient approach of measuring similarity between observations

A) is a path with right turns as if one is walking a grid in a city.
B) measures the distance as the true straight line distance between two points.
C) makes calculations based on how dissimilar two observations are from each other.
D) measures the similarity between two observations with values that represent the minimum differences between two points.

Answer 2

measures the similarity between two observations with values that represent the minimum differences between two points.

Question 3

Summarize practitioner Kaitlin Marvin's views about the use of market segmentation and cluster analysis to improve sales.

Answer 3

No Answer

Question 4

Compare and contrast agglomerative and divisive clustering approaches within the hierarchical clustering method of market segmentation.

Question 5

When running k-means clustering, it is best to begin with a dataset in which each variable is measured differently.

Question 6

In the k-means clustering analysis, the silhouette score is calculated ________.

A) when a cluster seed is randomly selected and designated as the initial cluster centroid
B) before the algorithm randomly assigns each observation to one of the k clusters
C) when the analyst decides on k initial subgroups to experiment with
D) after the cluster algorithm has assigned each observation to a cluster

Question 7

Explain how a dendrogram is used to illustrate all four measurement approaches for determining clusters in hierarchical clustering.

Question 8

Compare and contrast hierarchical and k-means cluster analysis techniques of market segmentation.

Question 9

Explain how automotive manufacturers such as Nissan Motor Group and Daimler AG use segmentation strategies to maximize marketing capabilities, new product development, pricing strategy, and advertising.

Question 10

The ________ approach of measuring similarity between observations is also referred to as the "City Block" distance measure.

A) Manhattan distance
B) Euclidean distance
C) Matching coefficient
D) Jaccard's coefficient

Question 11

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the maximum distance between observations in two different clusters.

A) complete linkage
B) average linkage
C) single linkage
D) parallel motion linkage

Question 12

In cluster analysis, a market is segmented using ________.

A) outlier values
B) shared traits
C) dissimilar characteristics
D) distance from company headquarters

Question 13

Which of the following companies spent $50 million remodeling stores and training staff according to identified consumer segments, labeled as Buzz, Jill, and Barry, to enhance in-store customer experiences, resulting in a 10 percent growth in sales?

A) RadioShack
B) Best Buy
C) Staples
D) Circuit City

Question 14

A typical silhouette score ranges between ________.

A) +1 and ?1
B) +10 and ?10
C) +5 and ?5
D) +100 and ?100

Question 15

Describe how Best Buy used insights gained through market segmentation to achieve a 10 percent growth in sales.

Question 16

Which of the following is true of the agglomerative clustering approach?

A) At the end of the process, all observations are included in a single cluster.
B) The most dissimilar observations are sequentially separated from the initial 100-observation cluster.
C) The process starts with a single cluster of 100 and quickly ends up with 100 different clusters.
D) All observations are initially assigned to a single cluster.

Question 17

The two most common techniques of cluster analysis discussed in the chapter are ________ and ________.

A) schema comparisons; DAX functions
B) distribution clustering; connectivity clustering
C) centroid clustering; density clustering
D) k-means clustering; hierarchical clustering

Question 18

In which of the following methods of linking individual observations both within and between clusters is similarity defined as the shortest distance from an object in a cluster to an object from another cluster?

A) average linkage
B) reverse motion linkage
C) single linkage
D) complete linkage

Question 19

In which of the following functions is the distance measured equivalent to the true straight line distance between two points?

A) The Euclidean distance
B) Jaccard's coefficient
C) The Manhattan distance
D) Matching coefficient

Question 20

Explain how cluster analysis functions.

Question 21

Identify a true statement regarding the divisive clustering approach of hierarchical clustering.

A) The process starts with a single cluster of 100 and ends up with 100 different clusters.
B) All observations are assigned to a cluster that has unique and common characteristics.
C) It is a bottom-up approach in which each observation is initially considered to be a separate cluster.
D) At the end of the process, all observations are included in a single cluster.

Question 22

Which of the following statements is true of the clustering process?

A) It enables companies to assign groups of customers to their network.
B) It enables marketers to forecast revenue for next quarter.
C) It enables marketers to identify hidden structures in data.
D) It typically assigns names and definitions to unrelated sections of data.

Question 23

Ward's method measures the average distance between each observation in a cluster and a cluster centroid.

Question 24

K-means clustering uses the mean value for each cluster and minimizes the distance to individual observations.

Question 25

The k-means clustering method can be inefficient and complex when used with large sample sizes.

Question 26

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the group average of observations from one cluster to all observations from another cluster.

A) bell crank linkage
B) single linkage
C) average linkage
D) complete linkage

Question 27

In the k-means clustering algorithm, what happens after observations are randomly assigned to a cluster?

A) Observations are reassigned.
B) Cluster centroids are recalculated.
C) Cluster centroids are determined.
D) Initial k clusters are determined.

Question 28

In hierarchical clustering, approaches such as ________ are most often used when numerical variables are analyzed.

A) Matching coefficient or the Manhattan distance
B) Jaccard's coefficient or the Euclidean distance
C) the Euclidean distance or the Manhattan distance
D) Matching coefficient or Jaccard's coefficient

Question 29

Distinguishing clusters from the larger population or dataset is necessary for learning and responding to different engagement or buying behaviors.

Question 30

Data groups exhibit dissimilar characteristics (heterogeneity) within cluster (intra-cluster) and homogeneity or similarities between groups (inter-cluster).

Question 31

Segmenting a market using shared characteristics is called cluster analysis.

Question 32

The Jaccard's coefficient approach of measuring similarity between observations

A) measures the distance as the true straight line distance between two points.
B) is a path with right turns as if one is walking a grid in a city.
C) makes calculations based on how dissimilar two observations are from each other.
D) measures the similarity between two observations with values that represent the minimum differences between two points.

Question 33

The first step in the k-means clustering algorithm is ________.

A) determining the initial k clusters
B) calculating cluster centroids (means)
C) recalculating and reassigning cluster centroids
D) randomly assigning and designating a cluster seed as the initial cluster centroid

Question 34

In the Manhattan distance approach of measuring similarity between observations

A) the distance is measured as the true straight line distance between two points.
B) the distance between two points is a path with right turns as if one is walking a grid in a city.
C) the similarity between two observations is measured with values that represent the minimum differences between two points.
D) the similarity is based on how dissimilar two observations are from each other.