Customer Segmentation Analysis

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Questions and Answers

In customer descriptor analysis, computing the mean value of each variable is done for each segment.

True (A)

Cluster analysis can only be done on demographics and interests.

False (B)

In the given example, the segmentation basis set is based on age and income.

False (B)

K-Means clustering is used to determine the number of segments in a dataset.

False (B)

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The optimal number of segments in the given example is 2.

False (B)

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What is the main objective of exploiting differences among customers in classical segmentation?

To increase effectiveness and attractiveness of the offering by identifying key differences among customers to develop a customized offering.

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What is the primary goal of cluster analysis in segmentation?

To partition items into groups such that items within the same group are similar (homogeneity within) and items across groups are different (heterogeneity across).

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What is the ultimate goal of a segmentation project?

To grow profits by achieving a proper balance between exploiting differences and exploiting similarities among customers.

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What is the key benefit of exploiting similarities among customers?

To increase the cost efficiency of the offering by identifying and eliminating irrelevant differences among customers.

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How do you determine the effectiveness of a segmentation project?

By observing growth and changes in the product line or marketing mix after segmentation.

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Match the following segmentation approaches with their characteristics:

Algorithmic-analytical approach = Uses market share modeling + optimization to find core features of a product Classical Segmentation-Targeting 3Cs approach = Involves cluster analysis based on preference parameters Customer descriptor analysis = Focuses on computing mean value of each variable per segment Persona development = Aims to identify attractive segments for product construction

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Match the following segmentation objectives with their benefits:

Exploiting differences among customers = Enables targeted marketing and increased sales Exploiting similarities among customers = Facilitates product development and cost reduction Determining the optimal number of segments = Helps in resource allocation and budgeting Characterizing customer segments = Improves customer satisfaction and loyalty

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Match the following demographic analysis techniques with their applications:

Cluster analysis = Identifying customer segments based on preferences Background parameter analysis = Analyzing customer demographics and interests Preference parameter analysis = Understanding customer needs and preferences K-Means clustering = Determining the optimal number of segments

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Match the following customer characterization methods with their goals:

Customer descriptor analysis = Computing mean values of variables per segment Persona development = Creating detailed customer profiles Segmentation analysis = Identifying and understanding customer segments Attribute importance analysis = Prioritizing customer preferences and needs

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Match the following segmentation outcomes with their benefits:

Increased annual profit growth = Result of successfully executing a segmentation strategy Improved customer satisfaction = Outcome of characterizing customer segments effectively Enhanced marketing effectiveness = Result of identifying and targeting attractive segments Optimal product development = Outcome of using market share modeling + optimization

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