# Summary: Marketing Models | 9781133588108

• This + 400k other summaries
• A unique study and practice tool
• Never study anything twice again
• Get the grades you hope for
• 100% sure, 100% understanding
Remember faster, study better. Scientifically proven.

## Read the summary and the most important questions on Marketing Models | 9781133588108

• ### 1.2 What is a model?

This is a preview. There are 1 more flashcards available for chapter 1.2

• #### What is the ultimate question in assessing a model? How can it be answered?

• Is it useful?
• Best answered by comparing to another, competing model.

• ### 2.1 Introduction

This is a preview. There are 1 more flashcards available for chapter 2.1

• #### What are the goals of a good segmentation scheme?

1. Identifying a group of customers who are similar to each other in their preferences and purchases regarding the brand.
2. Looking for segments to be different from group to group
• #### What does a cluster analysis algorithm do?

It takes the input variables and computes a measure of the similarities between entities (e.g. customers). Then it groups together the entities that are most similar, keeping those that are more different in different clusters.
• ### 2.2 Input variables

This is a preview. There are 2 more flashcards available for chapter 2.2

• #### What types of variables can be used by marketeers?

Indicators that are:
• Geographic
• Demographic
• Behavioral
• Attitudinal
• ### 2.3 Measures of similarity

This is a preview. There are 1 more flashcards available for chapter 2.3

• #### What is a measure of similarity among all the customers?

Correlation coefficient. Ranges from +1 (2 customers have identical patterns) to -1 ( 2 customers have very different patterns).
• #### What do you have to take notice of when using correlations?

Correlations reflect relative patterns, not mean differences. E.g. 2 customers have bought the same 3 types of books, but 1 customer bought 2x as much. So high correlation, but 1 has a higher profile than the other.
• #### Explain the measures of association method for SKU's and the simple matching coefficient (Smc).

• Measure of association method is a 2x2 matrix cross-classifying the purchases of 2 customers.
• Counts the number of products that both bought (a), that only one of them bought (b,c) and that neither of them bought (d).

• Smc is a measure of similarity and ranges from 0 to 1.
• Smc = (a+d)/(a+b+c+d).
• #### What is the Jaccard coefficient?

• Like the simple matching coefficient (Smc), but ignores the d cell in both the numerator and the denominator. It ignores SKUs that appear in neither customers
• J=a/(a+b+c)
• ### 2.4 Clustering algorithms

• #### What are hierarchical clusters?

Once 2 customers are put in the same segment, they are always together. Others might join the cluster, but the cluster is never broken into separate clusters in any of the model stages.
• ### 2.4.1 Hierarchical clustering models

This is a preview. There are 2 more flashcards available for chapter 2.4.1