Module 5 Overview (1 of 2)
In this module, you will learn about the existence and importance of more sophisticated tools for data analysis. You will also learn about preparing research reports and presentations for effectively communicating the results of marketing research projects.
Because of continuing advancements in the computer world, increased capabilities in data collection, data storage, and data processing make much more information available to business decision makers. However, having the information available is of little value unless it can be accessed and organized to support decision making. Fortunately data mining and data analysis tools are readily available to access, organize and help interpret the wealth of information that is available.
In many cases, the need for surveys to collect data has been reduced by the availability of so much information already housed in data warehouses. Although some of the data can be analyzed using less sophisticated statistical methods, multivariate techniques are required to access the power buried with much of the data. These techniques simultaneously analyze multiple measurements on each of the multiple variables present in the multidimensional marketing problem.
Factor analysis is a technique that summarizes, subsets and simplifies a large number of variables. Among the many uses of factor analysis are its capability to help identify the characteristics of different groups of customers and its capability of identifying the factors that drive customer choices.
Cluster analysis is a method used to classify customers or products or any other objects into groups that are relatively homogeneous in terms of the variables under study. It might be used in test marketing or market segmentation decisions.
Discriminate analysis is used when the dependent variable is categorical (non-metric) and the multiple independent variables are metric. A common use is in identifying characteristics of potential customers who will respond favorably to certain marketing tactics.
Conjoint analysis is used to measure the importance placed on different attributes by customers and the significance they attach to different levels of the attributes. It might be used to identify the attributes that have significant influence on product selection or advertising effectiveness.