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Cluster analysis is a statistical procedure that identifies homogeneous groups or clusters of individuals. In marketing research, cluster analysis is often used to determine whether there are distinct groups of customers with different needs, preferences, perceptions, product usage and purchasing behavior. By understanding how customers differ, management can develop products and/or marketing strategies that are tailored to each group's individual needs.
K-means analysis is a clustering technique that can be used to create customer segments. This type of cluster analysis uses a procedure in which individuals are assigned and reassigned to "clusters" or "segments" repeatedly until each individual is assigned to a final segment. Each final segment is comprised of individuals who are more similar to other people within that segment than those in other segments.
This method implicitly minimizes the diversity within each segment - and thus, in this case, produces distinct segments with homogenous needs, preferences, etc.
Factor analysis is a statistical technique that is used to identify the structure within a set of variables. By examining the association among variables, factor analytic techniques produce a smaller set of variables or factors that represent the underlying dimensions of the original set of variables. Each factor is not a single, directly measurable entity, but rather a construct that is derived from the measurement of the original set of variables. This technique is often used for the purpose of data reduction- that is, reducing a large number of variables to a smaller set of factors greatly simplifies the description and understanding of large sets of data.
Neural networks are an alternative to traditional statistical techniques for prediction, classification, segmentation, and time series analysis. A primary advantage of neural networks is that they can find non-linear relationships in the data.
They do not depend upon the same assumptions (i.e. multivariate normal distributions, equal variance-covariance matrices, etc.) as conventional techniques.
Neural networks "learn" patterns in the data, and use an iterative process to create models. They start with randomly generated weights and compare these to known predicted outputs. The weights are then adjusted and compared again.