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Cluster Analysis

Cluster analysis is a statistical procedure that identifies homogeneous groups or clusters of individuals.

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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.

Logistic Regression

Logistic regression is a technique that identifies variables that are important for distinguishing between two groups of individuals.

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It should be mentioned that other techniques such as regression and discriminant analysis can also be used to predict a dichotomous dependent variable. However, these techniques depend on certain assumptions (i.e., multivariate normality of the independent variables and equal variance-covariance matrices for the two groups). When these assumptions are violated (which they typically are in studies of this type), the results of the analysis may be less than accurate. Logistic regression does not depend on these assumptions.

Managing Brand and Customer Expectations

  • Brand work—awareness, value
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  • Measuring brand—personality, brand image
  • Repositioning brand

Factor Analysis

Factor analysis is a statistical technique that is used to identify the structure within a set of variables.

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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.  

Consider an example of a data set that includes preference ratings for a large number of specific foods such as bread, apples, blueberry pie, cereal, spare ribs, green beans, watermelon, chocolate mousse, pasta, ham, asparagus, artichoke, and roast beef.  A factor analysis of this data set would distill this large amount of information by identifying the dimensions that underlay the individual ratings.  

More specifically, dimensions are identified for groups of variables that are highly intercorrelated with each other, but are not highly correlated with variables outside of that group.  In this data set, for example, we might expect a fairly high association among preferences for cereal, pasta and bread — that is, in general, the higher an individual’s preference for bread is, the higher we might expect his preference would be for pasta and cereal.  However, knowing an individual’s preference rating for cereal, pasta, and bread would probably not be useful in predicting that individual’s preference rating for any other food on the list.  If this were the case, these three variables would be represented by a factor that might be called the “starch” factor.  Cereal, bread, and pasta would then be said to “load” on the “starch” factor. Besides the “starch” factor, other factors likely to emerge in this example include “fresh fruits,” “deserts,” “green vegetables,” and “beef.”  

When a small number of factors account for most of the variance in the original set of data, we can “explain” the original set of variables in terms of a smaller set of factors without losing important information.  Simplifying large sets of market research data in this way provides a conceptual clarity that facilitates one’s comprehension of the data and its strategic marketing implications.

Discriminant Analysis

Discriminant analysis is a technique that is used to identify variables that are important for distinguishing among groups of individuals.

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For example, it might be useful to identify those variables that determine whether or not, for a given product, an individual would be most likely to choose to deal with a (1) bank, (2) finance company, or (3) credit union. Variables to be used in such an analysis would depend on your particular interests, but might include demographics, past loan/credit experience, likely reasons for loans, and needs/benefits.

If desired, discriminant analysis can also be used to develop a procedure for predicting group membership (bank vs. finance company vs. credit union) for individuals not included in the analysis. This type of procedure might be useful in selecting potential customers for a targeted marketing campaign.

Communicating Well

These projects were designed to investigate basic attitudes customers have about the company.

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As well as the evaluations customers make of proposed programs that might be offered by a company to demonstrate its concern and caring for the needs and problems of its customers. Tracking surveys measuring the effectiveness of communications efforts developed from the research generally followed these studies.

Developed communications programs designed to enhance the overall corporate image. This experience includes the development of programs and messages based on market segmentation and tracking surveys that measure the impact of the communication program.

Cluster Analysis

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

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.

Consider an example of a data set that includes preference ratings for a large number of specific foods such as bread, apples, blueberry pie, cereal, spare ribs, green beans, watermelon, chocolate mousse, pasta, ham, asparagus, artichoke, and roast beef. A factor analysis of this data set would distill this large amount of information by identifying the dimensions that underlay the individual ratings.

More specifically, dimensions are identified for groups of variables that are highly intercorrelated with each other, but are not highly correlated with variables outside of that group. In this data set, for example, we might expect a fairly high association among preferences for cereal, pasta and bread — that is, in general, the higher an individual’s preference for bread is, the higher we might expect his preference would be for pasta and cereal. However, knowing an individual’s preference rating for cereal, pasta, and bread would probably not be useful in predicting that individual’s preference rating for any other food on the list. If this were the case, these three variables would be represented by a factor that might be called the “starch” factor. Cereal, bread, and pasta would then be said to “load” on the “starch” factor. Besides the “starch” factor, other factors likely to emerge in this example include “fresh fruits,” “deserts,” “green vegetables,” and “beef.”

When a small number of factors account for most of the variance in the original set of data, we can “explain” the original set of variables in terms of a smaller set of factors without losing important information. Simplifying large sets of market research data in this way provides a conceptual clarity that facilitates one’s comprehension of the data and its strategic marketing implications.

Advertising Tracking

The study design and analysis centers around the array of powerful diagnostic tools utilized in our proprietary approach to continuous tracking.

Our experience has been honed with considerable analysis of previous campaigns in a variety of market sectors—successful campaigns and campaigns that are flawed. By comparing the good campaigns with the less-successful campaigns, we have arrived at views of how to look at advertising in a certain way.

Frost & Sullivan seeks to answer these fundamental questions:

  •     Has my advertising worked?
  •     How well has it worked? (better or worse than competition)
  •     Can it be further improved? How?

These are simple questions and are answerable by utilizing our approach.

In research terms we could express these objectives as:

  • To evaluate the efficiency of media expenditure in being noticed and linked to the brand
  • To track the image of the brands relative to competitors; to evaluate perceptions of strengths and weaknesses of the brand
  • Through the use of the research data, give recommendations on how to improve the performance of the advertising and media expenditure

Neural Networks

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.  Since neural networks are non-linear, they can find patterns of any form — linear, logarithmic, exponential, sigmoidal, sinusoidal.   

Neural networks rely on a validation dataset to avoid the problem of “overtraining” a model that will not extrapolate well to similar data.  They have generalization ability — they can correctly process data that only broadly resemble the data on which a model is built. 

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.  This adjustment process continues until the network produces output sufficiently close to the actual output.  

Neural networks can take two forms:

  1. Supervised networks – Similar to regression analysis or discriminant analysis. The input dataset consists of records with known outcomes (i.e., the targeted respondent purchases product or does not purchase product).  The neural net produces predicted output values that it compares with the known outcomes.  The trained model can be saved and used to predict purchase behavior of future respondents.
  2. Unsupervised networks – Similar to cluster analysis or factor analysis. The input dataset does not have known outcomes.  The neural net will group input patterns together. Useful in identifying similar segments in a market.

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