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Value Cluster Analysis is a technique used for classifying objects into groups. This can be used to sort data (a number of people, companies, cities, brands or any other objects) into homogeneous groups based on their characteristics.
The result of Value Cluster Analysis is a grouping of the data into groups called clusters. The researcher can then analyze the clusters for their characteristics and give the clusters names based on these. Ideally, the mean values of variables used to form the clusters should be used to define them.
The marketing application of value cluster analysis is in customer segmentation and estimation of segment sizes. Industries where this technique is useful include automobiles, retail stores, insurance, B-to-B, durables and packaged goods. Some of the well-known frameworks in consumer behavior (like VALS) are based on value cluster analysis.
Value Cluster Analysis is applicable when:
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There are two ways in which Value Cluster Analysis can be carried out:
This is the basic approach used in cluster analysis. This can be used to segment customer groups for a brand or product category, or to segment retail stores into similar groups based on selected variables.
Ideally, the variables should be measured on an interval or ratio scale. This is because the clustering techniques use a distance measure to find the closest objects to group into a cluster. An example of its use can be clustering similar locations across the USA based on various demographic characteristics like average income, number of Housing Starts (an economic indicator that tracks how many new single-family homes or buildings were constructed throughout the month), the number of people in an age group or income group, the number of sports goods shops, etc. Clusters of towns similar to each other can help decide where to locate new retail stores.
If clusters of customers are found based on their attitudes towards new products and interest in different kinds of activities, an estimate of the segment size for each segment of the population can be obtained, by looking at the number of objects in each cluster. We use the Case listing of Cluster Membership for the chosen solution to do this.
Names can also be given to clusters to describe each one. For example, there can be a cluster called "the liberal new-agers". Segments are prioritized based on their estimated size.
Marketing strategies for each segment are fine-tuned based on the segment characteristics. For instance, a segment of customers that likes outdoor sports can receive a special promotional offer of a golf weekend at a resort.
Data is only as useful as its interpretation. Let O2I's experienced researchers strengthen your marketing strategy with cluster analysis.
First, we collect data on the objects to be clustered. Then, we create a dataset of all objects to be clustered; we use SPSS (Statistical Package for the Social Sciences) package options of either Hierarchical Cluster or K Means Cluster. This choice depends on whether clusters are unknown in number or known in number.
In case of K Means Cluster, we type the number of clusters needed, we find the final cluster centers in the output and interpret the clusters.
If cluster numbers are unknown, we look at the agglomeration schedule (requested in the Statistics dialogue box of the Hierarchical Clustering procedure) to determine the number of clusters. We look for large gaps in the Coefficient column, and choose the corresponding solution. The last row represents a solution with 1 cluster, the one above that represents a solution with 2 clusters, and so on. Once the number of clusters is determined, average values for each variable are determined for all the clusters, and interpretation of clusters follows. The estimate of the segment size depends on the number of objects falling into each cluster. The stability of the clusters is checked by splitting the sample and repeating the value cluster analysis.
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