Outsourcing Value Cluster Analysis to O2I
Are you looking for a specific customer segment for your brand? You can use our Value Cluster Analysis services to zero in on your targeted market. Value cluster analysis is a research technique that is 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. Researchers can then analyze the clusters for their characteristics and give the clusters appropriate names based on the results. Ideally, the mean values of the variables used to form the clusters should also be used to define them.
Where can Value Cluster Analysis be applied?
The marketing application of value cluster analysis is in customer segmentation and in the estimation of segment sizes. Industries where this technique is useful include automobiles, retail stores, insurance, B2B, durables and packaged goods. Some of the well-known frameworks in consumer behavior like VALS are based on value cluster analysis.
The following are some examples where value cluster analysis can be used -
- An FMCG company wants to map the profile of its target audience in terms of lifestyle, attitude and perceptions
- A consumer durable company wants to know the features and services a customer considers when purchasing through products by using catalogs
- A housing finance corporation wants to identify and cluster the basic characteristics, lifestyles and mind sets of persons who would be prone to taking housing loans
Our Cluster Analysis Process
There are two ways in which value cluster analysis can be carried out -
- In the first method, objects/respondents are segmented into a pre-decided number of clusters. In this case, a method called non-hierarchical or k-means clustering can be used, which will partition the data into the specified number of clusters. Then, the clusters will be analyzed based on the mean values of the variables used for clustering
- The second way in which cluster analysis can be used is to find all the possible solutions from a 1-cluster solution to a (n-1) cluster solution (n is the number of objects to be clustered), and then narrow it down to a possible solution with a certain number of clusters. In this case, a hierarchical method is used and is based on either the agglomeration schedule or the dendrogram model. The desired number of clusters will then be fixed. After this, the clusters will be analyzed based on the mean values of the variables used for clustering, as in the first step.
These are the two basic approaches 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.
How are the Results of Cluster Analysis Interpreted?
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 the number of new single-family homes or buildings that 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 in deciding 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. At Outsource2india, we use the Case listing of Cluster Membership for the chosen solution to carry out such estimations.
Appropriate names can also be given to clusters to describe each one. For example, there can be a cluster called "the liberal new-agers". The segments can be prioritized based on their estimated size. The 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.
Outsource2india's Approach to Cluster Analysis
First, we will collect the data on the objects to be clustered. Then, we will create a dataset of all the objects to be clustered. To carry out the cluster analysis, we use the SPSS (Statistical Package for the Social Sciences) package options of either Hierarchical Cluster or K Means Cluster. This choice depends on whether the clusters are unknown in number or known in number. In case of K Means Cluster, we will type the number of clusters needed, find the final cluster centers in the output and then interpret the clusters.
If the cluster numbers are unknown, we will look at the agglomeration schedule (requested in the Statistics dialogue box of the Hierarchical Clustering procedure) to determine the number of clusters. We will 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, the average values for each variable are determined for all the clusters, and interpretation of the clusters will follow. 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 process.
Did you know that your data will be more useful if you interpret it? With cluster analysis services from Outsource2india, you can strengthen your marketing strategy and understand the needs of your customers. Contact Outsource2india now to outsource cluster analysis and give your business a head-start.
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