Tour Operators and the use of cluster analysis

The goal of cluster analysis is to classify subjects as customers in relatively homogeneous groups, or clusters. Each customer can be placed in only one group, and there is no overlap between the clusters. Good segmentation solutions are accessible, actionable, differentiable, measurable, and substantial. (Ed peelen & Rob Beltman, 2013)

Cluster analysis is used in market research which is beneficial for Tour Operators. It is used to divide customers into market segments and to better understand the relationships between different groups of customers. The goals of cluster analysis is to identify groups, before starting you don’t know who belongs in which group and often you don’t even know the number of groups. (Marija J. Norusis, 2009)

When performing a cluster analysis you start with you customer database and subdivide these into homogeneous groups. First you choose the segmentation variables for your clusters or groups. Next, you must decide whether to standardize the variables in some way so that they all contribute equally to the distance or similarity between customers. After you decide which cluster procedure is best to use. There are many cluster algorithms, you choose a clustering procedure based on the number of customers and types of variables you want to use for forming clusters. As well you can look at how many clusters you need to represent your data. You do this by looking at how similar clusters are when you create additional clusters or collapse existing ones. A commonly used clustering procedure is the K-means. In K-means clustering, you select the number of clusters you want. The algorithm iteratively estimates the cluster means and assigns each case to the cluster for which its distance to the cluster mean is the smallest. (Marija J. Norusis, 2009)

The K-means procedure is explained by using a Tour Operator as an example. When performing the K-means cluster analysis, first the number of groups will be distinguished. For this Tour Operator we use two clusters or segments. The first cluster is called group A and the second Group B. The Tour Operator chooses segmentation variables for the two groups. Next, a computer draws two observations when looking at the customer database. By Using the customer database, you may be able to form clusters of customers who have similar buying habits or demographics. Individual subjects are assigned to the group which is closest to them in terms of distance. After, the average scores for the segmentation criteria are calculated for the two groups. A new group classification is then formed on the basis of the cluster averages, and objects are allocated to the clusters for which the distance to the cluster average is the smallest. After finishing this cluster analysis, Tour Operators can divide their holiday offers between clusters and provide them with offers that are most receptive to them. By knowing these customer habits, you can for example send your customers and email with the most appealing holiday offers for them. After doing this you can also make a cluster analysis by looking at your customer’s response patterns. (Ed Peelen & Rob Beltman, 2013)

Overall we can conclude that the use of cluster analysis is an important tool for Tour Operators to determine customer segments and target them accordingly. A cluster analysis can provide a Tour Operator with valuable information to make the Tour Operator’s business more profitable by using their insights in different clusters.


Ed Peelen & Rob beltman. 2013. Customer Relationship Management. United Kingdom: Pearson Education Limited.

Marija J. Norusis, 2015. SPSS 16.0 Statistical Procedures Companion. Cluster Analysis. Retrieved from:

Figure: Chire, 2010. Different cluster analysis results on ”mouse” data set. Retrieved from:


One thought on “Tour Operators and the use of cluster analysis”

  1. Does the touroperator choose the variables with an iterative approach or does the analysis program tell the touroperator what the clusters are? How does this work?
    Can you give an example of clusters that may result from such an analysis?


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