Cluster Analysis in Business Analytics

Businesses have a lot of unstructured data. According to statistics, almost 80% of companies’ data is unstructured. Also, the growth rate of unstructured data is 55-65% per year. Since this data cannot be arranged into a tabular form, it is difficult for enterprises, especially small businesses, to use unstructured data. This is why business analytics tools are becoming widely popular. Cluster analysis is a business analytics tool that helps companies sort unstructured data and use it for their maximum advantage.

This blog helps you understand what cluster analysis is in business analytics, its types, and applications.

What is Cluster Analysis?

Cluster means arranging or grouping similar items. Therefore, as the name suggests, cluster analysis is a statistical tool that classifies identical objects in different groups. Objects within a cluster have similar properties, whereas objects of two separate clusters are entirely different. Cluster analysis serves as a data mining or exploratory data tool in business analytics. It is used to identify similar patterns or trends and compare one set of data with another.

The cluster analysis tool is mainly used to segregate customers into different categories, figure out the target audience and potential leads, and understand customer traits. We can also understand cluster analysis as an automated segmentation technique that divides data into different groups based on their characteristics. It comes under the broad category of big data.

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What are the Different Types of Clustering Models?

There are broadly two types of clustering: hard and soft clustering. In hard clustering, each data point is definite and included only in one cluster. On the other hand, data points in soft clustering are arranged based on probability. We can fit one data point in different clusters in soft clustering. The following are the most popular types of clustering models in business analytics:

• Hierarchical:- The hierarchical clustering algorithm arranges the clusters in a hierarchy. It creates a tree of clusters. Then, the two closest clusters are arranged into one pair. This new pair is further combined with another pair.

For example, if there are eight clusters, the two clusters with maximum similar characteristics will be arranged together and form one branch. Similarly, the other six clusters will be arranged into a pair of three clusters. The four pairs of clusters will be brought together to form two pairs of clusters. The remaining two clusters will also be merged to form a head cluster. The clusters appear in the shape of a pyramid.

Hierarchical clustering is further divided into two different categories – agglomerative and divisive clustering. Agglomerative clustering is also called AGNES (Agglomerative Nesting) in which two similar clusters are merged at every step till one combined cluster is left. On the other hand, divisive hierarchical clustering, also called DIANA (Divise Analysis), contradicts AGNES. This algorithm divides one cluster into two clusters.

• K – Means:- The K-means cluster analysis model used predefined clusters. Using the K – means clustering algorithm is to find local maxima in each iteration. This algorithm keeps on calculating the centroid until it finds the correct centroid.
• Centroid:- Centroid is also an iterative clustering algorithm. It finds similarities between two clusters by calculating the closest distance between the data point and the centroid. Then, the centroid clustering algorithm is used to find the local optima. The data points in this algorithm are predefined.
• Distribution:- This clustering algorithm is based on probability. It uses normal or Gaussian rules to find the probability between data points of one cluster. The data points are arranged in a cluster based on a hypothesis or a probability in the distribution model. However, this is an overfitting model. It means that we need to put some limitations while using the distribution algorithm.
• Density:- The density cluster algorithm searches the data space to arrange the data points with varying densities. This algorithm creates separate density regions based on different densities.

Benefits of Cluster Analysis

Here are the two most significant benefits of cluster analysis!

• Undirected Data Mining Technique:- Cluster analysis is an undirected or exploratory data mining technique. It means that one cannot form a hypothesis or predict the result of cluster analysis. Instead, it produces hidden patterns and structures from unstructured data. In simple terms, while performing cluster analysis, one does not have a target variable in mind. It produces unexpected results.
• Arranged Data for Other Algorithms:- Businesses use various analytics and machine learning tools. However, some analytics tools can only work if we provide structured data. We can use cluster analysis tools to arrange data into a meaningful form for analysis by machine learning software.

Cluster Analysis Applications

Businesses can use cluster analysis for the following purposes:

• Market Segmentation:- Cluster analysis helps businesses in market segmentation by creating groups of homogenous customers with the same behaviors. It is beneficial for businesses with a wide range of products and services and cater to a large audience. Cluster analysis helps businesses determine customer response to their products and services by arranging the customers with the same attributes in one cluster. This allows the businesses to organize their services and offer specific products to different groups.
• Understanding Consumer’s Behavior:- Cluster analysis is beneficial for companies to understand consumer behavior like their preferences, response to products or services, and purchasing patterns. This helps businesses to decide their marketing and sales strategies.
• Figuring Out New Market Opportunities:-  Businesses can also use cluster analysis to understand news trends in the market by analyzing consumer behavior. It can help them expand their business and explore new products and services. Cluster analysis can also help businesses figure out the strengths and weaknesses and their competitors.
• Reduction of Data:- It is difficult for businesses to manage and store tons of data. Cluster analysis helps businesses segregate valuable information into different clusters, making it easier for companies to differentiate between valuable and redundant data that can be discarded.

How to perform Cluster Analysis?

Each cluster analysis model requires a different strategy. However, the following steps can be used for all cluster analysis techniques.

• Collect Unstructured Data:- You can perform cluster analysis on existing customer data. However, you will need to collect fresh information if you wish to understand recent trends or consumer traits. You can conduct a survey to learn about new market developments.
• Selecting the right variable:- We begin cluster analysis by choosing a variable or a property based on which we can segregate one data point from another. It helps narrow down the property based on which clusters will be formed.
• Data scaling:- The next step is to scale the data into different categories. It means categorizing data based on the selected variables.
• Distance Calculation:- The last step of cluster analysis is calculating the distance between variables. Since the data points are arranged into clusters with different factors, we need to prepare an equation considering all the variables. One of the most simple ways is to calculate the distance between the centers of two clusters.

Conclusion

Cluster analysis is a popular business analytics tool that helps convert unstructured data into usable formats. As companies are collecting increasing amounts of data every passing year, it becomes necessary for them to use data for meaningful purposes. Therefore, cluster analysis jobs are expected to grow by multiple folds in the coming years. According to statistics, the average salary of a cluster manager in the US is $79,109. On the other hand, the average salary of a data analyst in the US is $65,217.

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