People seek approximation based on similarity, especially when it comes to business statistics. With data, the process is much more precise, what we call clustering.
Think of a puzzle, the fastest way to begin is to separate the pieces by color, shape, and edges. This is what we call a conscious or non-organizing strategy.
For companies, organization and categorization strategies are fundamental to success. Clustering information can make all the difference.
Stay with us and find out what clustering is, its importance, as well as the different types of clustering.
What is clustering?
The term clustering is used to refer to the act of grouping information, which can be data from customers, products, and computers, among others.
Separating into groups, categorizing, and segmenting is a way of gathering information or data based on common characteristics.
Clustering is the way that professionals from different areas group different data into similar categories.
An example are developers who, when analyzing the data architecture of a retail store, organize stock information by merchandise, price, size, volume.
Marketing analysts, when creating a digital ad campaign, can separate data by gender, interests, income, places visited, and consumption affinity. The universe of information is vast, but clustering can help take you where you need to go.
Data Science: the new agenda for rethinking business models
Why is clustering important?
Clustering is a process that has enormous applicability. It can efficiently address diverse problems and objectives, from the simplest to the most complex.
For example, in-depth data analysis is essential for segmentation. The best way to get the most out of your clusters is to make sure the surveying is done carefully and in line with the company’s objectives.
For this, clusters need three essential steps:
- Identification: Know what you are dealing with. Identifying is like putting all the pieces on the table, mapping out the situation, and sorting them using patterns.
- Analysis: Analyze these patterns to make your clusters more focused and accurate.
- Strategy: Create differentiated strategies for each of the clusters, with specific objectives, actions, and goals.
Three types of clustering per application
Client Clustering is the technique of organizing individuals according to their behavior, habits, preferences, values, social class, geographic information, and demographics, among others.
In a practical way, this technique goes beyond segmentation, because it considers more intangible variables in the group organization framework, such as mindset, opinions, and context.
It is important to know that data analysis tools are the basis of customer clustering. Getting data from various digital platforms also makes it easier to identify patterns like common interests.
Through clustering, companies optimize the quality of the messages they send to the public, such as product promotions with more acquisition power or an after-sales service in line with the previous purchase. This strengthens customer relations and consequently increases sales.
When it comes to retail and e-commerce, products are the heart and soul of the business. A major challenge for this sector is how to be more assertive in the distribution of products.
For this to work, one of the most important techniques is the establishment of clusters.
Based on clustered data, either quantitative or qualitative (which take into account information such as: average income in the region, climate, consumption habits, age, tourist profile, among others), it is possible to predict which clothes will sell the best at each franchise. This allows logistics to send a customized shipment to each store to maximize sales.
This data is also complemented with historical information on sales in these regions; Information such as store size, average stock, and sales figures per season, among others.
In the end, clustering ensures more efficiency for retail chains and a better positioning of each unit based on its context.
Data Clustering has more to do with operations. It’s how data is organized into clusters for archiving. This allows future queries to be answered faster, giving employees fast and easy access to data whenever they need it.
One of the many jobs of data analysts is summarizing collected information.
A very useful method is to create clusters, especially when there are a large number of observations. This can also help if the data within sets are similar to one another while at the same time being vastly different from the information in other sets,
This clustering can be applied in two ways:
- Hierarchical: when the cluster’s main characteristic is an algorithm capable of providing more than one type of data partition. It generates several possible clusters, where one cluster can be merged with another.
- Non-Hierarchical: non-hierarchical cluster analysis methods are characterized by the need to define an initial partition. They offer flexibility since the elements can be swapped between groups during the algorithm execution.
A Step towards Clustering
The most enticing aspect of clustering is the fact that almost any operation can benefit from it.
Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities.
Now that you know what clustering is and what types of clustering are possible, it’s time to put this strategy into practice!