When companies invest in advanced market research tools like cluster analysis, they uncover strategic insights to improve everything from company culture to customer satisfaction.
And once these insights have been discovered, they can then be used to create a targeted business strategy based on data.
In this post, we’ll cover the meaning of cluster analysis, how it works, and when you should use it. Keep reading!
What Is Cluster Analysis?
Cluster analysis is an advanced statistical technique used to identify distinct subgroups within a data set.
When using cluster analysis, the main goal is to create categories in which the members of those groups are as similar as possible to each other, while at the same time maintaining their own unique differences.
When to Use Cluster Analysis in Market Research
Cluster analysis can be used in a variety of ways. Below, we’ll briefly describe popular ways it can be used to enhance business efforts.
Market Segmentation
Market segmentation is probably one of the most common uses of cluster analysis.
This application helps businesses break down their target audience into unique segments. Once that has been done, you can craft more effective messaging in advertisements and marketing.
As a result, this will ensure that the messaging resonates more with the right target market. This can lead to stronger customer bonds, promoting loyalty and retention.
Concept or Exploratory Data
Let's say you have a big data set that includes hidden patterns.
Cluster analysis is ideal for trying to find those underlying connections within data, shedding more light on how respondents answer certain questions similarly. After that, clusters can be designed using that information.
This may raise some new questions leading to further research, or maybe even give you some interesting insights to better understand your business.
Managing Resources
Cluster analysis can help teams manage important aspects of their business.
For instance, it can be used to improve the employee experience by helping employers assess employee needs, preferences and behaviors. In doing this, you’ll have a better understanding of how employees feel about your company and how things can be improved.
Additionally, cluster analysis can also help your business in the following ways:
- Streamline project management goals
- Software allocation within teams
- Build teams based on specific strengths
How Does Cluster Analysis Work?
The purpose of cluster analysis is always to create separate groups within data that are both similar and distinct.
After the clusters have been created, it's then key that they remain actionable. Meaning, they need to align with the goals of the client to ultimately be useful.
Let's say customer data is being segmented. The clusters should accurately represent differences that can ultimately enhance marketing and other related strategies.
Lastly, the clusters need to be thoroughly analyzed after they've been created. Ensuring that they align with key goals and that they are useful in practice is essential.
During this stage, think about the following...
- Do the clusters align with my needs/goals?
- Will the clusters be effective?
Essentially, effort needs to be put in upfront to get the clusters right.
How to Conduct Cluster Analysis
Conducting cluster analysis successfully involves a few key steps. From setting initial goals to carefully analyzing the final product, each step ensures the process runs smoothly.
Set Goals & Objectives
When conducting cluster analysis, always start by clearly defining the objectives. Going into the process blind will not only make each step difficult, but it will likely lead to disorganization throughout.
Take this example: You're working with a group of customers. A good objective to have would be creating clusters specifically to develop more targeted marketing materials.
Lastly, don’t assume that just because a technique is sophisticated it's the right choice for you. Cluster analysis is all about finding the right fit for your specific needs, regardless of how impressive it may sound on paper.
Determine the Project Approach
After the key goals have been established, an approach to conducting cluster analysis needs to be solidified.
First, consider how you want to measure the distance between the data points. These different data points are known as cluster centers, or centroids. Often, the method used to do this is called Euclidean distance.
This method calculates the distance from a data point to the center of its cluster. The simplicity (and accuracy) of this method has made it a popular choice when conducting cluster analysis.
Now, it's time to pick the type of clustering algorithm to use.
Common algorithms include...
- K-means clustering
- Hierarchical clustering
- Density-based clustering
K-means is the most commonly chosen, as it's simpler to use and the results are often easier to understand.
Run the Numbers
Next, the algorithm is run using the chosen distance metric.
In the example below, we'll be referring to the K-means approach.
Stage 1
Here, you begin by randomly selecting starting points for each cluster within your data set. Imagine a large, scattered map of all the data points that you're including and you choose to create four clusters.
Then, you assign each data point in the set to the closest starting point. This will provide the initial cluster set, with each cluster containing the data points closest to its starting point.
Stage 2
At this point, the clusters are not very accurate or helpful - think of it as a first draft. In order to refine them, the mean (AKA the centroid) of each cluster is calculated. Now, these centroids are the new center points of the clusters.
Stage 3
After that, you reassign data points to the nearest centroid, repeating the process.
The clusters will then continuously become refined, making them better aligned with the underlying data structure. This process continues until the clusters stabilize, revealing a more accurate representation of your data.
Create the Clusters
Now, you'll continue to repeat the steps we listed in the last section. This will create multiple iterations of cluster centers. By repeating the algorithm, you'll be able to see what data points are closest to the center points.
The distances will then be measured with new data points being assigned to the clusters, creating new cluster centers. As more iterations are run, the centers will shift over time and eventually settle.
The algorithm will ultimately determine the best position for each cluster center, with the data points assigned to clusters.
Analyze the Results
Lastly, once the clusters have settled, the rest of the data from the set can be analyzed - think of it as using the clusters as data cuts.
For example, when using customer survey data, details like demographics, geography, and purchasing habits can be gleaned. This data can then be used to improve messaging techniques and provide a better experience for customers.
Contact Our Full-Service Market Research Firm
With over 80 years of combined market research experience, our team is well-versed in conducting cluster analysis. We're ready to work with you to provide the quality data you need to improve your business strategy.
To learn more about our market research services, get in touch with us today!
- Message us on our website
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- Call us at 888-725-DATA
- Text us at 315-303-2040
Tim Gell
As a Senior Research Analyst, Tim is involved in every stage of a market research project for our clients. He first developed an interest in market research while studying at Binghamton University based on its marriage of business, statistics, and psychology.
Learn more about Tim, here.
