The Difference Between Segmentation And Clustering
Behind every successful person is a significant amount of coffee drunk and a large amount of processed information. Therefore, we invite you to drink coffee and read our new article on the difference between clustering and segmentation.
Previously, it seemed to us that there was no point in writing a blog about clustering vs. segmentation. But you know what? It turns out that there is a lot of information on the Internet about this, but it is entirely incomprehensible, plain, and does not show the real difference. Let's find the difference between clustering and segmentation finally. Attention. Today's blog will be somewhat different from the previous by style. In parallel with important information, we will demonstrate everything with an example.
What is the difference between segmentation and clustering?
All analytics specialists who work with data are familiar with clustering and customer data segmentation concepts. However, how do they differ, where they are used, and what benefits do they bring?
These two terms are often taken almost as synonyms, but this is not true. Clustering is more about a statistical perspective while segmentation is about a business perspective. Imagine a car and the parts that make it up. So, we can segment the car but cluster the elements from which it is created. Still confused? Then keep reading.
What is segmentation?
The segmentation data process is putting customers into a similar group based on similarities. That is, you know who to target. For instance, we target men and women with high annual incomes if we're selling Porsche cars. We know for sure that this group of people can afford to buy such precious cars.
Customer segmentation uses a mathematical model to identify similar customer groups based on significant commonalities. This approach allows you to create something like a "customer archetype" or a "customer portrait."
Segmentation is the selection of the buyer's group in this example. This is useful because, through segmentation, we have chosen the part of customers who will be able to pay for our precious product. Accordingly, based on this data, we will be able to better and more purposefully build a marketing strategy.
But the selected group of payable persons is still significant, and it needs to be reduced. After all, it is not a fact that all rich people will buy Porsche. Some of them may prefer Ferrari or other luxury car brands. Therefore, we add one more dimension to reduce our sample. For example, let's take an age. According to statistics, women and men start buying Porsches at 50years old. Therefore, our next step will be to weed out all buyers under the age of forty. We continue to further reduce the segment of buyers. What if we add a location? Again, according to statistics, most of our Porsches customers are located in Asia-Pacific, Africa, Middle East countries, and China. Let's remove women and men who are not from this territory. We do all this until we get one single segment.
To get this the holy grail segment, you need to do thousands of screenings and go through hundreds of data. That's where clustering comes in handy.
The advantages of segmentation
Customer segmentation helps to optimize the marketing strategy for the needs of a particular group. More often customers are segmented by:
- typical buying behaviour
Segmentation works to improve your company's revenue, customer experience, and a better understanding of who you're selling to and what customers expect from you. Without knowing target odds, you will only be wasting money on marketing.
The use of customer segmentation and clustering tactics makes it easier to find and attract new customers. You won't waste your time and money. In addition, you can increase the conversion based on the data received. Once you get to know your customers, you can make an emotional connection and encourage them to buy from you.
What is clustering?
First of all, let's understand the definition of clustering. Clustering is the process of finding commonalities based on relationships and creating new segments of customers by using machine learning and algorithms. That is, clustering is aimed at finding relationships between different groups and discovering a new segment. To make it clearer, let's go back to our Porsche example.
You can then create a mockup to see how many people are buying your expensive cars, at what age, and when. Drupal eCommerce development agency helps you consider even more data options and dimensions. For example, we noticed a trend that our cars are most often bought before the New Year holidays, buyers are on average 45 years old and most of them are from China. We focus on 3 dimensions. All these buyers bought the car, which means it works.
Top benefits of cluster analysis
Of course, there are more than three reasons why you should use clustering in marketing. Let's focus our attention on the main ones.
- Cluster analysis is convenient when you do not have facts that could be used as a basis for research.
- This method allows you to open structures in data without additional details about why these connections exist and so on.
- Marketers often use clustering to develop market segments, and it is better to edit their market to fit their needs.
Often we do not even notice how clustering simplifies our lives. What does cluster mean? A cluster can even be a group of friends in a cafe or similar products in a supermarket, located side by side on a shelf.
Need Help? – Consult the Golems team to implement segmentation and clustering!
The better you know your customer, the more income you get. Customer segmentation and clustering will be helpful in any business, regardless of the field. You can use this in banking, e-commerce, education, marketing, etc.
Develop your brand constantly to stay in the top positions. Implement clustering or segmentation. Our team has experienced analysts and developers to help you make it. To learn more or contact us, visit the Drupal services page.
Thank you for reading and being with us! Your Golems team!