How To
Feb 3, 2023

Customer Segmentation: How To & Examples

No matter their size or industry, all businesses at some point wonder – who exactly is their customer? Customer segmentation is a strategy that they use to answer that question. Customer segmentation, as you know, involves dividing a customer base into smaller groups of individuals that have similar needs or characteristics.

With segmentation, businesses are 60% more likely to understand customers’ challenges. But for a long, the strategy was only used by large enterprises simply because it needed huge datasets with customer details, investment in analytical tools, a massive marketing budget, and skilled personnel.

But that is no longer the case. Today even small businesses can segment their customers more effectively as technology advances, particularly in data collection and analytics. So at Gigasheet, we have developed a no-code data analytics solution tailored for non-tech users like small business owners and solopreneurs.

With Gigasheet, you can process and analyze huge datasets without hassle, understand your customers, optimize your marketing efforts, and compete on par with large businesses. In this blog, let us explore how customer segmentation is made easy with Gigasheet.

Customer Segment Examples Dataset

For our analysis, we are using a huge dataset with records of 51,000 customers. The data set include the following details about the customers:

First Name, Last Name, Title, Gender, Email, City, Country, Country Code, Latitude, Longitude, Phone, Street Address, Street Name, Street Number, and so on.

To analyze customer data, we will head to, login, and upload our file. If you do not have a Gigasheet account, you can create one here for free.

We will be working with a CSV file, but you can also upload datasets in formats like JSON, XLSX, LOG, ZIP, and more, and Gigasheet will process and organize your data in rows and columns.

Now, let's analyze customer data and segment our customers.

Segment 1: Which Countries Have the Most Customers?

Let's begin with some basic analysis of our customer dataset. First, we will find the total number of customers in each country. To do so, we will head to the country column, click on the header row to bring up the options menu, and select group. And that's it.

Customer Segmentation Examples using Gigasheet's Group feature.

Alternatively, we can also group rows by columns from the Group options in the menu bar. We can go ahead and click on any country's group and expand it to analyze customer data further.

Snapshot of Customer Segmentation by Country in Gigasheet.

Now, let's visualize the customer count for the top 10 countries with the most customers. First, we will need row count, which in our case equals the total number of customers. For row count, we will head to the bottom of the first_name column and select row count.

Gigasheet's aggregation options to complete customer segmentation

Then, we will click on the header row of the same column and sort rows in descending order. And we will have top countries on our screen.

Sorting data for a column is a one-click operation in Gigasheet.

We will select rows in the first_name column with row count, right-click, and Chart Range > Bar > Grouped.

Snapshot of Gigasheet's data visualization options for customer segmentation examples
Snapshot of a bar graph in Gigasheet showing a customer segment examples

Segment 2: What is the Gender of Customers in Different Cities?

Now, let us dive a bit deeper into our customer dataset and analyze how customers of different gender groups are divided between cities. For this round, we will analyze customer data for American cities. So, we will head to the Filter option in the menu bar and apply the following filter.

Snapshot of Gigasheet's filter option to help create customer segmentation example

Next, we will head over to the Group option and first group data by city column. Then, we will create a group within the city groups by adding another column, gender, as shown below.

Group by option allows users to create pivot tables.

Now, let us visualize data for the following cities – Washington, Houston, Chicago, Denver, L.A., and Memphis. Simply get the row count for first_name column, select rows for these cities, right-click, and Chart Range > Column > Grouped. There we have a visual representation of customer segmentation by gender in six American cities.

Column graph in Gigasheet showing customer segment by city

Segment 3: How Many Customers Work as Marketing Managers and What Is Their Location?

Our huge dataset with customer segmentation data also includes the designation of each customer under the job_title column. So, let us analyze customer data to see how many Marketing Managers we have as our customers across the globe. We will also visualize our findings with an interactive scatter graph.

First, let's filter the customer dataset by where the entry in the job_title column equals Marketing Manager, as shown below.

Snapshot of Gigasheet's filter option creating customer segment

We have a total of 462 Marketing Managers as customers across the world. To visualize their location on a graph, we select the following columns: country, longitude, and latitude. Then, hit right-click, and choose Scatter X/Y > Scatter. The resultant scatter graph looks like an imprint of a world map, and when you hover your mouse over each point, it displays the country the Marketing Managers reside in.

Snapshot of a scatter graph in Gigasheet.

Alternatively, we can replace the country column with the city column to display the exact city that Marketing Managers reside in. Simply open the Data section in the graph pop-up window and select the radio button for the city column.

Snapshot of a scatter graph and graph settings in Gigasheet.

Wrapping Up Customer Segmentation

In 2023, if you are not trying to know your customers as a small business owner, you are leaving success on the table. Customer segmentation is essential to divide your customers into smaller groups with overlapping characteristics and grow your business with targeted marketing efforts. So, jump on the Gigasheet bandwagon, and explore huge datasets with our no-code, no-database, and no-nonsense data analytics yourself.

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