Data Analysis: Customer Distribution with a Geospatial Heatmap Using Python

 Visualizing Customer Distribution with a Geospatial Heatmap

In today's data-driven world, understanding your customers' geographical distribution is essential for making informed business decisions. Visualizing this information can provide invaluable clarity, whether you're optimizing your delivery routes, targeting specific markets for promotions, or just gaining insights into your customer base.

In this post, I'll walk you through how I used geospatial heatmaps to visualize customer locations based on data from the Fecom Inc. E-commerce Marketplace Orders Data CRM dataset available on Kaggle.

What is a Heatmap?

A heatmap is a data visualization technique that uses color gradients to represent values in a two-dimensional space. In this case, we're using a heatmap to show the concentration of customer locations worldwide. Areas with higher concentrations of customers will be highlighted in warmer colors, making it easy to spot where most of your customers are located.

The Data

The dataset I used comes from Fecom Inc.'s e-commerce marketplace, containing customer order details and associated CRM data. It includes information about customer locations such as city and country. This made it a perfect fit for our analysis. If you're interested in trying out the analysis yourself, you can download the dataset from Kaggle here.

The Process

1. Data Preparation: First, I loaded the dataset and extracted unique city-country combinations to create a list of customer locations.

2. Geocoding: Using the geopy library, I converted the city and country names into geographical coordinates (latitude and longitude). This step is crucial because mapping customer locations requires precise coordinates.

3. Creating the Map: I used the folium library to generate an interactive map, centered around the average latitude and longitude of all customer locations. Markers were added for each customer, showing their city and country.

4. Adding the Heatmap: To get a sense of where most of the customers are concentrated, I overlaid a heatmap on top of the map. The heatmap visually represented areas with the highest customer density, making it easy to identify geographic clusters.

The Result

The final map is an interactive visualization that can be zoomed in and out to explore the global distribution of customers. The heatmap highlights the areas with the highest number of customers, providing a clear view of where the business is most popular.

Key Insights

• Customer Clusters: By looking at the heatmap, you can quickly identify the regions where most of your customers are located. For instance, larger cities or metropolitan areas will often appear as the most concentrated regions.
• Targeted Marketing: With this map, businesses can design more targeted marketing campaigns, focusing on areas with high customer density.
• Logistics Optimization: Understanding where your customers are located can also help in optimizing delivery routes, reducing shipping costs, and improving overall efficiency.

Conclusion

Heatmaps provide a powerful way to visualize and analyze geographical data, allowing businesses to make data-backed decisions based on customer locations. By utilizing tools like folium and geopy in Python, you can create interactive maps that bring your data to life.

I hope this post inspires you to explore how geospatial data can enhance your analyses. Whether you're working with customer data, logistics, or even crime statistics, heatmaps offer a clear, insightful way to identify patterns that would otherwise be hard to spot.

If you're interested in trying out the code yourself, feel free to download the dataset and follow along with the steps provided.