Harnessing AI to Help Coworking Operators Understand Their Communities Better

Will AI change how coworking operators interact with their community?

After more than twelve years in the coworking industry, we’ve seen the movement evolve at an incredible pace, especially in recent years. Spaces have grown larger, making it harder for operators to truly connect with their communities. At the same time, expectations for higher service standards and increasing competition mean that creating tailored experiences and fostering long-term member engagement is more important than ever.

From the very start of the coworking movement, one thing has always been true: the better operators know their communities, the better they can help them thrive. And today, that’s more important than ever.

The Challenge: Diverse Operators, Fragmented Data

Coworking spaces are as diverse as the people who use them. Some operators meticulously track detailed data about their members, while others collect only the essentials. This variation creates a challenge: how do we build an AI-driven segmentation tool that works effectively for all operators, regardless of the quality or completeness of their data?

When we started this project, we wanted to develop an intelligent model that could provide meaningful insights to operators managing spaces of any size, regardless of whether their data was sparse or inconsistent. The idea was that anyone using this module would see its value and be able to act on the insights provided.

That said, it’s important to remember that the more structured and complete the data, the more value you can get from the model.

The Data That Drives AI-Powered Segmentation

At Nexudus, we’re taking a big step forward with AI-powered customer segmentation in our coworking management software. With this new feature, we aim to give coworking operators a powerful tool to gain deeper insights into their communities, making it easier to personalize experiences, streamline operations, and scale their spaces.

To build a segmentation model that truly works, we focused on key behavioral and transactional patterns, including:

• Membership Type – Whether members use virtual offices, dedicated desks, hot desks, private offices, or part-time memberships.
• Financial Interactions – Payment history, membership plans, and spending on extras like meeting rooms, food, parking, or printing.
• Engagement Metrics – How often members visit, how long they stay, and which amenities they use.
• Demographics & Team Dynamics – Team size, individual vs. group work habits.
• Contract Duration – How long a member has been part of the space.

By analyzing these factors, we’ve created a flexible yet powerful model that helps coworking operators understand their community on a deeper level. Best of all, our software ensures these insights are easy to access and act on—regardless of how much or how little data an operator collects.

Why AI? And Why These Specific Techniques?

Traditional segmentation methods struggle to handle complex, high-dimensional data. AI, on the other hand, excels at finding hidden patterns in large datasets. We chose two specific AI techniques to power our segmentation model:

• UMAP (Uniform Manifold Approximation and Projection): A dimensionality reduction method that simplifies high-dimensional data into 2D or 3D while preserving the relationships between data points. This helps us visualize and analyze member data without losing the essence of their behaviors and interactions.
• HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise):  A clustering algorithm that automatically identifies groups of similar members based on their behaviors, even when the data is noisy or incomplete. Unlike traditional methods, HDBSCAN does not require us to specify the number of clusters in advance, making it ideal for our diverse operator base.

These techniques work together to identify meaningful trends and member groups, ensuring that even operators with limited data can extract valuable insights using our coworking management software.

Overcoming the Biggest Challenge: Data Quality and Consistency

The biggest challenge in this project was managing the fragmented and inconsistent data across different operators. Some operators kept detailed and consistent records, while others had varying amounts of data depending on their operational maturity.

We tackled this challenge by carefully selecting the most relevant features for segmentation. Our AI models were designed to handle missing data gracefully, ensuring that even operators with limited data could still benefit from actionable insights. This process taught us the importance of balancing data quality with the need for broad applicability across our customer base.

Surprising Insights: Patterns Hidden in Plain Sight

One of the most exciting outcomes of this project was how well our AI techniques worked, even with a reduced set of features. The models successfully identified distinct member cohorts, reflecting the diverse audiences each operator caters to. From digital nomads using hot desks to corporate teams in private offices, the AI uncovered patterns that help operators better serve each group’s unique needs.

The Impact: Empowering Operators to Thrive

With AI-powered segmentation embedded now in our coworking software, operators are able to:

• Enhance Customer Satisfaction: Deliver personalized services and amenities that resonate with each member segment.
• Unlock Revenue Opportunities: Implement targeted upselling and cross-selling strategies based on member behaviors.
• Operations: Allocate resources more effectively by understanding member preferences and usage patterns.

By providing operators with these insights, Nexudus helps them build stronger, more meaningful relationships with their members, driving both satisfaction and sustainable growth.

A Learning Journey: Building AI in the Coworking Industry

This project was not just about building an AI solution; it was a journey of learning and discovery. Our team gained valuable insights into how AI can be leveraged in the coworking industry, and, along the way, we also deepened our understanding of our own customers. This experience has been both revealing and rewarding, showing us the immense potential of AI in enhancing coworking operations.

Data Privacy: A Core Priority

Throughout this project, data privacy remained a top priority. All analysis was conducted within our proprietary infrastructure, ensuring that no personal data left our systems. Our AI models worked exclusively with anonymous numerical data, ensuring complete confidentiality and compliance with data privacy standards. Doing this meant we could leverage powerful segmentation models while taking advantage of the most recent LLM systems to generate meaningful insights without sharing any personal data with those models.

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