Driving intelligent expansion with ML-based location analytics

Clearbox AI partnered with a large dental service organization to develop a machine learning model that determines the best locations for new clinic openings. By leveraging historical performance data and external data sources such as demographic information and map insights, Clearbox AI empowered the organization to streamline its expansion strategy. The model is now integral to the decision-making process as the company continues to grow rapidly.

The challenge

As the dental service organization expanded rapidly, selecting new locations became increasingly complex. Relying on intuition risked overlooking key factors like local demographics, competitor presence, and infrastructure accessibility.

A major obstacle was fragmented data, scattered across internal systems, public datasets, and mapping platforms, limiting comprehensive analysis. To sustain high growth, they needed a data-driven approach to prioritize locations based on revenue potential, patient volume, and strategic fit.

The solution

Clearbox AI developed an advanced analytics framework to streamline site selection.

We first aggregated and prepared data, merging historical clinic performance (patient visits, revenue, operational metrics) with public demographic insights (population density, income levels, age distribution) and map-based factors (competitor proximity, real estate costs, traffic patterns).

Next, we built a predictive model to estimate demand and profit potential, integrating historical trends and market variables. The model included what-if scenarios, allowing the organization to simulate growth trajectories and market shifts. To support decision-making, we provided an interactive dashboard, enabling teams to compare multiple locations using visualized data and scenario simulations for better forecasting.

The result

The ML-powered approach enabled faster, more confident decisions, helping teams identify high-potential locations while reducing guesswork. By mitigating risk and ensuring efficient resource allocation, the organization avoided oversaturated markets and maximized ROI.

With a scalable, repeatable process in place, the company can now expand strategically, using predictive analytics as a core tool for sustainable growth.