Case Study: Cushman & Wakefield Waterloo Region achieves better real estate insights with PiinPoint

Cushman & Wakefield Waterloo Region uses PiinPoint to achieve 81.62% accurate traffic count predictions

Cushman & Wakefield Waterloo Region, a commercial real estate services firm, needed a reliable and consistent method to understand vehicle traffic patterns for its site selection and leasing decisions. The challenge was that traditional Annual Average Daily Traffic (AADT) data from municipalities was often outdated, inconsistently collected, and not available for all areas. To overcome this, they turned to PiinPoint's Location Intelligence platform and its mobile location data service.

The solution implemented by PiinPoint used machine learning and anonymized mobile location data from 45 million devices to predict accurate AADT counts for any major road segment in North America. The results provided consistent, up-to-date coverage with a median accuracy of over 81%, enabling confident, apples-to-apples comparisons across different markets. For Cushman & Wakefield Waterloo Region, this meant their retail analyst could provide clients with professional reports and trusted insights to make informed real estate decisions, with one analyst stating she didn't know how she would do her job effectively without PiinPoint.

Cushman & Wakefield Waterloo Region