MBI Launches “MBI Where To”: Revealing Real Visitor Flows and Regional Connections

Understanding Movement. Spotting Opportunity. Targeting Precisely.

With MBI Where To, users can clearly see where people come from and where they go, uncovering the invisible connections between regions. For every postal code, the dataset provides transparent inbound and outbound travel patterns, showing both the top 10 and top 25 postal codes linked to the selected area, as well as the postal codes that account for 80% of total visitor or resident movements.

The result: a comprehensive, ready-to-use foundation for Origin-Destination Analysis that captures not only final destinations but also every stop – from work commutes and shopping trips to social visits and coffee breaks.

“With MBI Where To, we’re giving our clients a new lens on movement,” said Ray Roberts-York, Managing Director at MBI. “Whether you’re in retail, urban planning, marketing, or real estate, understanding how people actually move between areas provides an unparalleled competitive edge.”

Actionable Insights Across Industries

MBI Where To is designed for professionals across sectors who rely on population dynamics to make data-driven decisions. It helps to:

• Identify promising business locations based on actual visitor flows
• Target advertising campaigns to the right catchment areas
• Understand local versus cross-regional reach
• Support infrastructure and urban planning
• Benchmark area attractiveness and performance

Smart Methodology, GDPR-Compliant Data

The dataset is built using a sophisticated methodology that integrates multiple variables, including population data, workplace density, internal and cross-border migration, and temporary population patterns. All data is aggregated and processed in full compliance with the General Data Protection Regulation (GDPR), ensuring the highest standards of data privacy and reliability.

By combining MBI Where To with MBI’s globally consistent geodata portfolio, such as demographics, purchasing power, and area boundaries, users can pinpoint the who, where, and why behind every movement pattern, enabling precise audience segmentation and strategy optimization.

Example: Zurich’s Dynamic Flows

The example above from postal code 8055 Zurich illustrates the power of the dataset:
Residents of 8055 most frequently visit 8003, 8004, and 8045, while the area itself attracts visitors primarily from 8045, 8003, and 8047 – offering a clear view of real commuting and visitor relationships within the city