Know the flow! Unique Footfall Information as USP in Payment and PropTech

Know the flow! Unique Footfall Information as USP in Payment and PropTech

Abstract

Development of a footfall model fed by a unique set of real data in order to generate footfall information in an unknown granularity (time and location), on-demand and as a basis for an ecosystem of location-intelligent products, and services for both implementation partners.

In an era of omnichannel retail, footfall has become the most important KPI for retailers. Nowadays, on-site retail is mainly about contacts, less about sales. Going along with this development, more and more renting contracts are based on footfall rather than OCR (Occupancy-Cost Rate; based on turnovers). Thus, there is a huge market demand of both retailers and landlords for accurate and granular footfall data for today, tomorrow, and a location’s history. Investigating currently offered solutions like mobile data, agent-based modeling, and physical measurements like radar or laser reveal the lack of appropriate solutions. This project is developing a footfall model of real and highly granular data being able to provide location-specific footfall information as a now-cast, forecast, and review of the past. We use transactional data from SIX Payment Services (SPS) and a data pipeline consisting of demographics, merchant structure, and location-specific user feedback from Popupshops.com (POS). The core idea is simple and consisting of reverse engineering of the sales funnel.