Smart city analytics: new opportunities for the retail sector

The focus is on connected infrastructures and the growing use of artificial intelligence and big data analytics to evaluate large amounts of data. Those responsible in cities hope to be able to ensure better urban planning, more efficient traffic management and optimized real estate use. City marketing and the local economy should also benefit from the findings.

In order to strengthen these areas with data-based decisions, the demand for smart city solutions will continue to grow – not only on the part of city administrations, but increasingly also among companies that want to align their strategies with data. The Smart City Analytics white paper from Ariadne Maps GmbH discusses this development and what it means for the retail sector.

Cities are facing numerous challenges that will shape life and the economy in the coming decades:

In order to be able to respond to these challenges and changes with effective solutions, those responsible need a good database for decisions and measures. Analysis solutions for smart cities are a tool for meeting these challenges.

A central component of smart cities is the systematic recording of movement patterns – known as visitor frequency measurement. This makes it possible to precisely identify how people move through urban spaces.

These technologies provide valuable data for urban planning, traffic optimization and city marketing.

Movement data provides valuable insights into the current status and development of visitor frequency in municipalities. In combination with other information such as purchasing power data, sales figures or real estate use, informed decisions can be made about urban planning or city marketing measures. As local authorities have a major interest in the prosperity of the retail sector, they can also help to strengthen local economic areas.

According to the Ariadne Maps whitepaper, the city of Traunstein in Bavaria provides a particularly illustrative example. Here, transaction data was linked with demographic and economic data such as retail sales and purchasing power index in order to make well-founded decisions for urban development and retail. Thanks to this data, it was not only possible to optimize the administrative budget – brick-and-mortar retail also benefited: Heatmaps and Sankey diagrams helped to identify hotspots and potential customer flows and to develop targeted marketing strategies for these areas.

In Waldkirchen, also in Bavaria, frequency analyses showed that 43% of visitors spent more than 30 minutes in the town on a Sunday when it was open for business. Attractive activities and a varied shopping experience therefore increase the length of stay and potentially also sales. Thanks to the findings on the impact of event marketing and its influence on footfall and purchasing behavior, municipalities can focus on targeted stimulating measures or events to strengthen local trade.

Retailers use frequency data in their stores to optimize footfall, improve product placement and offer targeted promotions in the right place at the right time. Municipalities can also use such data to identify attractive zones, strategically plan events and initiate joint advertising campaigns to revitalize retail, e.g. via:

• digital information boards in highly frequented zones
• push messages to smartphones via beacon networks
• geofencing campaigns in cooperation with local retailers

There is potential here for the retail trade – through partnerships with the city or joint initiatives with other traders.

As promising as frequency analyses and smart city insights are, it is important not to lose sight of the associated risks. This is because data-based management can produce unintended effects if it is used without a strategic balance.

A key risk is the one-sided focus on high-frequency locations: if investments are geared exclusively towards highly frequented areas, other districts and peripheral locations risk falling behind. This can lead to uneven urban development, vacancies and a declining quality of life. Closely linked to this is the risk of gentrification. If rents rise in popular locations as a result of targeted gentrification, smaller, owner-managed businesses can be increasingly squeezed out. The result is a market in which large chains or financially strong brand spaces are able to assert themselves. The result: an impoverishment of the supply structure. Smaller companies also suffer from unequal access to technology and data with fewer financial and human resources, which further exacerbates competitive disadvantages.

On the visitor side, a one-sided focus of city marketing can distort consumer behavior by constantly directing visitors to the same places and thus suppressing diversity and individual shopping routes. Last but not least, there are data protection concerns among the population and the feeling of being monitored.

Balanced concepts are needed to counteract these risks: cities should consciously

• also promote less frequented areas,
• involve local retailers through joint analyses and initiatives and support
• support technologically weaker players.

Cities should also communicate transparently how data is collected and used – and what benefits this brings for the general public.

Only in this way can urban data analysis become a tool for a fair, diverse and liveable city.