Retail & Geomarketing

Improve location decisions using dynamic information and predictive models

Who we work with

  • Retail chains & Shopping malls
  • Geomarketing consultants
  • Market research companies
  • Advertising companies and agencies
  • Event industry

How can we help?

Location intelligence, understood as the process of deriving meaningful and actionable insights from spatial data, is an essential tool to solve a variety of marketing problems, from retail location decisions to the optimisation of product distribution and advertising. While geomarketing is not a new concept, big data and artificial intelligence have opened new opportunities to understand and predict the spatial behaviour of consumers, multiplying the applications of location intelligence to the planning and implementation of marketing activities.

The spatial distribution of consumers is one of the most critical inputs for geomarketing studies. To estimate the number of people present in a particular area, traditional geomarketing solutions relied heavily on the use of static information (census data, statistics on number of workers, retail floorspace, etc.), which was then used to feed different types of models (e.g., gravity models) with the aim to forecast traffic and sales. New data sources for dynamic population mapping, such as mobile location data, allow a much finer analysis of the number and profile of people located in a particular area at a particular time, which in turn enables the development of more accurate predictive models of traffic and customer behaviour. Nommon relies on its expertise in mobility analytics, spatial analysis and data science to provide tailor-made location intelligence solutions to help businesses assess their market penetration, identify new market opportunities, inform site planning and business expansion strategies, and optimise marketing, sales and advertising investments.

Our products

Transforming location intelligence through high-resolution mobility data and predictive analytics

Best-in-class origin-destination matrices from mobile network data

Leveraging mobile location data to understand visitor footfall and customer behaviour

Crafting innovative solutions based on geospatial big data and predictive analytics

Related content

Research Projects
2013 - 2016