Context A discount store chain with several hundred locations, all operating with near-zero profitability, is looking to consolidate its national network to improve long-term profitability. Challenge How can you ensure that the decisions regarding closures, relocations, and renovations are made correctly to achieve the company’s objectives? Innovation Indicia conducted a provenance analysis of the company’s current customers to define the commercial areas of the stores and identify the most profitable population segments. The definition of commercial areas helped determine the average area of attraction for a retail location, which in turn allowed for the quantification of how many branches the network could support while minimizing inter-branch cannibalization. Customer segmentation highlighted the best customer profiles for the company based on socio-demographic variables such as age, ethnicity, household size, education level, and income. With access to census data by neighborhood, Indicia was able to pinpoint the areas with the best prospects. Finally, Indicia created statistical models using the most crucial variables for a store's success, proposing hundreds of potential changes to the network. Results The use of this geo-factual data enabled the development of a much more robust and profitable branch network in the long term. Thanks to the 32 closures, 47 relocations, and 112 renovations implemented, gross profitability peaked at 17% after three years, compared to the previous 0%.