Context A garden furniture manufacturer with a network of distributors and stores is looking to open a retail outlet in a market with potential for future growth. Challenge The main challenge is to guarantee both immediate and future growth, by establishing itself in a rapidly developing area, while minimising the risks associated with poor positioning. Innovation To address this issue, Indicia used a longitudinal database of the last 20 years, making it possible to identify the economic and demographic trends of the targeted region. By combining this data with the use of centrography indices, Indicia was able to locate areas of attraction and anticipate the movements of the population and shopping centres. An analysis of housing starts over the past ten years was also conducted, since it represents a key indicator for identifying areas of high residential growth. In addition, by studying the recent opening of many points of interest such as pharmacies, grocery stores, SAQ stores and discount stores, Indicia was able to identify booming commercial axes. Finally, by combining all these elements, the future urban development could be estimated with an excellent level of accuracy, guaranteeing the company an optimal choice of location for its new point of sale. Results With this rigorous analytical approach, the company selected a strategic location in a fast-growing region, benefiting from increased visibility and access to a specialized clientele.

Context A local pet food delivery company is seeking to identify its key customer segments to reduce spendings in its advertising efforts and local product deliveries. Challenge Since approximately 55% of households own a cat or dog, how can we precisely determine which of these households would be interested in a delivery service and would benefit the most from it? Innovation Indicia conducted a customer analysis to identify the main segments that have used the service over the past three years, focusing on recurrence. Three key segments emerged: owners of large breed dogs, busy large families, and elderly owners of small animals. The segments were geolocated using cadastral data on land size (to accommodate large dog breeds), census data on household size (to identify large families), and population age data (to identify elderly individuals). This information was then cross-referenced with pet ownership data to pinpoint the optimal neighborhoods for targeted postal and digital advertising, as well as to create more efficient delivery routes. Results Thanks to the targeted advertising efforts, the company was able to reduce its annual marketing budget by 35%, while increasing its revenues by 7%.