Identifying Geographical Areas using Machine Learning for Enrolling Women in the Canadian Armed Forces

To improve the visibility of military service as a career option for women, the Canadian Armed Forces (CAF) can tailor marketing campaigns to geographical areas and demographics within Canada that have historically high enrollment of women. To aid in this recruitment strategy, a logistic regression model was developed using historical recruiting data. The score obtained was used to rank Canadian postal codes and to identify the ones with the highest potential for recruiting of women. Additional demographic filtering was applied using marketing segments provided by a vendor. The final top 10% postal codes with the highest probability of women enrollment were clustered based on the collective social media behaviour of each postal code and was binned using the distance to the nearest recruiting centre. Several social media outlets were observed to be of interest, among them YouTube and Snapchat appear as viable options to reach women with a high probability of CAF enrollment.