loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ryuichi Ueno ; Peter Boyd and Dragos Calitoiu

Affiliation: Department of National Defence, 60 Moodie Drive, Ottawa, ON, K1A 0K2, Canada

Keyword(s): Feature Selection, Clustering, Logistic Regression, Propensity Scores.

Abstract: 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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.131.13.37

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ueno, R.; Boyd, P. and Calitoiu, D. (2021). Identifying Geographical Areas using Machine Learning for Enrolling Women in the Canadian Armed Forces. In Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-485-5; ISSN 2184-4372, SciTePress, pages 307-316. DOI: 10.5220/0010186703070316

@conference{icores21,
author={Ryuichi Ueno. and Peter Boyd. and Dragos Calitoiu.},
title={Identifying Geographical Areas using Machine Learning for Enrolling Women in the Canadian Armed Forces},
booktitle={Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - ICORES},
year={2021},
pages={307-316},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010186703070316},
isbn={978-989-758-485-5},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - ICORES
TI - Identifying Geographical Areas using Machine Learning for Enrolling Women in the Canadian Armed Forces
SN - 978-989-758-485-5
IS - 2184-4372
AU - Ueno, R.
AU - Boyd, P.
AU - Calitoiu, D.
PY - 2021
SP - 307
EP - 316
DO - 10.5220/0010186703070316
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic