TY - JOUR
T1 - Modeling of Tourist Profiles with Decision Trees in a World Heritage City
T2 - The Case of Cuenca (Ecuador)
AU - Serrano López, Ana Lucía
AU - Freire Chaglla, Segundo Amador
AU - Espinoza-Figueroa, Freddy Edgar
AU - Andrade Tenesaca, Dolores Susana
AU - Villafuerte Pucha, María Elena
N1 - Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/9/3
Y1 - 2019/9/3
N2 - This research project proposes the formulation of profiles composed of foreign and national tourists in a world heritage city as a contribution to the strategies of the Association of Accommodation Establishments in the city of Cuenca. A total of 1,293 surveys, gathered during holidays between 2015 and 2016, underwent a statistical analysis of decision trees and statistical regression using seven variables. In addition, by means of CART and Random Forest technical comparisons, the models and predictive variables were prioritized for marketing strategies and planning. Two tourist profiles were described, the backpacker type and the cultural type, with an error of 1%, where the variables of visitor age, daily average accommodation expense, and type of employment at the origin predicted the accommodation selection upon arrival at the tourist destination in a better way.
AB - This research project proposes the formulation of profiles composed of foreign and national tourists in a world heritage city as a contribution to the strategies of the Association of Accommodation Establishments in the city of Cuenca. A total of 1,293 surveys, gathered during holidays between 2015 and 2016, underwent a statistical analysis of decision trees and statistical regression using seven variables. In addition, by means of CART and Random Forest technical comparisons, the models and predictive variables were prioritized for marketing strategies and planning. Two tourist profiles were described, the backpacker type and the cultural type, with an error of 1%, where the variables of visitor age, daily average accommodation expense, and type of employment at the origin predicted the accommodation selection upon arrival at the tourist destination in a better way.
KW - decision tree
KW - Grouping
KW - offer and demand
KW - random forest
KW - statistics
KW - tourist profiles
KW - world heritage city
UR - https://www.scopus.com/pages/publications/85050697247
U2 - 10.1080/21568316.2018.1501731
DO - 10.1080/21568316.2018.1501731
M3 - Artículo
AN - SCOPUS:85050697247
SN - 2156-8316
VL - 16
SP - 473
EP - 493
JO - Tourism Planning and Development
JF - Tourism Planning and Development
IS - 5
ER -
