TY - GEN
T1 - Data Mining Car Configurator Clickstream Data to Identify Potential Consumers
T2 - 22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023
AU - García-Sánchez, Juan Manuel
AU - Vilasís-Cardona, Xavier
AU - García-Piquer, Álvaro
AU - Lerma-Martín, Alexandre
N1 - Funding Information:
This work is partially funded by the Department de Recerca i Universitats of the Generalitat de Catalunya under the Industrial Doctorate Grant DI 2019-34.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The Car Configurator (CC) website provided by automotive Original Equipment Manufacturers (OEMs) enables customers to choose from the brand’s portfolio of cars without having to list them all. Afterwards, users move to dealership to formalize the purchase. However, the car they acquired might differ from the one they consulted online. Because there is no record from these deviations, CC data is considered noisy and meaningless. This paper investigates the question of whether valuable information can be extracted from CC clickstream data to aid automotive manufacturers in their operations. The data mining technique of genetic algorithms is employed to identify the characteristics that maximize the correlation between clickstream data and car sales. The findings reveal that the genetic algorithm outperforms the benchmark correlation value and that most frequently occurring elements from sales and webpage data may not be the most effective indicators of potential consumers. The proposed methodology can help identify future clients and target marketing efforts.
AB - The Car Configurator (CC) website provided by automotive Original Equipment Manufacturers (OEMs) enables customers to choose from the brand’s portfolio of cars without having to list them all. Afterwards, users move to dealership to formalize the purchase. However, the car they acquired might differ from the one they consulted online. Because there is no record from these deviations, CC data is considered noisy and meaningless. This paper investigates the question of whether valuable information can be extracted from CC clickstream data to aid automotive manufacturers in their operations. The data mining technique of genetic algorithms is employed to identify the characteristics that maximize the correlation between clickstream data and car sales. The findings reveal that the genetic algorithm outperforms the benchmark correlation value and that most frequently occurring elements from sales and webpage data may not be the most effective indicators of potential consumers. The proposed methodology can help identify future clients and target marketing efforts.
KW - Automotive industry
KW - Car Configurator
KW - Clickstream
KW - Correlation
KW - Data mining
KW - Genetic algorithm
KW - R2 Score
KW - Sales
UR - http://www.scopus.com/inward/record.url?scp=85172415134&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-42505-9_32
DO - 10.1007/978-3-031-42505-9_32
M3 - Conference contribution
AN - SCOPUS:85172415134
SN - 9783031425042
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 375
EP - 384
BT - Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings
A2 - Rutkowski, Leszek
A2 - Scherer, Rafał
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 June 2023 through 22 June 2023
ER -