Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Automotive companies can use data from connected vehicles to enhance customer experience. Driver assistance functions have a low usage rate, and appropriate proactive function recommendations can improve both usage rate and customer experience. Qualitative studies often drive the development, and functions are recommended using a rule-based system. We provide a patented machine learning-based classification concept to make intelligent function recommendations based on customer usage. Therefore, we classify customer experience based on the driving context. We defined how to create an experience label for a function activation context and evaluated the approach using 716,000 function activations collected from the customer fleet data by an automotive manufacturer. To improve the quality of the binary classification model, we defined geospatial key performance indicators that provide quantifiable measures for the performance of a function on a road section. Our results reveal that the novel classification concept is a viable solution for car function recommendations.
Recommended Citation
Micus, Christian; Homola, Daniel; Böhm, Markus; and Krcmar, Helmut, "Classification of Experience for Proactive In-Car Function Recommendations Based on Customer Usage Data" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 7.
https://aisel.aisnet.org/hicss-57/da/big_data_and_analytics/7
Classification of Experience for Proactive In-Car Function Recommendations Based on Customer Usage Data
Hilton Hawaiian Village, Honolulu, Hawaii
Automotive companies can use data from connected vehicles to enhance customer experience. Driver assistance functions have a low usage rate, and appropriate proactive function recommendations can improve both usage rate and customer experience. Qualitative studies often drive the development, and functions are recommended using a rule-based system. We provide a patented machine learning-based classification concept to make intelligent function recommendations based on customer usage. Therefore, we classify customer experience based on the driving context. We defined how to create an experience label for a function activation context and evaluated the approach using 716,000 function activations collected from the customer fleet data by an automotive manufacturer. To improve the quality of the binary classification model, we defined geospatial key performance indicators that provide quantifiable measures for the performance of a function on a road section. Our results reveal that the novel classification concept is a viable solution for car function recommendations.
https://aisel.aisnet.org/hicss-57/da/big_data_and_analytics/7