BEV Buyer Identification via Driver Segmentation
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Solution Overview
Problem
Internal combustion engines (ICEs) are limited by the use of costly fuels and high emissions, and they generate louder noises due to detonation, whereas battery electric vehicles (BEVs) face challenges in identifying prospective purchasers effectively.
Innovation Solution
A computer-implemented method to identify prospective BEV purchasers by categorizing drivers based on their vehicle ownership history and driving factors, including proximity to charging stations and trip behavior, to target potential buyers of BEVs and notify them of incentives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional marketing methods are used to identify BEV purchasers, then marketing coverage is broad, but identification accuracy is low
Solution Approach 1:
The patent segments drivers into three distinct subsets based on vehicle ownership history: those who owned ICE vehicles then ICE vehicles, those who owned ICE vehicles then BEVs, and those who have not changed vehicles. This segmentation allows for targeted analysis of each group's characteristics and behaviors, improving identification accuracy without overwhelming complexity.
Solution Approach 2:
The patent transforms raw driving data into meaningful parameters such as trip behavior patterns, charging station proximity metrics, and vehicle usage statistics. By changing the parameters from raw data to actionable insights, the system achieves high identification accuracy while managing data processing complexity through structured transformation.
2Measurement precision
If comprehensive driving factors are analyzed, then purchaser identification accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the three driver subsets and establishing the analytical framework before processing individual cases. By setting up the segmentation structure and evaluation criteria in advance, the system can quickly classify new drivers without repeating the entire analysis process, reducing processing time while maintaining comprehensive factor analysis.
Solution Approach 2:
The patent creates simplified representations (copies) of driver profiles based on key characteristics from the comprehensive dataset. Instead of processing all raw data for each evaluation, the system uses condensed profile copies that capture essential purchasing indicators, achieving high accuracy with reduced processing time.
Data Source
AI summary
A method for identifying prospective purchasers of battery electric vehicles (BEVs) may include defining a predetermined time frame associated with vehicle ownership, receiving a first dataset including a first subset of drivers are drivers who have owned or leased any first internal combustion engine (ICE) vehicle followed by any second ICE vehicle within the predetermined time frame, a second subset of drivers are drivers who have owned or leased any first ICE vehicle followed by any second BEV within the predetermined time frame, and a third subset of drivers are drivers who have not changed vehicles within the predetermined time frame, receiving a second dataset including a set of drivers who own or lease any first ICE vehicle which is in a same vehicle class as a target BEV, and identifying a target set of prospective purchasers of BEVs.