User Categorization Algorithm for Vehicle Sales Order Correlation
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Solution Overview
Problem
In vehicle sales, leasing, and rental interfaces, users often abandon orders without completing them, leading to inefficient and incomplete manual efforts by Inside Sales teams to correlate these abandoned orders with previous transactions.
Innovation Solution
An automated user categorization and insight method using algorithms to identify users, associate failed or incomplete transactions with successful ones based on various criteria, and generate a UI for the Inside Sales team, establishing communication links when a predetermined association threshold is met, thereby optimizing sales and customer engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correlation methods are used by the Inside Sales team, then some level of order association can be achieved, but the process becomes inefficient, inexact, and incomplete
Solution Approach 1:
The patent replaces the manual mechanical correlation process with an automated algorithmic system that uses machine learning models to match abandoned orders with user profiles. The system automatically compares order data points (vehicle selection, configuration, pricing) against historical purchase patterns, eliminating manual effort while improving accuracy through consistent algorithmic application.
Solution Approach 2:
The system enables self-service by automatically performing the correlation function without requiring Inside Sales team intervention. The algorithm independently identifies abandoned orders, matches them with potential user profiles, and presents results for verification, allowing the system to serve itself in the data matching task.
2Productivity
If automated algorithms are implemented for user identification and order correlation, then productivity and accuracy improve, but system complexity increases
Solution Approach 1:
The patent segments the complex correlation task into distinct functional modules: data collection from multiple sources, feature extraction from order and user data, machine learning model inference, and result presentation. Each module handles a specific aspect of the correlation process, making the overall system more manageable and maintainable despite its computational complexity.
Solution Approach 2:
The system introduces an intermediary layer of machine learning models that act as a bridge between raw order data and sales team decisions. These models process and interpret data patterns, translating complex multi-parameter correlations into actionable insights that are easier for the sales team to consume and act upon.
3Loss of information
If comprehensive data analysis is performed to improve user categorization, then insight quality improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing key features from historical order data and user profiles before correlation is needed. Data is cleaned, normalized, and key characteristics are extracted in advance, so when an abandoned order needs correlation, the system can quickly query pre-prepared data structures rather than processing raw data from scratch.
Solution Approach 2:
The patent applies parameter changes by adjusting the complexity and depth of analysis based on the specific context. The system can dynamically select which data parameters to analyze and to what depth, allowing it to balance comprehensiveness with processing speed by changing analysis parameters rather than always performing maximum-depth analysis.
Data Source
AI summary
Methods and systems for characterizing a user or a session associated with a user interface and customizing the user interface responsive to the characterization such that a sales transaction is facilitated. The methods and systems are applicable in the vehicle sales, leasing, subscription, and rental contexts, as well as in other contexts.