Journey Sequence Analysis Framework for Probabilistic Path Quantification
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Solution Overview
Problem
Existing solutions for analyzing journey data rely on heuristics and model-driven approaches, which are limited by behavior-based approximations and fail to accurately represent customer experiences, especially in complex and voluminous datasets with varied sources and long time frames.
Innovation Solution
The development of a Journey Sequence Analysis and Recommendation Framework (JSARF) that quantifies base probabilities of indirect journey paths leading to outcomes, providing a user-friendly graphical interface for analyzing complex journey data, including healthcare and customer interaction data, to understand crucial factors and recommend actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If model-driven approaches with heuristics are used to analyze journey data, then analysis can be performed with simplified assumptions, but measurement precision and reliability of customer experience representation deteriorate
Solution Approach 1:
The patent replaces model-driven heuristic approaches with a data-driven mathematical framework using random walk theory and Markov chains. This substitution eliminates the need for manually constructed assumptions and behavior-based approximations, directly addressing the contradiction by maintaining ease of analysis through automated mathematical processes while significantly improving measurement precision through rigorous probabilistic modeling of customer journey paths.
Solution Approach 2:
The patent transforms the analysis approach by changing from static heuristic models to dynamic probabilistic parameters that capture the true behavior of customer journeys. By using base probabilities derived directly from data and calculating statistical metrics like lift and confidence intervals, the system maintains analytical simplicity while achieving precise measurement of customer experience factors.
2Device complexity
If behavior-based approximations are used in existing solutions, then analysis complexity is reduced, but the ability to reveal real customer experience deteriorates
Solution Approach 1:
The patent substitutes behavior-based approximation models with a rigorous mathematical framework based on random walk theory and Markov chains. This replacement eliminates the need for behavioral assumptions while maintaining system tractability through automated probabilistic calculations, directly resolving the contradiction between complexity and reliability by using mathematically sound models that automatically adapt to the data.
Solution Approach 2:
The system enables self-service analysis by automatically deriving base probabilities and calculating statistical metrics from the journey data without requiring manual model construction or behavioral assumptions. The mathematical framework self-adjusts to capture real customer experience patterns, reducing system complexity while improving reliability through data-driven automatic adaptation.
3Ease of manufacture
If manual model construction is used, then assumptions can be simplified, but the quantity and complexity of data that can be analyzed deteriorates
Solution Approach 1:
The patent replaces manual model construction with automated probabilistic modeling using random walk theory and Markov chains. This substitution allows the system to handle voluminous and complex journey data from multiple sources and time frames automatically, resolving the contradiction by using mathematical frameworks that scale with data volume while maintaining analytical simplicity through automated probability calculations.
Solution Approach 2:
The mathematical framework provides universal applicability across diverse journey data types and sources. By using general probabilistic principles that work across different data formats, time frames, and event sequences, the system maintains simple model construction while dramatically increasing the quantity and variety of analyzable data through a unified analytical approach.
Data Source
AI summary
Analytical methods and systems applied to sequential event data are disclosed. An exemplary system and method analyzes datasets containing events in a plurality of journeys. The methods and systems described analyze and quantify the relative importance of events and sequences leading to outcomes where the data is complex and interconnected. In some embodiments, a graphical user interface illustrates the quantification of these datasets. In some embodiments, the graphical user interface maps the journey paths to show the relative importance of each journey path. In some embodiments, the maps of journey paths are interactive, allowing selection of paths of interest for detailed analysis. In some embodiments, the methods and systems calculate paths similar to a journey path of interest. An exemplary method and system also provides detailed recommendations for changing events within a sequence to either increase or decrease the likelihood of achieving a selected outcome.