Computational System for Cross-Channel Customer Journey Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital containment analysis systems rely on heuristics and manual analytical methods, which are labor-intensive, incomplete, and unrepeatable, and fail to provide high-fidelity insights into customer interactions across multiple channels, leading to increased costs and customer frustration due to their inability to accurately capture the dynamics of customer journeys.
Innovation Solution
The system employs powerful computational methodologies and intuitive visual features to analyze large-scale data from various sources, performing advanced probabilistic calculations and statistical measures to quantify cross-channel interactions, providing high-fidelity insights into customer journeys and identifying factors that drive customers to higher-cost interaction channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analytical methods and heuristics are used to analyze customer interaction data, then implementation simplicity is maintained, but measurement precision and reliability of insights deteriorate
Solution Approach 1:
The patent replaces manual analytical methods with an automated computational system that uses probabilistic calculations and statistical measures. The system automatically ingests data from multiple channels, performs complex calculations, and generates insights without manual intervention, thereby improving measurement precision while managing complexity through automation.
Solution Approach 2:
The system performs self-service by automatically conducting the entire analysis process from data ingestion to insight generation. The computational system independently executes probabilistic calculations, identifies driving factors, and produces results without requiring external manual analysis, ensuring consistent and repeatable high-fidelity insights.
2Reliability
If automated computational methodologies are employed to analyze large-scale multi-channel data, then measurement precision and reliability improve, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the complex analysis task into distinct computational components: data ingestion from multiple channels, probabilistic calculation modules, statistical measure computations, and result generation. This segmentation allows the system to manage complexity through modular design while maintaining high reliability through systematic processing of customer journey data.
3Measurement precision
If comprehensive data from multiple channels is collected and analyzed, then measurement precision improves, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing data from multiple channels before analysis. It establishes data pipelines and computational frameworks in advance, allowing for efficient processing of large-scale data when analysis is needed, thereby reducing the time loss associated with ad-hoc data collection and processing.
Data Source
AI summary
Analytical methods and systems applied to a plurality of input files to understand and explain crucial factors leading to customers jumping, or hopping, from one channel to another channel. The methods and systems described may include receiving a first file associated with a first channel dataset and receiving a second file associated with a second channel dataset. The methods and systems described may include merging the two datasets based on key fields found within the metadata of the two files. In some embodiments, additional statistical metrics and measures may be applied to the merged dataset to both rank the merged events and to display the characteristics of each event within the entire merged dataset.