Customer Journey Timelines for Root Cause and Escalation Analysis
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Solution Overview
Problem
Conventional systems fail to analyze customer journeys comprehensively, leading to difficulty in determining root causes of key performance indicators (KPIs) and effectively managing escalations, as they lack the capability to transcend data silos and preserve the integrity of customer interactions.
Innovation Solution
A system and method for escalation management and journey mining that identifies issues in customer timelines, determines their causes, and performs escalation management based on impact, utilizing a graphical user interface for annotation and remedial action, and employs engines for issue identification, timeline analysis, and journey building.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data aggregation and machine learning are applied to customer journey data, then processing efficiency is improved, but the ability to penetrate journey complexity and identify root causes deteriorates
Solution Approach 1:
The patent segments customer journey data into discrete events with specific attributes (event type, timestamp, channel, etc.) rather than aggregating them immediately. This event-level segmentation allows individual events to be analyzed while preserving their contextual relationships, enabling root cause identification without sacrificing processing efficiency through structured event streams.
Solution Approach 2:
The patent adds temporal and contextual dimensions to traditional data aggregation by creating event timelines that show the sequence and timing of customer interactions. This dimensional enhancement transforms static aggregated metrics into dynamic journey narratives, allowing analysts to identify root causes through temporal patterns while maintaining processing efficiency through structured event data models.
2Loss of information
If comprehensive customer journey data is collected across all channels and processes, then analytical completeness is improved, but data complexity and analysis difficulty worsen
Solution Approach 1:
The patent segments comprehensive customer journey data into standardized events with consistent attributes across different channels and processes. Each event is independently structured with uniform fields (event type, timestamp, channel, etc.), which reduces analysis complexity while preserving analytical completeness by maintaining all necessary data points in a manageable format.
Solution Approach 2:
The patent transforms multi-channel, multi-process data into a standardized event parameter structure. By converting diverse data sources into uniform event attributes (channel type, event category, timestamp format), the system reduces analysis complexity while maintaining analytical completeness through consistent parameterization of all journey data.
3Measurement precision
If detailed individual customer journey analysis is performed, then customer experience understanding is improved, but processing time and resources worsen
Solution Approach 1:
The patent performs preliminary structuring of customer journey data into standardized event timelines during data collection. By pre-organizing individual customer events with consistent attributes and temporal sequencing, the system enables rapid detailed analysis when needed without incurring excessive processing time during actual analysis operations.
Solution Approach 2:
The patent creates simplified event timeline representations that copy essential journey information in a structured format. These event copies maintain the critical temporal and contextual relationships needed for detailed customer experience analysis while being computationally efficient to process compared to analyzing raw comprehensive journey data.
4Ease of operation
If traditional KPI aggregation methods are used, then ease of monitoring is improved, but ability to understand customer experiences driving metrics deteriorates
Solution Approach 1:
The patent adds the event timeline dimension to traditional KPI aggregation by showing the sequence of customer events that drive metric changes. This dimensional enhancement allows monitoring systems to maintain ease of use through familiar KPI displays while simultaneously providing access to underlying event sequences that explain metric drivers, thus preserving both monitoring ease and metric understanding.
Solution Approach 2:
The patent introduces event timelines as an intermediary layer between traditional KPI aggregation and detailed customer journey analysis. This intermediary structure connects aggregated metrics to individual customer events, allowing users to easily monitor KPIs while having the option to drill down into event-level details to understand metric drivers without losing information.
Data Source
AI summary
The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.