Pathing Analytics System for Customer Journey Sequence Mining
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Solution Overview
Problem
Current customer journey mapping tools are inefficient in identifying high-value sequences due to their inability to analyze partial sequences, consider marketing goals, and provide timely results, often taking days to analyze and potentially misidentifying effective journeys.
Innovation Solution
Pathing analytics system that operates on interaction data stored in a columnar database, using recursive functions to identify high-value customer journey sequences by converting interaction data into sequences and sub-sequences, and presenting them on a graphical user interface for visualization and comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional customer journey mapping tools are used to analyze customer interaction sequences, then comprehensive analysis of entire sequences is performed, but the analysis time is excessively long (days) and the tools cannot efficiently identify high-value sequences
Solution Approach 1:
The patent segments customer interaction sequences into smaller sub-sequences and analyzes them independently using mapreduce operations. Instead of processing entire customer journeys sequentially, the system divides the analysis into manageable chunks that can be processed in parallel, dramatically reducing analysis time from days to minutes while maintaining comprehensive coverage of all sequence patterns.
2Adaptability or versatility
If traditional tools analyze only exact sequences, then processing is simpler, but they cannot identify partial sequences that may also be valuable for achieving marketing goals
Solution Approach 1:
The patent implements partial sequence analysis by allowing users to specify start and end points for sequence analysis, enabling examination of specific portions of customer journeys rather than requiring complete sequence matching. This partial action approach identifies valuable patterns within subsets of interactions, increasing adaptability to different analytical needs while managing complexity through parameterized queries.
3Measurement precision
If traditional pathing analytics are used, then common sequences are identified, but they do not consider marketing goals and may misidentify effective journeys
Solution Approach 1:
The patent incorporates marketing goals as feedback criteria in the sequence analysis process. By allowing users to specify desired outcomes (e.g., conversions, purchases) and analyzing sequences in relation to these goals, the system provides precise measurement of sequence effectiveness. The feedback mechanism ensures that identified patterns are truly valuable for achieving business objectives rather than merely statistically common.
Data Source
AI summary
Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.