Customer Friction Factor Scoring for Transaction Abandonment
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Solution Overview
Problem
Traditional methods for identifying and addressing friction points in online transactions are flawed, often being reactive, limited in scope, and prone to bias, which hampers the ability to reduce abandonment and improve conversion rates.
Innovation Solution
A method that provides quantitative evaluations of customer friction by identifying transactions, determining Customer Friction Factor (CFF) scores, and comparing them to valid industry benchmarks to pinpoint friction points, allowing for proactive improvements in the customer experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional customer surveys are used to measure friction, then customer feedback is collected, but the measurement is reactive and only captures extreme cases (tails of the bell curve) rather than providing comprehensive coverage
Solution Approach 1:
The system performs preliminary actions by automatically measuring friction indicators (time to complete tasks, error rates, navigation patterns) in real-time during the customer journey, before customers explicitly complain or provide feedback. This proactive measurement captures friction at the point of occurrence rather than waiting for reactive survey responses.
Solution Approach 2:
The system enables self-service measurement by having the CRM automatically collect and analyze friction data from transaction records without requiring customers to actively participate in surveys. The system uses existing transaction data to self-measure friction indicators, eliminating the need for customers to manually report their experiences.
2Measurement precision
If net promoter scores are used to evaluate customer friction, then customer loyalty is measured, but the scope is limited and does not explain the why behind friction points
Solution Approach 1:
The system segments the customer journey into distinct transaction stages (e.g., cart abandonment, checkout process, post-purchase support) and measures friction indicators specific to each segment. This segmentation allows the system to identify not just that friction exists, but precisely where in the journey it occurs and what specific actions contribute to it.
Solution Approach 2:
The system introduces intermediary friction indicators (time to complete tasks, number of errors, navigation paths) that mediate between raw transaction data and customer friction perception. These intermediaries translate objective transaction metrics into meaningful friction measurements that explain the why behind customer experiences.
3Measurement precision
If sentiment analysis is used to detect friction, then customer sentiment is captured, but sampling bias occurs and non-responses are missed
Solution Approach 1:
The system eliminates sampling bias by using self-service measurement through automatic analysis of complete transaction records. Every transaction is measured regardless of whether the customer responds to surveys or provides feedback, ensuring non-responses are not missed and all customer experiences are included in the analysis.
Solution Approach 2:
The system achieves universality by measuring friction through multiple indicators (time-based metrics, error rates, navigation patterns) that apply to all customer transactions regardless of their response behavior. This multi-functional measurement approach captures friction in both responsive and non-responsive customers equally.
4Measurement precision
If comprehensive friction measurement is implemented across all transactions, then measurement accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system manages complexity by segmenting the measurement process into distinct friction indicators (time to complete tasks, error rates, navigation patterns) that can be calculated independently from transaction records. This segmentation allows comprehensive measurement without requiring a single complex analysis system, as each indicator can be processed through dedicated, optimized routines.
Data Source
AI summary
Quantitative evaluations of friction within a customer experience may be provided to reduce abandonment and improve conversion of transactions. One or more transactions may be identified. One or more personas corresponding to the one or more transactions may be identified. One or more customer friction factor (CFF) scores corresponding to the one or more transactions may be determined. A given CFF score may be a quantification of an aspect of a given transaction that has a negative impact on a customer experience associated with the given transaction. The one or more CFF scores may be compared with one or more valid comparisons relating to the same industry and the same transaction type. One or more friction points may be identified based on the comparison of the one or more CFF scores. Addressing a friction point may reduce abandonment and improves conversion associated with transactions.