Automated Customer Data Cleansing System
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Solution Overview
Problem
Customer Relationship Management (CRM) systems face challenges in efficiently capturing new contact data, archiving obsolete data, ensuring privacy compliance, and detecting and merging duplicate entries, leading to inefficiencies and inaccuracies in data management.
Innovation Solution
A computer-implemented method and system that applies automated filtering to identify contacts based on defined criteria, provides feedback for additional filtering, and automatically updates customer data, incorporating a data cleanse system to assist in data stewardship tasks such as archiving, privacy compliance, and duplicate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated capture of new contact data is implemented, then data capture efficiency is improved, but irrelevant information over-capture occurs
Solution Approach 1:
The data capture process is segmented into multiple filtering stages: initial automated capture, followed by sequential filtering based on contact attributes (activity level, recency, frequency), and finally manual review for high-value contacts. This segmentation allows automated efficiency while preventing irrelevant information over-capture through progressive filtering.
Solution Approach 2:
The system applies partial automation by capturing data automatically for low-value contacts but requiring manual review for high-value contacts. This partial action approach ensures comprehensive data capture while maintaining human oversight for critical decisions, preventing both under-capture and over-capture of irrelevant information.
2Measurement precision
If manual search and archiving of obsolete data is performed, then data archiving accuracy is improved, but human interaction and time consumption increase
Solution Approach 1:
The system performs self-service by automatically identifying obsolete contacts based on predefined criteria (activity level, recency, frequency) and initiating archiving without human intervention. This self-service mechanism handles the bulk of archiving tasks automatically, reducing human interaction time while maintaining accuracy through systematic evaluation of contact attributes.
Solution Approach 2:
The system performs preliminary action by pre-defining archiving criteria and automatically executing the archiving process based on these predefined rules. This eliminates the need for manual search and decision-making, significantly reducing human interaction time while ensuring consistent and accurate archiving of obsolete data.
3Manufacturing precision
If comprehensive data filtering is applied, then data quality is improved, but processing complexity and computational resources increase
Solution Approach 1:
The filtering process is segmented into multiple independent stages, each evaluating specific contact attributes (activity level, recency, frequency). This segmentation allows complex data quality filtering to be broken down into manageable steps, improving overall data quality while reducing the complexity burden on any single processing stage.
Solution Approach 2:
The system applies parameter changes by adjusting filter thresholds and criteria dynamically based on contact characteristics. This allows comprehensive data quality filtering through parameter optimization rather than complex processing logic, improving data quality while maintaining processing efficiency and reducing computational complexity.
4Measurement precision
If duplicate contact detection is performed manually, then detection accuracy is improved, but processing time and human resources increase
Solution Approach 1:
The system performs self-service by automatically detecting duplicate contacts using algorithms that compare contact attributes (name, email, phone, organization). This automated detection handles the bulk of duplicate identification without human intervention, significantly reducing processing time while maintaining high accuracy through systematic comparison of multiple contact parameters.
Solution Approach 2:
The system replaces manual mechanical inspection with automated computational algorithms for duplicate detection. This substitution uses electronic comparison of contact data attributes, eliminating human labor while improving processing speed and maintaining accuracy through consistent, objective evaluation of contact information.
Data Source
AI summary
System and method for updating customer data that includes a plurality of electronically stored contact records that each include contact information for a respective individual contact. Filtering is applied to the customer data to identify contacts that fall within defined filtering criteria. Information about the identified contacts is provided to a decision making authority for a further layer of filtering. Customer data is updated based on feedback derived from the decision making authority.