Identity Resolution Service for Duplicate Customer Profile Merging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Call centers face inefficiencies due to duplicate customer profiles across different systems, leading to time-consuming data parsing and inaccuracy in customer information access, which detracts from the customer experience.
Innovation Solution
Implementing an identity resolution service that uses merging criteria and machine learning models to identify and merge duplicate profiles, prioritizing data fields based on recency, source, and completeness to create a unified and accurate customer profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple customer profiles are maintained across different systems, then data completeness is improved, but data accuracy and ease of access deteriorate due to duplicates
Solution Approach 1:
The patent merges multiple duplicate customer profiles into a single unified profile by comparing data across different systems (Salesforce, SalesNow, etc.) and consolidating them. This eliminates duplicate entries while preserving complete customer information, thereby maintaining data completeness while improving data accuracy.
Solution Approach 2:
The patent introduces an intermediary profile comparison service that acts as a mediator between multiple customer profile systems. This service compares profiles from different sources, identifies duplicates, and facilitates their consolidation, resolving the contradiction between maintaining complete data across systems and ensuring accurate, non-duplicate information.
2Loss of information
If multiple customer profiles are maintained across different systems, then data completeness is improved, but agent productivity deteriorates due to time-consuming data parsing
Solution Approach 1:
The patent performs preliminary actions by automatically comparing and merging customer profiles before agents need to access them. The system proactively identifies and consolidates duplicate profiles across different systems, so when agents access customer information, they immediately receive a single, accurate profile without needing to manually parse multiple duplicates.
Solution Approach 2:
The patent merges multiple customer profiles into a single unified profile, consolidating data from different systems (Salesforce, SalesNow, etc.). This eliminates the need for agents to manually parse through multiple duplicate profiles, thereby preserving data completeness while significantly improving agent productivity.
3Measurement precision
If profile merging is implemented, then data accuracy is improved, but system complexity increases due to merging criteria and machine learning models
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently evaluate merging criteria and determine whether profiles should be merged. The system automatically compares profiles, applies merging rules, and consolidates duplicates without requiring manual intervention or complex configuration, thereby improving data accuracy while managing system complexity through automation.
Solution Approach 2:
The patent uses parameter changes by implementing machine learning models that dynamically evaluate multiple criteria (data completeness, recency, source reliability) to determine merging decisions. The system automatically adjusts merging parameters based on profile characteristics, improving data accuracy while managing complexity through adaptive, data-driven decision-making.
4Measurement precision
If manual profile parsing is performed, then data accuracy can be verified, but time consumption increases
Solution Approach 1:
The patent replaces manual mechanical profile parsing with automated machine learning-based comparison systems. The machine learning models automatically evaluate merging criteria, compare profiles across multiple systems, and determine accuracy without human intervention, thereby maintaining data verification accuracy while eliminating time consumption associated with manual parsing.
Solution Approach 2:
The patent performs preliminary automated profile comparison and accuracy verification before agents need to access customer information. The system proactively merges and validates profiles using machine learning models, so when agents access customer data, accuracy has already been verified automatically, eliminating the need for time-consuming manual verification.
Data Source
AI summary
Systems and methods are described for merging customer profiles, such as may be implemented by a computer-implemented contact center service. In some aspects, a subset of profiles may be determined that satisfy merging criteria, where individual profiles include a plurality of data fields. At least one value in a first data field that conflicts between at least two profiles may be identified. Next a merged value may be selected for the first data field based on data deduplication criteria, where the data deduplication criteria includes at least one indicator of accuracy of values of the plurality of data fields. As a result of a determination that at least the subset of profiles of the group of profiles meet the merging criteria, at least the subset of profiles may be combined into a combined profile using the merged value.