Electronic Activity Matching for Automatic Record Creation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing and maintaining systems of record associated with electronic communications is challenging due to the large volume of heterogeneous data and the complexity of manually entering information, leading to inefficiencies and errors.
Innovation Solution
Systems and methods for generating new record objects based on electronic activities, using parsing techniques and specific matching rules to determine whether to create new or update existing record objects, balancing resource use and match strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new record objects are generated for every electronic activity, then the system of record contains more complete and up-to-date information, but computer resource usage increases and system complexity increases
Solution Approach 1:
The system performs preliminary actions by establishing matching rules and filtering criteria before processing electronic activities. The matching engine pre-configures object type mappings, field mappings, and match score thresholds, allowing it to efficiently determine whether to create or update record objects without exhaustive processing of each activity, thus reducing system complexity while maintaining information completeness
Solution Approach 2:
The system changes parameters by adjusting match score thresholds and filtering criteria based on object types and data source priorities. By dynamically modifying these parameters, the system optimizes the balance between creating comprehensive records and managing system complexity, generating new record objects only when match scores exceed configured thresholds
2Measurement precision
If manual data entry is used for electronic communications, then data accuracy can be verified, but the process is time consuming and error prone
Solution Approach 1:
The system performs self-service by automatically parsing electronic activities, extracting relevant information, and matching it to existing or creating new record objects without human intervention. The matching engine autonomously determines match scores, applies filtering rules, and updates the system of record, eliminating manual data entry while maintaining accuracy through configurable matching criteria and validation rules
Solution Approach 2:
The system replaces the mechanical process of manual data entry with an automated information processing mechanism. The matching engine uses parsing techniques and algorithmic matching to extract and validate data from electronic activities, substituting human manual operations with automated computational processes that are both faster and equally accurate when properly configured
3Quantity of substance
If all electronic communications are processed into the system of record, then the system contains comprehensive data, but the volume of data to be managed increases significantly
Solution Approach 1:
The system applies segmentation by dividing electronic activities into distinct object types (contacts, accounts, opportunities, etc.) with specific matching rules for each type. The matching engine segments the processing workflow into parsing, matching, filtering, and record creation/update stages, making data management more organized and manageable while maintaining comprehensive data coverage across different business entities
4Measurement precision
If matching rules are made more stringent to improve match accuracy, then fewer false matches occur, but the number of record objects that need to be created increases
Solution Approach 1:
The system applies dynamics by making match score thresholds and filtering criteria configurable and adaptable. The matching engine can dynamically adjust the stringency of matching rules based on object type, data source reliability, and business requirements. This allows the system to maintain high match accuracy for critical records while being more permissive for less critical data, optimizing the balance between accuracy and record creation volume
Data Source
AI summary
Methods, systems, and storage media for generating new record objects based on electronic activities are disclosed. Example implementations may: access a plurality of electronic activities; access a plurality of record objects; parse an electronic activity of the plurality of electronic activities; determine, responsive to parsing the electronic activity, that the electronic activity is to be matched to one or more record objects; determine for each candidate record object that a match score indicating a likelihood of electronic activity being matched to the candidate record object is below a threshold; determine an object type of a new record object to generate based on one or more participants of the electronic activity; generate the new record object of the determined type; and store in a data structure an association between the new record object and the electronic activity.