Contact Importer With Affinity Scoring And Incentives
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Solution Overview
Problem
Users of web platforms and content management systems often fail to recognize the benefits of referring new users and do not prioritize their contacts for referral requests, leading to a need for a system to harvest and process contacts to maximize the likelihood of successful referrals.
Innovation Solution
A contact importer functionality that leverages user contacts to find potential new users by initially culling non-human contacts and assigning an affinity score to each contact, offering incentives for sending or approving invitations based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually send referral requests to their contacts, then they can control the referral process, but the process is time-consuming and users do not prioritize contacts by likelihood of acceptance
Solution Approach 1:
The system performs preliminary actions by automatically importing contacts from external sources, filtering non-human contacts, and pre-calculating affinity scores before the user needs to send referrals. This prepares the contact list in advance, allowing users to quickly review and send referrals without manual processing time.
Solution Approach 2:
The system serves itself by automatically managing the entire contact import and processing workflow without requiring user intervention. It autonomously imports contacts, filters invalid entries, calculates affinity scores using multiple tests, and presents prioritized results to the user, freeing users from time-consuming manual contact management.
2Productivity
If the system imports and processes all user contacts, then it maximizes potential referrals, but the complexity of processing and analyzing contact data increases
Solution Approach 1:
The contact processing system is segmented into distinct modular components: contact import module, non-human contact filter, affinity test module, and scoring module. Each component handles a specific aspect of processing, making the overall complex system manageable and maintainable while still processing all contacts comprehensively.
Solution Approach 2:
The system performs multiple affinity tests on each contact to comprehensively evaluate referral potential, going beyond what a simpler system would do. This excessive action ensures high accuracy in identifying likely acceptors, maximizing productivity despite the increased processing complexity.
3Measurement precision
If the system assigns detailed affinity scores based on multiple tests, then it accurately prioritizes contacts, but the computational resources and processing time required increase
Solution Approach 1:
The system performs affinity tests and score calculations in advance before the user needs to send referrals. By pre-processing contact data and establishing affinity scores beforehand, the system reduces real-time computational energy requirements while maintaining high measurement precision.
Solution Approach 2:
The system uses multiple different affinity tests that evaluate various parameters of contact relationships (email domain matching, social connections, interaction history). By changing and comparing multiple parameters, the system achieves precise measurement of contact affinity while distributing computational load across different evaluation criteria.
4Productivity
If users are offered incentives for sending referrals, then referral acceptance rates increase, but the system must manage and distribute incentives which adds operational complexity
Solution Approach 1:
The system implements feedback by tracking which contacts receive invitations and whether they convert to new users. This feedback loop allows the system to measure the effectiveness of different incentive strategies and adjust incentive distribution accordingly, managing complexity through data-driven decision making.
Solution Approach 2:
The system offers different incentive levels based on the calculated affinity score of each contact. High-affinity contacts receive higher incentives, while lower-affinity contacts receive smaller or no incentives. This parameter-based approach to incentive distribution increases conversion rates while managing operational complexity through automated, score-based decision making.
Data Source
AI summary
Embodiments are provided for importing contacts. A contact importer leverages the various contacts associated with a user of a content management service to find potential new users and/or customers. A contact importer may run on one or more devices of a user associated with an account on a content management system, and import various contacts of that user to a contact list. The list may be culled to weed out non-human contacts, and processed so as to assign an affinity score to each contact, expressing a degree of affinity to the user. Incentives may be offered to the user for either sending or approving an invitation to a contact to register with the content management system, paid upon the invitee successfully registering. Different incentives may be offered for an accepted invitation from various contacts or classes thereof.