Lead Quality Score for Privacy-Preserving Lead Prioritization
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Solution Overview
Problem
Sponsors face challenges in effectively evaluating and prioritizing leads due to limited information and resource constraints, often resulting in high-quality leads going unpursued while low-quality leads are pursued, exacerbated by privacy restrictions that prevent sharing of user data necessary for accurate assessment.
Innovation Solution
An online system scores leads based on purchase capacity and purchase intent, using user activity and historical data to determine a lead quality score without disclosing sensitive information, allowing sponsors to prioritize leads without breaching privacy and utilizing all available resources for lead evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the online system shares detailed user information with the sponsor for lead evaluation, then the sponsor can accurately assess lead quality, but user privacy is compromised
Solution Approach 1:
The patent introduces an intermediary scoring system that processes user information on behalf of the sponsor. The online system calculates lead quality scores based on user data without directly sharing the underlying personal information with the sponsor. This intermediary mechanism enables accurate lead assessment while maintaining privacy boundaries, as the sponsor receives only the aggregated score rather than raw user data.
2Reliability
If the sponsor pursues all generated leads, then no high quality leads are missed, but resources are exhausted pursuing low quality leads
Solution Approach 1:
The patent transforms the lead evaluation process by introducing a quality score parameter that quantifies lead potential. Instead of treating all leads equally or relying on subjective judgment, the system calculates an objective quality score based on user data and historical patterns. This parameter change enables the sponsor to efficiently prioritize leads by score threshold, ensuring high-quality leads are pursued while low-quality leads are deprioritized, optimizing resource allocation.
Solution Approach 2:
The patent replaces the manual, resource-intensive process of individual lead assessment with an automated scoring system. The online system automatically calculates quality scores for all leads using algorithms that analyze user information and historical data. This substitution of automated computation for manual evaluation dramatically improves productivity while maintaining or enhancing assessment accuracy.
3Quantity of substance
If lead campaigns focus on generating high numbers of leads, then the volume of potential customers increases, but the quality of leads decreases and assessment capability is insufficient
Solution Approach 1:
The patent implements preliminary scoring of leads immediately upon generation, rather than attempting to assess quality after the fact. The online system calculates quality scores for leads as they are created during lead campaigns, enabling the sponsor to filter and prioritize high-quality leads from the outset. This preliminary action allows the sponsor to maintain focus on quality even when generating large volumes of leads, as the scoring system processes leads automatically without requiring subsequent manual review.
Data Source
AI summary
Leads may be generated for content sponsors based on expressions of interest by users, the users being users of an online system. These leads typically require time or expense from a sponsor to follow up on the lead. The online system scores leads for sponsors to prioritize responding to the generated leads. The scores may include a purchase capacity score and a purchase intent score. The purchase capacity score estimates a user's ability to spend an amount specified by the sponsor for the product, and the purchase intent score estimates a user's likelihood to convert or be interested in the sponsor's product. The scores may be combined to provide a single lead quality score for a user that obscures the source of the online system's prediction to the sponsor.