Contact Interest Scoring for Research Dissemination
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Solution Overview
Problem
Sell-side firms face challenges in efficiently disseminating research work products to contacts who are interested, as existing methods lack a systematic approach to determine the interest levels of contacts in research items produced by multiple analyst teams across various sectors and regions.
Innovation Solution
A computer-based system that calculates interest scores for contacts based on data such as accessed research work products, communication interactions, topics, and analyst team affiliations, using a propensity model to predict the likelihood of contacts reading specific research items, and then distributes research work products accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If research work products are distributed to all contacts, then coverage is maximized, but dissemination efficiency decreases due to lack of targeting
Solution Approach 1:
The system segments contacts into distinct groups based on their interest profiles derived from communication interactions and work product access patterns. This segmentation enables targeted distribution of research work products to specific contact segments, improving dissemination efficiency by ensuring relevant content reaches the right audience without wasting resources on uninterested contacts.
Solution Approach 2:
The system performs preliminary analysis of contact behavior data, communication interactions, and work product access patterns to generate interest profiles before distribution occurs. This preliminary action allows the system to pre-determine which contacts are most likely to engage with specific research work products, enabling proactive targeted distribution rather than reactive broad casting.
2Measurement precision
If interest scores are computed for all contacts and work product items, then targeting precision improves, but computational complexity increases
Solution Approach 1:
The system leverages contacts' own behavior data, communication interactions, and work product access patterns to automatically generate their interest profiles without requiring manual input or external data sources. This self-service approach enables precise interest measurement while keeping the system relatively simple, as the contacts' own actions provide the data needed for scoring.
Solution Approach 2:
The system transforms raw behavioral data into standardized interest profile parameters through computational models. By converting complex behavioral patterns into simplified interest score parameters, the system achieves precise measurement while managing computational complexity through efficient parameter transformation rather than complex analysis of every raw data point.
3Reliability
If research work products are targeted only to high-interest contacts, then engagement quality improves, but quantity of distributed products decreases
Solution Approach 1:
The system applies local quality by tailoring the distribution strategy to different contact segments based on their interest profiles. Rather than uniform treatment, contacts with higher interest scores receive more targeted and frequent distribution, while contacts with lower interest scores receive less frequent or different types of products. This local differentiation improves engagement reliability for targeted contacts while maintaining overall distribution volume through segmented approach.
Data Source
AI summary
Computer-based systems and methods that determine interest profiles of contacts of work product. The contact interest profiles may indicate the contacts' interests in particular topics of research work product and/or analyst teams within the research department that produced the research work product. The contacts' interest profiles may be determined based on research work product accessed by the contacts according to topic and/or analyst team that produced the research work product, the interaction of the contacts with the research department, and any other reliable information indicative of the contacts' topic and/or team interests. Also, research work product items produced by the entity may be rated for each contact based on, for example, the topic(s) of the research work product items and the analyst teams that produced the research work product items, as well as the contacts' topic and/or analyst team interests.