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Neo Brainstorm (Preqin Search Topics (Fund Profiles, League Tables, Deal…

- - - - Similar to Fund Manager "Process"
- - - - Profile data is up-to-date
      - Appropriate contacts would be up-to-date
      - Since followers / DQS metrics are publicly shown on their profile, networking opportunities would be efficient and help develop platform trust
  - - - Data Quality Score & Calculation
        "Process" - 1. We have a repository that collects incoming tear sheets
      - AI / ML scans tear sheets and inputs Preqin profile-related tear sheet data (recent investments, performance metrics, etc.) and updates profile
      - After update, ML (w/ "supervised learning" can be implemented to determine Data Quality Score (DQS). Data Quality Score is calculated by different weighted variables: a) frequency of updated information, b) amount of data contributed, c) amount of relevant data contributed, d) We can offer guidelines to the user on how to attain a high DQS score
      - What will make them keep coming back? DQS is a depreciating metric over time (sim. to an options contract). Since DQS is a driver of profile visibility, funds managers need to continue updating information.
      - Extra incentive?: To further drive data contribution, if DQS attains a certain score - i.e. 4 out of 5, we can provide additional service (expedited client-side assistance, exclusive access to additional reports, custom presentation tools etc.).
        However, If DQS dips below 4 out of 5, then the user will lose their additional services
        If additional services are worth maintaining a high DQS, then the incentive plan can reinforce customer loyalty to continue data contribution