How? By issuing metrics that represents proportional exposure on the site - i.e. metrics: Followers (sim. to YT subs) and Data Quality Score (sim. to Uber star-rating)

Data Quality Score & Calculation
"Process" - 1. We have a repository that collects incoming tear sheets

2. AI / ML scans tear sheets and inputs Preqin profile-related tear sheet data (recent investments, performance metrics, etc.) and updates profile

3. 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

4. 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

Followers are generated by users "subscribing" to their profile ; the more followers, the more exposure their profile receives

Since followers / DQS metrics are publicly shown on their profile, networking opportunities would be efficient and help develop platform trust