Preqin~4 min read
What would make fund managers share performance data again?
Fund managers had stopped contributing performance data. The blocker was not the form; it was asking them to share numbers they were embarrassed by.
- Role: Product strategy and design
- Timeline: 3 weeks
- Team: 1 PM, 1 engineer, head of engineering, me
- Impact: 5% → 15% retention
- Platform: Web
What was going on?
Preqin is only as valuable as its data, and fund managers had stopped contributing performance figures. Everyone paying for the platform got less back.
Research showed that collection was not the problem. Partners were not withholding numbers because submission was tedious. They were withholding numbers they were embarrassed by, and better upload tooling would not change that. The work had to move from collection to exchange.
My first experiment, a leaderboard, backfired. The version that worked kept the comparison and removed the identity.
What I owned
I owned
- Research with partners at large and mid-market private equity funds
- Reframing the problem from collection to exchange
- Both experiments: the leaderboard that backfired and the quartile ranking that replaced it
- Keeping the form to what someone could complete in one sitting, despite a push for 32 fields
Decided with others
- Getting Legal and Sales to the same answer: I brokered it; they made the call
- The cohort floor beneath anonymization, with the head of engineering
The problem
The platform depended on data that was going stale.
Fund managers were not contributing performance figures, reducing the platform’s value for every subscriber who relied on it. Revenue and renewals were exposed.
The obvious diagnosis was friction: submission was tedious, so make it easier. Research pointed somewhere else.
That last question changed the brief: what does a fund manager get for sharing a number that might embarrass them?
We considered six options. Three tackled collection, an automated scraper, upload flow, and parser, and would have been easier to engineer. Three tackled exchange: a leaderboard, trust signals, and an inline form. The collection options solved a problem nobody had.
The first decision
I tried a leaderboard. It was wrong.
I considered a public leaderboard, a private benchmark against peers, and no comparison at all. Competition seemed like the obvious lever when people would not contribute, and I was confident enough to ship a named ranking with prompts to submit benchmark data first. I did not think it needed mitigation, although a product asking for trust was about to publish the information partners guarded most closely.
Partners saw exposure rather than motivation: “This doesn’t reflect who we are.” We removed it.
The second decision
Could comparison work without naming names?
The choices were a named ranking, which Legal refused; no comparison, which Sales refused; or an anonymized quartile position. Legal would not accept anything identifiable, while Sales would not accept something a partner could not use for comparison. Both positions were reasonable but incompatible as stated.
We used an anonymized quartile position in an inline field. Legal could accept it because nobody was identifiable, and Sales could sell it because partners still had something to measure against. Anonymity holds only while the peer set is large enough to hide in: slicing by fund type, geography, and vintage can leave four funds in a quartile, allowing everyone in it to work out who is bottom. The feature then stops protecting people at exactly the specificity that makes the data useful. A minimum cohort size, rather than anonymization alone, is the control, and someone must keep deciding where that floor sits as the dataset grows. Both sides said yes, and the timeline went from five sprints to two.
The third decision
Would anyone finish 32 fields?
The options were the 32 fields stakeholders requested, a phased multi-session form, or a short inline form. Every field had an internal advocate and a report it supported; the issue was their cumulative cost.
We used a short inline form with trust signals beside it, limited to what someone could finish in one sitting. The data model stayed thinner than the business wanted, and the argument returned every quarter. We would watch what people completed and extend the form from evidence rather than requests. We never tested the 32-field version, so this choice rests on reasoning rather than comparison.
What changed?
Retention
up from 5%
Due diligence
down from 10 months
Both figures held through 2021, when I left Preqin. The diligence figure deserves more scrutiny than any other number here. Cutting an institutional diligence cycle from ten months to four is a large claim to attach to a data-contribution feature, and the link between them, better coverage leading to fewer manual verification steps, is reasoning rather than measurement. Treat retention as the result of this work. Treat diligence as directional until it can be sourced.
The obvious lever made people feel exposed
Competition was the obvious lever, right up until a product asking for trust made its partners visible. I built the leaderboard first because people would not contribute, and I was confident it would work. It backfired.
The quartile ranking that worked used the same idea without identity. That fix was available before the leaderboard: research had already said, “if my numbers aren’t great, I don’t want anyone to know.” I shipped it anyway.
The retention figure does not separate recovery from damage, so I do not know what the leaderboard cost. The useful unresolved judgment is whether the later retention result overcame the harm or merely concealed it.
