The give-to-get UGC mechanic — Glassdoor's salary disclosure flywheel
Outcome: When user-generated taboo data is the asset, give-to-get reciprocity converts free-riders into contributors and produces a self-reinforcing supply flywheel.
“Their innovation after kind of hand-cranking it with surveys was give-to-get. You show me yours, I will show you mine. We will give you a little taste, but if you want any more data, you have to share your salary and your title and your company — promise you will be anonymous — and do a company review.”
- 1
Identify the taboo data asset
Pick a category of data that is socially sensitive (salary, attorney quality, medical pricing, landlord behavior). The taboo factor is what creates the Provocation Marketing surface.
- 2
Build the disclosure gate
Allow users to see a small taste of aggregated data without contributing. Lock the deeper data behind a contribution gate that requires their own data point plus structured fields (title, company, role, etc) plus an anonymous review.
- 3
Guarantee anonymity with explicit protocols
Promise anonymity in copy. Implement protocols for unique-position contributors (e.g., the only CFO at a small company) — aggregate within larger buckets or suppress display until N>=3.
- 4
Amplify with public-figure data
Layer review of public-facing roles (CEO performance, partner reviews, professor reviews) on top of the anonymous individual data. Public-figure provocation generates news cycles; the anonymous data generates the trust.
- 5
Wire the data into Provocation Marketing distribution
Feed constantly-changing aggregated data (median salary by city/role, CEO approval ratings) to local news + LinkedIn / X. The data is now the marketing.
- 6
Monitor contribution rate vs view rate
If view-to-contribute ratio drops below threshold, tighten the gate (less free taste). If contribution rate is low at signup, loosen the friction (fewer required fields).
Stop or pivot when
- →Free-view-to-contribution ratio >1:20 means the gate is too loose; <1:5 means too tight
- →Re-disclosure rate (users updating data each year) <20% means the trust loop is broken — audit anonymity
Scripts
Before you start
- · A category where the data is socially sensitive enough to be a Provocation Marketing surface
- · Anti-deanonymization protocols engineered before launch
- · A patience window of 6-12 months for the flywheel to compound