Methodology

Why we built Talent Trust.

The screen industry runs on tight schedules, high stakes and thin margins. Yet there is no consistent way to see how a production is holding together while it is still in motion. Traditional oversight is retrospective. Formal reporting captures issues only after they have escalated. Insurance, bonds and commissioning conversations rely on assumptions that no live data currently backs up.

The answer

Talent Trust built the measurement layer that sits in between. Live, anonymous, structured workforce data that shows where retention, delivery and workforce stability are being tested, in time to act.

The core idea

Workforce experience is a leading indicator of financial performance. Our methodology makes it measurable.

The mechanism

How the methodology works in practice.

01 · Capture

Anonymous survey response

A single QR code. No app, no login. Every response under a minute.

02 · Aggregate

Pooled at source

Responses are anonymised before analysis. No individual is identifiable at any point.

03 · Detect

Category signal surfaces

Structured measurement across preparation, support, safety, conduct and inclusion. The five conditions that in our data predict retention intent and workforce stability.

04 · Act

Early correction

Leaders act on live signal before pressure compounds into delay, escalation or turnover.

The four principles

How the methodology holds together.

Talent Trust rests on four principles, design choices that determine what kind of signal the service produces, who can act on it, and when. Each one is non-negotiable.

01

Anonymous by design.

Honest data requires safe participation. Without it, the signal is unreliable.

  • Responses are anonymised at source and aggregated before analysis
  • No individual or small group can be identified in any output
  • Structured to encourage participation, which is the condition for meaningful signal
  • The focus is the workforce environment, not any individual within it
02

Structural insight.

The categories capture the structural conditions of a production, not the individuals within it. This is what makes the data actionable and defensible.

  • Signals surface at the level of preparation, support, safety, conduct and inclusion
  • Pressure is visible before it manifests as incident, complaint or attrition
  • Trends are compared across teams, departments and, where the dataset supports it, across productions
  • Findings support proportionate, early action rather than late-stage escalation
03

Upstream of risk.

Proactive risk visibility, not retrospective reporting. Early sight of pressure is the condition for early action.

  • Provides live visibility before formal issues, complaints or claims arise
  • Supports proactive risk management and evidenced duty of care
  • Reduces reliance on retrospective reporting, escalation and after-the-fact investigation
  • Gives leaders, and the parties they answer to, confidence they are seeing the full picture
04

Grounded in evidence.

The five categories, the TT Index and the traffic-light bandings are derived from an accumulating multi-production dataset. Findings are benchmarked, not asserted.

  • Validated across multiple productions, formats and territories
  • The TT Index is benchmarked against our multi-production dataset, not against an internal target
  • Category weightings within the Index are derived from observed data, not from expert opinion alone
  • The methodology is updated as the dataset grows, and the update logic is transparent to clients

See it in practice

Want to see how this could apply to your next production?

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