Operating ProcedureExecution layer

Clip Volume Capacity Model (Retention)

A checklist with 7 steps: Pull the last 90 days of clip output per client and per editor seat.

By InnovaAI ResearchPublished

What are the steps?

checklist

Clip Volume Capacity Model (Retention)

  1. 01

    Pull the last 90 days of clip output per client and per editor seat

    Count delivered clips, revision rounds, and publish dates from the scheduler rather than from memory. A retainer that quietly doubled its clip count without a scope change is the first thing this model surfaces.

  2. 02

    Time three representative clips end to end, from raw upload to client-ready export

    Include the human pass: hook rewrite, caption style check, and any b-roll swap. Auto-clipping in OpusClip or Klap may take minutes, but the review loop is what actually consumes the seat.

  3. 03

    Separate machine minutes from human minutes in a single line per client

    Rendering, transcription, and reframing scale near-linearly with volume; judgment does not. Mixing the two hides which client is unprofitable.

  4. 04

    Set a per-seat ceiling and a per-client floor before the next renewal conversation

    A workable starting point is 40 to 60 finished clips per editor per week when captions and reframing are automated, dropping sharply once custom motion graphics enter the brief.

  5. 05

    Flag any client whose clip volume grew more than 30 percent without a matching retainer adjustment

    Bring the delta to the account lead as a scope conversation, not a complaint. Volume creep is normal in short-form work and is easiest to correct at renewal.

  6. 06

    Stress-test the ceiling against a peak week, not an average week

    Launch weeks and product drops are when agencies break promises. Model the worst realistic week and confirm the roster can absorb it without borrowing editors from other accounts.

  7. 07

    Re-run the model quarterly and after any tool change

    Switching from Vizard to a different repurposing stack can shift throughput in either direction. Re-baseline the numbers instead of carrying forward old assumptions.