171M View Short-Form Programmatic Video Experiment

Situation

We created and operated a scaled network of social accounts and fan pages for label releases and brands. We automated short‑form posting with audio tagging and programmatic video generation.

Sector: Media/Entertainment/CPG automation.

Market/product: Labels, artists, and brands need scaled distribution on TikTok/Shorts/Reels. Value is attention and conversions, constrained by platform ops, compliance, and creative throughput.

Hard numbers (baseline → result; timeframe):

• Project‑attributed views: 0 → 171,000,000+ (≤ 4 months).

• Average run‑rate: ≥ 24.43M views/month, ≥ 5.62M views/week, ≥ 0.80M views/day (derived).

• Enterprise reach: Worked with major label enterprises, managed fan‑page programs tied to releases, and grew brands.

Product & engineering owned by me:

• Website: I designed and shipped the marketing site end‑to‑end.

• Reporting backend & client portal: I built the backend app and dashboards for near‑real‑time visibility and QA.

Constraints/trade‑offs, handled:

• Scale vs. safety: We followed platform protocol for account setup, identity, and rate limits. No gray‑area automation.

• Build vs. buy: We combined APIs and programmatic video for throughput; avoided brittle third‑party “auto‑poster” tools to protect accounts.

Execution

Strategy

Hypotheses

1. Unit time drives margin.

2. Right channel mix beats ads. Founder‑led sales + partner referrals

3. Value‑based pricing.

Options considered

• A: Third‑party schedulers (fast, but API risk and limited features). Rejected due to ban risk and lack of control.

• B: Full custom app with headless browser “API.” Rejected for compliance risk and brittle maintenance.

• C: Phone‑managed posting with automation (chosen). Physical device control, rate‑limit compliance, and resilient ops.

• D: Keep paid ads heavy. Rejected given CAC pressure and cash constraints. Shifted to partner channels.

Results (Business Impact)

Reach at speed.

• 171,000,000+ views in under 7 months.

• Derived run‑rate: ≥ 24.43M views/month, ≥ 5.62M views/week, ≥ 0.80M views/day.

Quality and safety.

• Accounts were set up under proper protocol, respecting platform policies.

• No reliance on gray‑area automation.

• Fan‑page governance maintained tone, naming, and disclosure patterns.

Interpretation.

• Release windows benefit from dense posting across many compliant accounts.

• Audio tagging improved discoverability and re‑use, compounding views and trending.

• A reporting backend shortened the detect‑and‑fix loop on posting health.

External benchmark framing (EMV, clearly labeled Assumption).

• Earned Media Value (EMV) can approximate view value:

  • EMV = (Views ÷ 1,000) × CPM.

  • With 171,000,000 views, EMV ranges by CPM:

    • At $3 CPM → $513,000; at $6 CPM → $1,026,000; at $8 CPM → $1,368,000.

  • Assumption: CPM range $3–$8 reflects short‑form norms; replace with your measured value per view if available.

We did not disclose revenue, CAC, or costs here. The Appendix shows formulas and scenario tables.

FAQS

1. Prove the 171M+ views are program‑attributed.

   We counted platform views only on posts produced by the program. Pre‑launch baseline for program‑attributed views was 0. Excluded organic posts we didn’t create.

2. How do you separate brand follower growth from baseline trends?

   We compute net new followers on target handles over the window then subtract the pre‑program trend (e.g., last 4–8 weeks). The remainder is program lift. Method in Appendix.

3. Why use AI‑generated visuals—brand safety risk?

   We used brand style guides, negative prompts/exclusions for sensitive content, and human review. Disclosures were applied where required by policy.

4. Why not rely on one main brand account?

   Single hubs cap throughput. Governed account networks and fan pages improve discovery paths and conversation density.

5. What prevents rate‑limit or ban issues?

   Strict setup protocols, cadence caps by account maturity, and duplication controls. We did not use gray‑area automation.

6. How much did you personally build?

   I designed and shipped the website and built the reporting backend/portal. I owned the program’s guardrails and measurement.

7. What’s the value beyond views?

   For labels: release‑window exposure. For brands: views plus follower growth. We translate to EMV and CPF/LTV‑per‑follower (formulas provided).

8. How do you avoid duplicate content fatigue?

   Template diversity, AI‑assisted visual variants, caption variation, and per‑account cadence policies.

9. What if a platform changes policy?

   Our orchestration respects protocol by design. We can dial cadence, adjust formats, or pause specific tactics without rebuilding.

10. What would you add next?

    Follower quality scoring, geo‑mix targets, and pre‑flight audio/tag checks before scale.

APPENDIX

Website-build.pdf

A) Observed vs. Assumptions vs. TODO

B) Measurement methods

• Program‑attributed views: Sum platform views for posts created by the program; exclude non‑program posts.

• Brand follower growth: Net followers gained on target brand handles minus projected baseline based on pre‑program trend.

• Account health: Flags/bans per 1,000 posts; cadence breaches; duplication incidents.

C) Formulas (decision‑useful)

• EMV ($) = (Views ÷ 1,000) × CPM.

• CPF ($/follower) = Program Cost ÷ Net Followers Gained (post‑baseline).

• Brand LTV/follower ($) = Conversion Rate × AOV × Gross Margin × Repeat Factor.

• ROI (%) = [(EMV or Revenue Attributed − Program Cost) ÷ Program Cost] × 100.

• Contribution Margin (%) = (Revenue − Variable Costs) ÷ Revenue.

D) Data dictionary

• Program‑attributed views: Platform‑reported views on posts produced by ClipScale.

• Fan‑page network: Governed cluster of accounts that amplify releases.

• Audio tagging: Associating posts with the correct audio object for discovery.

• AI‑generated visuals/content: Creative variants produced with modern AI tools under brand safety rules.

• CPF: Cost per net new follower after baseline subtraction.