Creative demand formula and carry rate

TL;DR

• New ads needed = (target spend − current spend × carry rate) ÷ expected spend per new ad. Run it monthly, or for the whole year from a forecast.¹
• Typically 70–75% of current spend carries into next month by itself, so new creative has to fill the other 25–30%. Observed range: 65–122%.²
• Same spend, different health: two brands at $200K/month on Meta might need 40 vs 200 new ads a month, depending on their creative score.³
• Pull three levers in order: mine past winners → make better ads (product × persona × pain point × URL) → run the catalog layer separately.⁴⁵⁶

Details

Inputs (CTC definitions).¹⁷⁸

• Current spend — projected end-of-month spend: month-to-date plus the remaining run rate, adjusted for day-of-week pacing and planned spikes.
• Carry rate — for every ad active in the trailing 60 days, whether it survives into next month and how much of its spend it keeps.
• Expected spend per new ad — the average first-month spend of new ads, across the whale / winner / testing / dust tiers (Creative hit rate — what the spend data says and why Barry Hott calls it misleading).

Tiers carry differently. Whales have 82% survival and 70% spend retention; winners 71% survival and 90% retention. So each ad isn’t given the same carry.⁹ Brands that push budget onto proven winners before ordering new production carry 85–100%+ of prior spend.²

Creative score — five metrics.^10111213

Metric	Definition	Drives
Evergreen share	% of ads running >30 days	survival → carry rate
Spend degradation	avg week-over-week spend change after launch (one brand: −39%/week)	spend retention
Ad concentration	% of spend in top 5 ads (whale-heavy accounts carry ~59%, distributed 64–92%)	resilience; out-of-stock or lost creator rights can sink a concentrated account
Zero-revenue rate	% of ads with zero revenue in the period	expected spend per ad — a 65% brand needs 2–3× the ads of a 30% brand
ROAS degradation	avg week-over-week efficiency change (5% = winners age well)	value of new ads over time

Three levers.

1. Mine historical winners before briefing anything new: revive last year’s and the last 30 days’ top ads, re-enable paused winners, and send existing creative to new landing pages.⁴
2. Make better ads, not just more. Each brief = product + persona + pain point + destination URL. Spread briefs across video and static formats and across distinct entity IDs.⁵
3. Catalog layer — carve it out of the formula. Catalog ads carry at ~100% while the feed persists. They are often the highest-ROAS line and are rarely briefed. Run them broad with Advantage+ catalog as an acquisition engine, not only as retargeting. Test dynamic overlays and catalog video.⁶¹⁴

Monthly workflow (5 steps).¹⁵¹⁶

1. Total demand — run the formula with the 60-day carry rate.
2. Split moments vs evergreen. Score past calendar moments by promo and LTV, size them by expected incremental spend, and don’t starve the evergreen pipeline.
3. Assign products — follow the moment, or for evergreen use the product matrix and weighting.
4. Angles × treatments — format split (video/static/UGC) per angle.
5. Assign producers and counts; keep ads flowing into the account.

Disagreements / caveats

• The numbers come from CTC’s Statlas dataset (its own clients) and are presented as a methodology the agency sells. The tier cut-offs ($1K, $5K) are absolute, so they mean different things for small vs large spenders.
• Barry Hott argues against using any hit-rate tier as a goal; he would only use spend (Creative hit rate — what the spend data says and why Barry Hott calls it misleading).
• Compare with production capacity: a formula that asks for 200 ads still fails if the team can make 20 (Brand reality — production capacity, true creative cost and team alignment).

See also

• Creative hit rate — what the spend data says and why Barry Hott calls it misleading · Is ad fatigue real? Creative longevity vs refresh cadence · Planning Meta creative volume, budget and team
• Adrianne (Common Thread Collective)

Footnotes

1. The Math Behind Creative Hits (With Real Spend Data) @ 07:34 — Adrianne (CTC) ↩ ↩²

2. The Math Behind Creative Hits (With Real Spend Data) @ 11:45 — Adrianne (CTC) ↩ ↩²

3. The Math Behind Creative Hits (With Real Spend Data) @ 17:43 — Adrianne (CTC) ↩

4. The Math Behind Creative Hits (With Real Spend Data) @ 18:28 — Adrianne (CTC) ↩ ↩²

5. The Math Behind Creative Hits (With Real Spend Data) @ 19:16 — Adrianne (CTC) ↩ ↩²

6. The Math Behind Creative Hits (With Real Spend Data) @ 20:12 — Adrianne (CTC) ↩ ↩²

7. The Math Behind Creative Hits (With Real Spend Data) @ 08:21 — Adrianne (CTC) ↩

8. The Math Behind Creative Hits (With Real Spend Data) @ 10:02 — Adrianne (CTC) ↩

9. The Math Behind Creative Hits (With Real Spend Data) @ 10:54 — Adrianne (CTC) ↩

10. The Math Behind Creative Hits (With Real Spend Data) @ 13:33 — Adrianne (CTC) ↩

11. The Math Behind Creative Hits (With Real Spend Data) @ 14:21 — Adrianne (CTC) ↩

12. The Math Behind Creative Hits (With Real Spend Data) @ 16:04 — Adrianne (CTC) ↩

13. The Math Behind Creative Hits (With Real Spend Data) @ 16:56 — Adrianne (CTC) ↩

14. The Math Behind Creative Hits (With Real Spend Data) @ 22:03 — Adrianne (CTC) ↩

15. The Math Behind Creative Hits (With Real Spend Data) @ 23:38 — Adrianne (CTC) ↩

16. The Math Behind Creative Hits (With Real Spend Data) @ 24:31 — Adrianne (CTC) ↩