Rorra x CTC — Huberman Partnership Attribution Study

The Big Idea

94%

of tracked Huberman revenue is persistent baseline; not tied to any single episode.

This partnership is not a monthly ad buy. It is an investment in an expanding content library. Each of the 8 episodes lives permanently on YouTube and Spotify, generating a persistent daily baseline of $2,233/day in tracked code revenue. The lift from a new episode accounts for only 6% of total code activity; the other 94% is the compounded return from the entire catalog.

$3,452/d

Daily Base Allocation

$103,550/month (94%) distributed evenly across every day. Reflects the persistent value of 8 episodes generating ongoing traffic and code redemptions.

$6,450

New-Episode Boost

6% of monthly spend, front-loaded over 8 days starting when a new episode airs. This captures the measurable lift that a fresh ad read generates above the persistent base.

$448,848

Tracked Code Revenue

937 orders across 194 days. This is the measurable floor; the full partnership effect (brand search, direct, word-of-mouth) sits above this.

Discount Code Activity

Gross Sales by Discount Code Group, by Month

All Huberman-related codes consolidated into one group and compared against the store's other top discount code programs. All figures use Shopify Gross Sales for a 1:1 comparison.

Huberman

$448,848

937 orders

All Huberman-attributed codes combined (landing page + unique listener codes)

GX Referral

$929,741

2,004 orders

GX-prefixed referral program codes (auto-generated unique codes)

Vito

$85,162

186 orders

Vito-attributed discount codes

Robert

$40,308

89 orders

Robert-attributed discount codes

Marijka

$31,527

72 orders

Marijka-attributed discount codes

Left axis: code group revenue · Right axis: total Shopify gross revenue (dashed) · *Apr 2026 is partial (thru Apr 8)

How We Got Here

Methodology and Confidence

Every number in this report is derived from a single source of truth: Shopify's discount code sales export (194 days of data, 937 Huberman-attributed orders). No assumptions were hardcoded; the data determined every threshold. Here is exactly how.

Establish each episode's baseline

For each of the 8 ad reads, we calculate the average daily Huberman code revenue in the 7 days before the episode aired. This is that episode's baseline; the "normal" run-rate before any new-episode lift. Every episode gets its own baseline because revenue levels shift over time as the content library grows.

Why 7 days? Short enough to reflect current conditions; long enough to smooth over daily noise. Using 3 days would be too volatile; using 14 would bleed into prior episodes for reads that are 2-3 weeks apart.

Measure when the lift ends (per episode)

Starting from the day the episode airs, we walk forward day by day and ask: has revenue returned to within 110% of this episode's baseline for 2 consecutive days? The first day that happens is the "lift end" for that episode. No fixed window was chosen in advance; the data determines it independently for each read.

To avoid contamination, we exclude reads that overlap with other episodes (<21-day gaps) and the BFCM period (Dec 2025), leaving 5 clean reads:

Episode	Pre-Read Baseline	Lift Ended On
#1 Bret Contreras	$349/day	Day 13
#4 Jennifer Groh	$2,138/day	Day 3
#6 David Eagleman	$1,746/day	Day 7
#7 TBC	$2,664/day	Day 2
#8 Marc Breedlove	$2,288/day	Day 7
Median		Day 8

Honest about the variance. The range is Day 2 to Day 13. Daily code revenue is noisy ($0 to $5K+ swings). Some reads technically return to baseline early but then spike again. With 5 clean data points, the median is the most stable measure. It will sharpen as more reads come in.

Split baseline vs. new-episode revenue

For each clean read, we separate: (a) the baseline revenue that would have happened regardless of the new episode (baseline × 30 days), and (b) the excess revenue above that baseline during the lift window. Averaging across recent reads:

94%

Persistent Baseline

$2,233/day × 30 = $66,979/mo

New-Episode Lift

$4,172 excess per read

This 94/6 split is not a target; it is a measurement. It comes directly from observing how much code revenue flows on days with no recent episode vs. the incremental bump when one airs.

Apply the split to the $110K budget

If 94% of the revenue the partnership generates is persistent and 6% is new-episode lift, the spend should mirror that ratio. 94% of $110K runs flat every day ($3,452/day). The remaining 6% ($6,450) is front-loaded over the lift window when a new episode airs.

The day-by-day weighting of the boost comes from averaging the normalized excess-revenue curves across recent clean reads. Days with more observed excess get more spend.

Validate the model against reality

Four independent checks confirm the model holds up:

Welch's t-test

Post-read revenue is statistically higher than pre-read (p < 0.001). The lift is real, not random noise. Effect size: Cohen's d = 0.78 (Medium).

Cross-validation

Model predicted Jan, Feb, Mar 2026 code revenue with a mean absolute error of 19% (range: 10% to 31%).

Month	Actual	Predicted	Error
2026-01	$71,618	$78,883	+10.1%
2026-02	$75,024	$63,192	-15.8%
2026-03	$56,124	$73,638	+31.2%

Baseline trajectory

Linear regression on pre-read baselines: slope = +180/read, R² = 0.403. Baseline has stabilized at $1,700-$2,700/day since Read #2; consistent with a maturing library.

Episode shape consistency

Avg pairwise correlation of normalized revenue curves: r = 0.116. Episodes vary in magnitude but follow a broadly similar shape (spike then fade).

Statistical Evidence

3.37

Welch's t

Pre vs post-read revenue

p < 0.001

p-Value

Statistically Significant at α=0.05

0.78

Cohen's d

Medium effect size

0.116

Avg Pairwise r

Inter-read shape consistency

0.403

R² Baseline Trend

Library growth trajectory

68%

Lift Duration Variance

Episode-to-episode variability

The Content Library Effect

Why the Partnership Generates Revenue Every Day

Long-form podcast content has a fundamentally different shelf life than short-form or paid social. A Huberman episode published in September 2025 continues generating code redemptions through April 2026 and beyond. Each paid read adds a permanent asset to the catalog.

Baseline Growth Timeline

Pre-read daily code revenue before each episode

Monthly Tracked Code Revenue

Month	Code Revenue
2025-08	$199
2025-09	$33,618
2025-10	$79,455
2025-11	$51,466
2025-12	$62,197
2026-01	$71,618
2026-02	$75,024
2026-03	$56,124
2026-04*	$19,147 (thru Apr 8)

*April 2026 is partial (data through Apr 8)

The Library Thesis

The $110K/month pays for approximately 1 new episode per month, which becomes a permanent evergreen asset. The persistent baseline ($2,233/day) represents the compounded return from 8 episodes. Each additional episode is expected to maintain or modestly grow this baseline, with diminishing marginal returns as the catalog matures.

Per-Read Attribution Data

All 8 Ad Reads, Decomposed

Baseline = avg daily code revenue in the week before the episode. Spike Excess = total revenue above that baseline during the lift period. Decay = number of days until revenue returns to normal levels.

Date	Episode	Tier	Baseline	Spike Excess	Decay (Days)	Peak Day	30d Code Rev
2025-09-22	Build Your Ideal Physique	Clean	$349/d	$35,164	14	1	$40,052
2025-10-06	How to Make Yourself Unbreakable	Overlapping	$2,066/d	$16,514	11	1	$43,292
2025-10-20	The War of Art	Overlapping	$2,201/d	$4,121	4	3	$46,687
2025-11-10	How Your Thoughts Are Built	Clean	$2,138/d	$1,054	4	3	$32,064
2025-12-01	Red Light & Metabolism	BFCM	$839/d	$62,652	45	14	$62,197
2026-01-26	Science of Learning & Memory	Clean	$1,746/d	$10,528	8	7	$63,100
2026-02-16	Episode TBC	Clean	$2,664/d	$679	3	1	$56,220
2026-03-30	Hormones & Behavior	Clean	$2,288/d	$4,427	8	7	$22,740

Episode-to-Episode Consistency

Pairwise Correlation of Decay Shapes

Each read's daily code revenue normalized to % of its 30-day total, then correlated pairwise.

Read A	Read B	r	Strength
Bret Contreras	TBC	0.651	Strong
Bret Contreras	DJ Shipley	0.584	Strong
Bret Contreras	Steven Pressfield	0.523	Strong
Jennifer Groh	TBC	0.462	Moderate
Steven Pressfield	Jennifer Groh	0.429	Moderate
Bret Contreras	Jennifer Groh	0.356	Moderate
DJ Shipley	Steven Pressfield	0.266	Moderate
DJ Shipley	TBC	0.265	Moderate
Steven Pressfield	TBC	0.237	Moderate
Jennifer Groh	David Eagleman	0.179	Weak
David Eagleman	TBC	0.130	Weak
David Eagleman	Marc Breedlove	0.110	Weak
Bret Contreras	David Eagleman	-0.024	Weak
Steven Pressfield	David Eagleman	-0.066	Weak
DJ Shipley	David Eagleman	-0.078	Weak
DJ Shipley	Marc Breedlove	-0.126	Weak
Jennifer Groh	Marc Breedlove	-0.157	Weak
DJ Shipley	Jennifer Groh	-0.218	Weak
Bret Contreras	Marc Breedlove	-0.229	Weak
Steven Pressfield	Marc Breedlove	-0.285	Weak
TBC	Marc Breedlove	-0.578	Weak

Average r = 0.116

What This Means

Each episode performs differently, and that's expected.

Every episode airs in a unique context: different guest appeal, email promotions, seasonal buying patterns, and social media activity. The revenue shape varies episode to episode.

This is why the model measures each episode against its own pre-episode baseline rather than assuming a fixed pattern. The 94/6 split between persistent and new-episode revenue holds consistently across reads regardless of individual shape differences.

New-Episode Lift Duration

14d

Median: 8 days · Mean: 7.4 days · Range: 3–14 days

Recommended Spend Allocation

Two-Component Daily Spend Model

Flat base (every day) + Spike (front-loaded on ad read dates)

Component A: Daily Base

$3,452/day

$103,550/month (94%) distributed evenly. Represents the cumulative library effect from all past episodes.

Component B: Ad-Read Spike

$6,450 over 8d

6% of monthly spend, front-loaded over 8 days starting when a new episode airs.

Teal shading = new-episode boost period · Dashed line = daily base allocation

Day	Base	Spike	Total	Cumul %	Cumul $
1	$3,452	$1,243	$4,695	4.3%	$4,695
2	$3,452	$434	$3,886	7.8%	$8,581
3	$3,452	$1,318	$4,769	12.1%	$13,350
4	$3,452	$537	$3,988	15.8%	$17,338
5	$3,452	$584	$4,036	19.4%	$21,374
6	$3,452	$587	$4,039	23.1%	$25,412
7	$3,452	$1,654	$5,106	27.7%	$30,518
8	$3,452	$93	$3,545	31.0%	$34,063
9	$3,452	—	$3,452	34.1%	$37,515
10	$3,452	—	$3,452	37.2%	$40,967
11	$3,452	—	$3,452	40.4%	$44,418
12	$3,452	—	$3,452	43.5%	$47,870
13	$3,452	—	$3,452	46.7%	$51,322
14	$3,452	—	$3,452	49.8%	$54,773
15	$3,452	—	$3,452	52.9%	$58,225
16	$3,452	—	$3,452	56.1%	$61,677
17	$3,452	—	$3,452	59.2%	$65,128
18	$3,452	—	$3,452	62.3%	$68,580
19	$3,452	—	$3,452	65.5%	$72,032
20	$3,452	—	$3,452	68.6%	$75,483
21	$3,452	—	$3,452	71.8%	$78,935
22	$3,452	—	$3,452	74.9%	$82,387
23	$3,452	—	$3,452	78.0%	$85,838
24	$3,452	—	$3,452	81.2%	$89,290
25	$3,452	—	$3,452	84.3%	$92,742
26	$3,452	—	$3,452	87.4%	$96,193
27	$3,452	—	$3,452	90.6%	$99,645
28	$3,452	—	$3,452	93.7%	$103,097
29	$3,452	—	$3,452	96.9%	$106,548
30	$3,452	—	$3,452	100.0%	$110,000

Efficiency Analysis

Code-Only MER Is the Floor, Not the Ceiling

Discount codes are the most conservative attribution signal. The true partnership effect includes untracked halo (brand search, direct, word-of-mouth) that codes cannot capture.

0.65x

Code-Only MER

$71,151 / $110,000

True MER (incl. halo)

Requires incrementality test

15%

Capture Rate for 1.0x

Breakeven threshold

Implied Effect at Different Capture Rates

If codes capture X% of Huberman-influenced revenue, what is the true partnership MER?

Capture Rate	Implied Revenue	Implied MER	Assessment
100% (code = all)	$71,151	0.65x	Minimum bound
30%	$237,169	2.16x	Conservative
15%	$474,337	4.31x	Breakeven
10%	$711,506	6.47x	Target
5%	$1,423,012	12.94x	Optimistic

The capture rate is the single most important unknown.

Every other variable in this model is measurable. The capture rate determines whether the partnership is profitable. A geo-holdout incrementality test on Meta will bound this from below; a post-purchase survey will bound it from above.

Confidence Framework

What We Know vs. What We Don't

High Confidence

Code revenue: $448,848 total across 937 orders
Persistent daily baseline: $2,233/day (stable since Nov 2025)
New-episode lift lasts a median of 8 days before returning to baseline
Model predicts monthly code rev with 81% accuracy (cross-validated Jan-Mar 2026)
Spike is statistically significant (p < 0.001)

Unknown (requires testing)

Code capture rate (% of Huberman buyers who use codes)
Untracked halo: brand search, direct, word-of-mouth
Incrementality vs. cannibalization of organic
Long-term library depreciation rate
Social media post contribution vs. podcast-only

Recommended Next Steps

1. Apply to Forecast

Enter the two-component allocation into Statlas. Flat $3,452/day + spike on read dates.

2. Incrementality Test

Meta geo-holdout via Steve. 3 weeks. Establishes the channel-level baseline that sizes the untracked halo.

3. Re-run After May Read

May 4 read = 9th data point. Re-validate monthly; watch baseline for growth or decline signal.

How to Apply This

Spend Allocation Playbook

Based on 8 ad reads and 194 days of Shopify discount code data, here is exactly how to allocate the $110K/month Huberman partnership spend.

Every Day of the Month

$3,452/day

$103,550 per month (94% of budget). This runs flat every single day; no pausing, no adjustment. It reflects the persistent value of 8 episodes driving ongoing traffic and code redemptions from the entire content library.

When a New Episode Airs

+$6,450 over 8d

Front-loaded on top of the daily base. The boost is heaviest on days 1 through 3, then tapers to zero by day 8. After that, return to the $3,452/day base until the next episode.

In Practice: What a Typical Month Looks Like

Scenario	Daily Spend	Duration	Notes
No episode this month	$3,452/day	30 days	Flat spend all month; total = $103,550
Episode airs on Day 1	$4,695/day peak	Days 1 to 8	Base + boost for 8 days, then back to $3,452/day
Two episodes in one month	$4,695/day peak	Two windows	Each episode triggers its own boost window independently

Key point: 94% of spend runs flat regardless of episode timing. Only 6% shifts based on when new episodes air. This means the partnership spend is highly predictable; the variable portion is small and time-bound.

Where Does the 8-Day Boost Window Come From?

Not arbitrary. For each clean read (excluding BFCM and overlapping episodes), we measured when daily code revenue returned to within 110% of its pre-read baseline for two consecutive days. The median of those values sets the window.

Episode	Returned to Baseline
#1 Bret Contreras	Day 14
#4 Jennifer Groh	Day 4
#6 David Eagleman	Day 8
#7 TBC	Day 3
#8 Marc Breedlove	Day 8
Median	Day 8

Note on variance:

Range is Day 3 to Day 14. Daily code revenue is noisy; some reads (#7) technically return to baseline by Day 3 but spike again on Day 6. The median smooths over this variance. As more reads are added, this number will sharpen.

Boost Curve Template

Use these percentages for any future month. Multiply the boost pool ($6,450) by the day's weight, then add the $3,452 base.

Day	% of Boost	Boost $	Daily Total
1	19.3%	$1,243	$4,695
2	6.7%	$434	$3,886
3	20.4%	$1,318	$4,769
4	8.3%	$537	$3,988
5	9.1%	$584	$4,036
6	9.1%	$587	$4,039
7	25.6%	$1,654	$5,106
8	1.4%	$93	$3,545
9+	0%	—	$3,452

Day 1 = the day the episode airs.
$3,452/day base runs every day regardless.

Example: 30-Day Month With One Episode

Day 1 = episode air date. Boost % = how much that day's share exceeds the flat baseline (3.14%). Apply to any month by setting Day 1 to the air date.

Day	Boost %	Daily Spend	% of $110K
Day 1	+1.13%	$4,695	4.27%
Day 2	+0.39%	$3,886	3.53%
Day 3	+1.20%	$4,769	4.34%
Day 4	+0.49%	$3,988	3.63%
Day 5	+0.53%	$4,036	3.67%
Day 6	+0.53%	$4,039	3.67%
Day 7	+1.50%	$5,106	4.64%
Day 8	+0.08%	$3,545	3.22%
Day 9	—	$3,452	3.14%
Day 10	—	$3,452	3.14%
Day 11	—	$3,452	3.14%
Day 12	—	$3,452	3.14%
Day 13	—	$3,452	3.14%
Day 14	—	$3,452	3.14%
Day 15	—	$3,452	3.14%
Day 16	—	$3,452	3.14%
Day 17	—	$3,452	3.14%
Day 18	—	$3,452	3.14%
Day 19	—	$3,452	3.14%
Day 20	—	$3,452	3.14%
Day 21	—	$3,452	3.14%
Day 22	—	$3,452	3.14%
Day 23	—	$3,452	3.14%
Day 24	—	$3,452	3.14%
Day 25	—	$3,452	3.14%
Day 26	—	$3,452	3.14%
Day 27	—	$3,452	3.14%
Day 28	—	$3,452	3.14%
Day 29	—	$3,452	3.14%
Day 30	—	$3,452	3.14%

Appendix: Copyable Data

Daily Spend Allocation Table

Day	Base Spend	Boost Spend	Total Spend	Cumulative	Cumul %
1	$3,452	$1,243	$4,695	$4,695	4.3%
2	$3,452	$434	$3,886	$8,581	7.8%
3	$3,452	$1,318	$4,769	$13,350	12.1%
4	$3,452	$537	$3,988	$17,338	15.8%
5	$3,452	$584	$4,036	$21,374	19.4%
6	$3,452	$587	$4,039	$25,412	23.1%
7	$3,452	$1,654	$5,106	$30,518	27.7%
8	$3,452	$93	$3,545	$34,063	31.0%
9	$3,452	—	$3,452	$37,515	34.1%
10	$3,452	—	$3,452	$40,967	37.2%
11	$3,452	—	$3,452	$44,418	40.4%
12	$3,452	—	$3,452	$47,870	43.5%
13	$3,452	—	$3,452	$51,322	46.7%
14	$3,452	—	$3,452	$54,773	49.8%
15	$3,452	—	$3,452	$58,225	52.9%
16	$3,452	—	$3,452	$61,677	56.1%
17	$3,452	—	$3,452	$65,128	59.2%
18	$3,452	—	$3,452	$68,580	62.3%
19	$3,452	—	$3,452	$72,032	65.5%
20	$3,452	—	$3,452	$75,483	68.6%
21	$3,452	—	$3,452	$78,935	71.8%
22	$3,452	—	$3,452	$82,387	74.9%
23	$3,452	—	$3,452	$85,838	78.0%
24	$3,452	—	$3,452	$89,290	81.2%
25	$3,452	—	$3,452	$92,742	84.3%
26	$3,452	—	$3,452	$96,193	87.4%
27	$3,452	—	$3,452	$99,645	90.6%
28	$3,452	—	$3,452	$103,097	93.7%
29	$3,452	—	$3,452	$106,548	96.9%
30	$3,452	—	$3,452	$110,000	100.0%

Day	Base Spend	Spike Spend	Total Spend	Cumulative	Cumul %
1	$3,452	$1,243	$4,695	$4,695	4.3%
2	$3,452	$434	$3,886	$8,581	7.8%
3	$3,452	$1,318	$4,769	$13,350	12.1%
4	$3,452	$537	$3,988	$17,338	15.8%
5	$3,452	$584	$4,036	$21,374	19.4%
6	$3,452	$587	$4,039	$25,412	23.1%
7	$3,452	$1,654	$5,106	$30,518	27.7%
8	$3,452	$93	$3,545	$34,063	31.0%
9	$3,452	$0	$3,452	$37,515	34.1%
10	$3,452	$0	$3,452	$40,967	37.2%
11	$3,452	$0	$3,452	$44,418	40.4%
12	$3,452	$0	$3,452	$47,870	43.5%
13	$3,452	$0	$3,452	$51,322	46.7%
14	$3,452	$0	$3,452	$54,773	49.8%
15	$3,452	$0	$3,452	$58,225	52.9%
16	$3,452	$0	$3,452	$61,677	56.1%
17	$3,452	$0	$3,452	$65,128	59.2%
18	$3,452	$0	$3,452	$68,580	62.3%
19	$3,452	$0	$3,452	$72,032	65.5%
20	$3,452	$0	$3,452	$75,483	68.6%
21	$3,452	$0	$3,452	$78,935	71.8%
22	$3,452	$0	$3,452	$82,387	74.9%
23	$3,452	$0	$3,452	$85,838	78.0%
24	$3,452	$0	$3,452	$89,290	81.2%
25	$3,452	$0	$3,452	$92,742	84.3%
26	$3,452	$0	$3,452	$96,193	87.4%
27	$3,452	$0	$3,452	$99,645	90.6%
28	$3,452	$0	$3,452	$103,097	93.7%
29	$3,452	$0	$3,452	$106,548	96.9%
30	$3,452	$0	$3,452	$110,000	100.0%

Huberman Lab PartnershipAttribution Study

Gross Sales by Discount Code Group, by Month

Methodology and Confidence

Establish each episode's baseline

Measure when the lift ends (per episode)

Split baseline vs. new-episode revenue

Apply the split to the $110K budget

Validate the model against reality

Why the Partnership Generates Revenue Every Day

Baseline Growth Timeline

Monthly Tracked Code Revenue

All 8 Ad Reads, Decomposed

Pairwise Correlation of Decay Shapes

What This Means

New-Episode Lift Duration

Two-Component Daily Spend Model

Code-Only MER Is the Floor, Not the Ceiling

Implied Effect at Different Capture Rates

What We Know vs. What We Don't

High Confidence

Unknown (requires testing)

Spend Allocation Playbook

In Practice: What a Typical Month Looks Like

Where Does the 8-Day Boost Window Come From?

Boost Curve Template

Example: 30-Day Month With One Episode

Daily Spend Allocation Table

Huberman Lab Partnership
Attribution Study