Daddy Gang vs. Hot MessFeatured case study · Creator economy · April 2026
Daddy Gang vs. Hot Mess.
Four Twins. Two from Daddy Gang. Two from Hot Mess.
Click to chat. Each persona is a portrait of one slice of one audience — cohort filter on top, real respondent rationales underneath. Every claim she makes is citation-grounded in the survey.
Kelsey
29 · Pittsburgh, Pennsylvania
Daddy Gang · Republican
The lifestyle-conservative slice the 'Gen Z liberal women' shelf misses — still listens to Daddy, but for different reasons than the brand assumes.
“Yeah, I'm Republican and still listen to Call Her Daddy, people assume that's weird, but it's not like the show's a political litmus test, ya know?”
Portrait of 205 Digital Twins
Talk to Kelsey →
Jenna
28 · Cleveland, Ohio
Daddy Gang · Long-form listener
The actual podcast audience — full 30+ minute episode, earbuds in, distinct from the clip-economy crossover.
“I tried the clip thing for a while, but it's like getting the trailer instead of the movie. If I'm going to care, I need the whole conversation, earbuds in, 45 minutes minimum, the kind of listen where you actually get what they're saying instead of just the hot take everyone's passing around.”
Portrait of 205 Digital Twins
Talk to Jenna →
Kayla
22 · Dallas, South
Hot Mess · Commerce-active
The commerce engine the influencer economy is built on — repeat purchases in 90 days, low-friction trust loop.
“ok so I've grabbed like three or four things in the last couple months from creators I follow, their recs just feel easy and spot-on for my skin”
Portrait of 197 Digital Twins
Talk to Kayla →
Madison
22 · Columbus, Midwest
Hot Mess · Parasocial-intense
The parasocial-intense core — 'I genuinely know her as a person.' Her trust is the asset every endorsement borrows from.
“ok so yeah, the whole Alix thing, I really do feel like I know her from all her videos and the drama with Alex, like it genuinely upset me when that went down.”
Portrait of 272 Digital Twins
Talk to Madison →
Built on the StatSocial stack
Three audience panels. 300K+ deterministic attributes. 2,000 Digital Twins.
3
USA-baselined audience panels
Cooper, Earle, and the overlap cohort
300K+
Deterministic behavioral attributes
Observed activity, not modeled predictions
2,000
Twins surveyed (1,000 per audience)
12 questions + 6-question calibration battery
The stack
How three StatSocial assets answer one question.
01 — Identity
PeopleGraph
Three USA-filtered audience panels built from a collection of publicly available cross-platform creator-followership data: Cooper's audience, Earle's audience, and the overlap (Earle fans who also follow Cooper on TikTok).
02 — Behavior
KnowledgeGraph
Deterministic observed behavioral attributes across brands, interests, media, influencer affinity, B2B roles, personality signals, and geography. Real social behavior, not modeled predictions.
03 — Attitude
Digital Twins
12-question attitudinal survey fielded to 1,000 AI-simulated respondents per audience, with a 6-question demographic calibration battery. Calibration MAE: ~3.5 pts vs. Pew/Gallup/Nielsen — better than typical opt-in panels.
Finding 01 · The overlap
26% of Cooper's audience also follows Earle.
26%
Of Cooper's audience also follows Earle
Cross-platform identity match. Anchor for the cohort analysis.
Why it matters
Brand planners default to “Gen Z women 18–34” as one addressable block. That block has at least three distinct audiences inside it — Cooper, Earle, and the 26% who follow both. Run both creators and you reach the overlap twice and under-reach the rest.
Finding 02 · The real split
It isn't income. It's life stage.
| Cooper | Earle | Δ | |
|---|---|---|---|
| Employed full-time | 31.7% | 23.1% | +8.6 |
| Student | 23.0% | 27.0% | −4.0 |
| Lives with family (no rent) | 32.7% | 40.3% | −7.6 |
| Owns home | 17.8% | 11.9% | +5.9 |
| Bachelor's or higher | 30.4% | 27.1% | +3.3 |
| Identifies as Democrat | 29.7% | 24.7% | +5.0 |
| Identifies as Republican | 20.5% | 12.7% | +7.8 |
| No political preference | 17.9% | 27.7% | −9.8 |
| Household income $100K+ | 31.0% | 28.5% | +2.5 |
| Kids under 18 at home | 23.0% | 20.8% | +2.2 |
Cooper's audience is 1.37× more likely to be working full-time. Earle's is 1.23× more likely to live with family. Cooper's audience claims a major political party at 50.2% vs. 37.4% for Earle — a cleaner adult-vs-pre-adult split than the demographic cuts alone show.
Finding 03 · The self-referential finding
The people buying your Earle ad areEarle's audience.
2.9×
Earle audience index for Marketing / Advertising / PR
vs. USA adult baseline of 1.0×
1.2×
Cooper audience index for the same B2B affinity
Essentially at the USA baseline
2.5×
Overlap cohort index for Marketing / Ads / PR
Concentrated where the industry consumes itself
Why it happened: Earle's content is heavy on creator-economy, DTC, beauty, and influencer-marketing topics — the daily reading of the marketing industry itself. The audience is the industry at ratios you don't see on most consumer creators. If your team watches Earle, your team is over-represented in the data you're buying against.
Finding 04 · The media diet gap
Cooper reads like grown-up America.
Earle reads like pre-grown-up America.
What this means for buyers
Two audiences inside the same demographic envelope are consuming two different content universes. Sports and news media on the Cooper side; lifestyle and cooking on the Earle side. A buy optimized for “Gen Z women 18–34” against Cooper's panel will under-deliver against an Earle audience and vice versa.
Finding 05 · The commerce flip
Cooper's audience listens.
Earle's audience shops.
45.7% / 39.2%
Bought a creator-recommended product in the last 90 days
Earle / Cooper. The deck rounded both down by ~3 points; these are the verified figures.
17.6% / 12.8%
Spent $51–$150 on creator products in 90 days
Earle / Cooper. The mid-tier creator-purchase band is where the spread shows.
1.48×
Earle's repeat-purchase ratio over Cooper's
19.7% of Earle fans bought multiple times in 90 days vs. 13.3% of Cooper's.
Earle brand signature · % of audience with brand affinity PeopleGraph
| Brand | Earle | Cooper |
|---|---|---|
| Rare Beauty | 7.4% | 5.3% |
| Skims | 4.4% | 3.6% |
| Tree Hut | 3.9% | 0.5% |
| Poppi | 2.0% | 1.9% |
| Alani Nu | 1.2% | 0.9% |
Finding 06 · The format split
Cooper presses play.
Earle scrolls.
Cooper's format
Long-form. Audio-first.
- 20.5% watch full long-form episodes (30+ min)Earle: 7.9% · 2.6× Cooper's favor
- 17.9% get news & culture from podcastsEarle: 9.6%
- 11.9% listen at 1.5× or 2× speedEarle: 8.6%
Earle's format
Short-form. Scroll-first.
- 36.7% prefer short clips under 5 minutesCooper: 23.0%
- 25.7% get news & culture from TikTokCooper: 14.6%
- 2.7× Earle's TikTok-to-podcast news ratioCooper: 0.82× — inverse pattern
The overlap cohort · 26% of Cooper's audience
The most interesting 26% in creator marketing.
Demographically
Cooper's female core
93% female (vs. 81% for Cooper solo). 61% aged 25–34, weighted younger than Cooper's full audience. The subset of Cooper fans that sits closest to Earle demographically.
Emotionally
The maximizer
Highest anxiety (75%), self-consciousness (64%), vulnerability (74%) — and highest cheerfulness (70%), agreeableness (62%), need for love (62%). The full emotional range, turned up.
Culturally
The creator super-consumer
Brianna LaPaglia in 52% of this cohort. Kylie Jenner 64%. Charli D'Amelio 49%. Selena Gomez 51%. Tate McRae 37%. They consume the meta-story of creator fame, not just any one creator.
Commercially
The DTC super-index
Of the overlap cohort: 12% follow Rare Beauty, 9% follow Skims, 5% follow Poppi, and roughly 1% follow Unwell Hydration (Cooper's own brand) — 2–3× the rate of either audience alone. This is the cohort buying the creator-era brand stack.
Creator sort · Follow the followership
Different audiences, different creator universes.
Pure Cooper
Over-indexed in Cooper. Absent from Earle's top follows.
- David Dobrik26%
- Doja Cat18%
- Corinna Kopf12%
- Zane Hijazi11%
- Oneya D'Amelio8%
The overlap
Over-index in both audiences; dominate the 26% overlap cohort.
- Kylie Jenner64%
- Brianna LaPaglia52%
- Selena Gomez51%
- Charli D'Amelio49%
- Addison Rae46%
Pure Earle
Concentrated in Earle's audience at 2–3× Cooper's rate.
- Tara Lynn21%
- Brooke Monk17%
- Maia Knight17%
- Anna Paul17%
- Nara Smith14%
Left and right columns: % of each audience following the named creator. Middle column: % of the 26% overlap cohort following.
The double-spend
Four brands accidentally paying twice.
| Brand | Cooper | Earle | Overlap |
|---|---|---|---|
| Rare Beauty | 5.3% | 7.4% | 11.6% |
| Skims | 3.6% | 4.4% | 8.9% |
| Poppi | 1.9% | 2.0% | 5.1% |
| Unwell Hydration Cooper's own brand | 0.35% | 0.14% | 0.93% |
Unwell Hydration is Cooper's own brand — and it over-indexes most inside the overlap.
Geography · Two different maps
Cooper country. Earle country.
Cooper country
Northeast Corridor + Midwest.
- Pittsburghindex 171
- Bostonindex 163
- Philadelphiaindex 134
- Chicagoindex 120
- Denverindex 120
Earle country
Sun Belt + New York.
- Phoenixindex 224
- Pittsburghindex 211
- New Yorkindex 185
- Dallas-Ft. Worthindex 164
- Minneapolisindex 163
Index 100 = USA adult average. Pittsburgh appears in both top-5s; every other DMA is unique to one audience.
So what
The feud isn't a fight.
It's a generational handoff.
Earle
Younger, more diverse, TikTok-native, commerce-active emerging adults. 27.7% claim no political preference. Below USA baseline on almost every adult consumption domain. A pre-adult audience with a 2.9× over-indexed Marketing / Advertising / PR footprint.
Cooper
Older, podcast-native, civically engaged adults. 50% major-party identified. At or above USA baseline on sports, news, politics, mainstream brands. A full adult-media audience wearing a lifestyle label.
The overlap
The 26% of Cooper's audience who also follow Earle. High-emotion, high-intensity super-consumers of the creator-era DTC brand stack. Any multi-creator campaign that reaches them reaches them twice.
Audience intelligence — not follower count — is how the next generation of creator budgets gets allocated.
Methodology
PeopleGraph
3 USA-baselined audience panels (Cooper, Earle, Overlap). Reports run April 21–22, 2026.
KnowledgeGraph
Deterministic observed behavioral attributes — brands, interests, media, influencer affinity, B2B, personality, geography.
Digital Twins
12 questions + 6-question calibration battery, n=2,000 Twins (1,000 per audience). Fielded April 2026. ~3.5pt MAE vs. Pew/Gallup/Nielsen benchmarks.
Run this on your audiences.
Same stack, your creators, your competitors, your category. Two-week turnaround from brief to live insights.