Why now
The consumer data industry
is breaking at its signal layer.
Advertising bleeds first.
80–90%
of iOS users opted out of tracking.[1]
The collapse.
Apple’s ATT wiped out the dominant signal source in mobile advertising. Most of the data brands had been targeting on disappeared overnight.
0
ad waste growth in two years — and accelerating.[2]
The damage.
More spend reaches the wrong users.
CAC rises. ROAS degrades.
Every year, the damage compounds.
The gap.
No consent-based infrastructure exists yet.
The signal is broken.
Legislation is catching up.
We built the replacement.
The answer
mymodel is the
for the consumer data industry.
How it works
Users consent. Brands pay.
Every transaction is
its own consent event.
Users
One-time onboarding.
A live Stats Card they own.
9:41
Set up
30-second one-time onboarding through Gmail.
Stats Card
A live portrait of their consumer behavior — updated automatically in the background.
No surveys required.
Earn
A push notification arrives when a brand sends a paid Audience Offer.
One tap to accept. 24-hour window.
Payout
100% of the offer amount goes to the user.
$5 minimum offer per user.
mymodel’s fee is charged on top to the brand — never from the user’s share.
Retention
0.2% monthly churn.
No habit to build, no engagement loop.
The app doesn’t bother users until it brings them money.
99.6%
of consumers would share their data for the right incentive.[4]
The supply was never the problem.
Distribution Strategy
$2M in. 150,000 waitlist users out.
The product sells itself. We just need the stage.
Live Streams
Founder Socials
Mainstream Podcasts
Press & Media
Panels & Speaking
Events
Content Production
3–4×
conversion rate for founder-led consumer brands vs. traditionally marketed equivalents.
Morning Consult · Creator Economy Report · 2023
10×
conversion lift for live-streamed CTAs vs. traditional e-commerce (~30% vs. 2–4%).
OneStream · Live Stream Marketing · 2025
~80%
of podcast listeners take action after hearing a host-read recommendation — one of the highest-converting media formats.
Edison Research · Super Listeners
Visibility Budget (Marketing)
22.5% of $2M raise · 12-month deployment
$450K
Visibility budget
150,000
Waitlist signups
$3
Cost per signup
Every input sourced. The sign-up-side anchor for every projection downstream.
Why me
Today’s tech founder archetype
can’t earn mass consumer trust.
I can.
I'm building a future where the relationship between people and technology is life-giving rather than life-extracting.
I saw targeting data collapse from inside the companies selling it.
The best stacks in the industry— breaking in real time.
My biggest advantage in winning mass consumer trust is my ability to liaison between both worlds.
Tech bros destroyed consumer trust.
A new era of consumer trust in tech must be developed by a different kind of founder.
Brands
Real users in your target market.
Consented access to 360° behavioral intelligence.
No guesswork · High-precision · Compliant by design
01 — SEARCH
Brands search mymodel with keyword-level precision.
Verified behavioral signals from live Stats Cards, not inferred segments.
02 — OFFER
Brand sends a paid Audience Offer to the specific users it wants to reach.
Minimum $5 per user, platform fee charged on top.
03 — GRANT
User receives the offer, accepts or declines within 24 hours.
Every accepted offer is a discrete consent event — the user grants the brand access to their Stats Card for this engagement.
04 — ACTIVATE
30-day access to the granted audience.
Brands use it for live market research and export it as a custom audience for campaigns across Meta, TikTok, and Google.
A new data class
What a user grants has a name.
We call it Granted Data.
Live behavioral signal released per consent event — explicit, priced, auditable.
30-day expiry by design. Retention is built in.
Here is how it stacks up.
| Data Type | Source | Quality | Best For | Limitation | Cost | Compliance |
|---|---|---|---|---|---|---|
| Third-party | Data brokers | Probabilistic, stale | Quantity > quality motions | Requires enormous volume. Compliance risk. Dwindling supply. | $$$$ | High risk CPRA / GDPR exposure |
| First-party | Brand’s own collection | Good but limited scale | Converting people already in your ecosystem | Confined to people who’ve already interacted with you. | $$$ | Low risk Owned data, limited scope |
| Zero-party | Surveys, panels | Self-reported, bias-prone | Focus groups & qualitative | Small samples. Stated vs. revealed preference. | $$$$ | Low risk Declared, hard to audit |
| Granted | mymodel | Deterministic. Real-time. 360° profile. |
Finding & winning net-new users | GA Q3 2026 | $$ | Zero risk Active consent, auditable |
✓Deterministic accuracy
✓Instant actionability
✓360° scope
✓Real-time
✓Cost-effective
✓Globally compliant
Phase One Market
154,000
U.S. advertisers in our range.
$30K+/mo
Meta spend per advertiser.
1 in 3
dollars of reported Meta ROAS is phantom.[10]
Granted Data closes the gap.
Recoverable margin. Testable in a single campaign cycle.
Business Model
Recurring revenue.
Structural retention.
Monthly subscription, per-user floors, transaction fees.
Three fee layers. All ongoing.
* Floor is paid 100% to the user. Transaction fee is charged separately, on top.
Brand Acquisition
How we bring brands in.
Three channels. One compounding flywheel.
Behavioral clusters catalyze every send.
User intent matched to brand opportunity.
• Each node = a mymodel user
CH 1 · Mo 1+
Competitive Targeting
Each user cohort maps to a competitive landscape — one cluster surfaces dozens of brand targets in a single run.
CH 2 · Mo 1+
Coverage Balancing
Users with few active offers get grouped and matched to brands not yet deployed — filling gaps automatically.
CH 1 · COMPETITIVE TARGETING
Vol:
Conv:
New:
SEQUENCE LIVE
CH 2 · COVERAGE BALANCING
⚠ Gap detected
Vol:
Conv:
New:
GAP FILLED
CH 3 · Mo 3+
High-Spend Meta
Agencies and DTC brands spending $100K+/yr on Meta. Unlocks Month 3, once cohort performance data is live.
Three channels.
Low lift. High volume. High accuracy.
Low lift. High volume. High accuracy.
Mo 12 Active Brands
4,444
2.9% of 154K addressable · base case
Click to reveal the formulas behind every input above
The Numbers
The engine pays a billion dollars to users by Month 19.
Less than 4.9% of the 154K-brand addressable US market.
Users
Active brands
ARR
—
Users—
Active brands—
ARR—
Paid to users—
24-mo peer envelope
Dosh · 1.5M users by Mo 24
Robinhood · 3M users by Mo 24
Team scales 2 → 6 by Mo 12 · full hiring plan in The Unlock ↓
Users
150K waitlist + 100% monthly inflow
Mo 24 → — MAU
0.2–1%/mo churn Y1 · no engagement loop, no reason to leave
Brands
50K emails/day × 0.025% conversion
Mo 24 → — active brands
Util ramp 30% → 100% as MAU → 500K · 0.5%/mo churn
Bear case is 10× below avg cold email conversion (BreakCold, 2026)
Bear case is 10× below avg cold email conversion (BreakCold, 2026)
ARR
$5 floor × 12% fee + tiered memberships ($499 / $999 / $1,999) · derived from users × brands
Mo 24 → — ARR · Klaviyo comp ~$180M at 4K brands (S-1, 2023)
Capital recoup
Month 5
$2M raise fully recouped from cumulative revenue
— total Mo 1–5 ≥ $2M raise
~4× faster than the typical SaaS 18–24 month recoup window · first invoice > CAC at every tier
Month 24 · user-side
$—/yr
Average projected earnings per active user · ~$—/month steady-state
— ÷ — users = $—/mo × 12 = $—/yr
8–20× the cashback category · Dosh ~$40–50/yr · Honey ~$126/yr (PayPal, 2020)
Month 24 · revenue
$—M ARR
—% of the 154K-brand addressable US market
$—M × 12 = $—M ARR · — ÷ 154K brands = —%
—% of the market untouched
Brand tier mix · Mo 24
Live economics · recomputes from sliders above
Starter—
— brands·
— AO/mo (1 cap)·
—% of revenue
— first invoice=
— membership+
— transaction fee·
—× CAC
$199 intro membership Mo 1–6 · steps up to $499 from Jan 2027 onward
Growth—
— brands·
— AOs/mo (5 cap)·
—% of revenue
— first invoice=
— membership+
— transaction fee·
—× CAC
Scale—
— brands·
— AOs/mo (unlimited + API)·
—% of revenue
— first invoice=
— membership+
— transaction fee·
—× CAC
▸Show the math
Live values recompute from the sliders. Formulas and sources are fixed.
1. User growth
MAU[m] = MAU[m-1] × (1 - churn[m]) + waitlist[m] + inflow[m]Waitlist conversionbase 65% / bear 55% · Robinhood 50%+ waitlist conversion (Forbes 2013)
Monthly inflow peak150K/mo at Mo 6 (base) · floor comp: Dosh 125K/mo (TechCrunch 2018-19)
Churn Mo 1-120.2%, 0.2%, 0.2%, 0.2%, 0.5%, 0.5%, 0.8%, 0.8%, 0.8%, 1.0%, 1.0%, 1.0%
Churn Y2+1.0%/mo flat · passive utility, no engagement loop to disengage from
Inflow Y2 decay1.5%/mo decay, floor 75% of Mo 12 · Ibotta S-1 (2024), a16z State of Fintech (2023)
→ Users Mo 12 (live)—
→ Users Mo 24 (live)—
2. Brand acquisition
newBrands[m] = Σ(chiEmails × conv × sat) × util + inbound · WOMEmail pipe50K/day × 30 days = 1.5M/mo · Influencers Club 100K/day at 95% inbox placement documented
End-to-end conversionbase 0.025% · bear 0.021% · ABM benchmark (ITSMA, Demandbase, Crayon, 6sense)
Utilization ramp30% at 150K MAU → 100% at 500K MAU (log) · targeting intelligence requires user density
Channel share (Mo 3+)Ch1 cluster 25% · Ch2 coverage 45% (× 0.70 overlap) · Ch3 high-spend 30%
Saturationsat = 1 / (1 + prevBrands / 154K Apollo pool) · hyperbolic decay, never fully exhausts
Inbound + WOMinbound ramps 15→120/mo Mo 1-12 · WOM 0.8% base / 0.5% bear of existing brand base
Brand churn0.5%/mo base · 1.0%/mo bear · Klaviyo 90%+ NRR S-1 2023 · LiveRamp 95%+ NRR 2023 AR
Y2 outbound decay0.4%/mo reduction past Mo 12 · floors at 95% (minimal — performance-differentiated)
Y2 inbound ramp4%/mo compounding from Mo 12 · Segment / HubSpot Y2 inbound 3-5× Y1 after case studies
→ Brands Mo 12 (live)—
→ Brands Mo 24 (live)—
3. Revenue
revenue[m] = Σ(AO × floor × users/AO × dm × cm × ar × fee) + membershipsTier mix Mo 1-1295/5/0 → 45/43/12 (Starter / Growth / Scale) by Mo 9 · deepens through Mo 24
AO floor (per user)Starter $5.00 · Growth $3.50 · Scale $3.00 · user payout is 100% of floor
Transaction fee (on top)Starter 12% · Growth 10% · Scale 8%
MembershipsStarter $199 (Mo 1-6) → $499 (Mo 7+) · Growth $999 · Scale $1,999
Users per AOStarter 500 · Growth 1,000 · Scale 1,000
AO utilization / brandStarter: 1 AO · Growth: 3 avg · Scale: 4-6 by Mo 12 (base) / 3-4 (bear)
Audience ramp (ar)1 + ln(MAU / 150K) × Ksc · K base 0.20 / bear 0.15 (targeting precision scales with MAU)
Competitive mult (cm)base 1.0 → 1.15 (Mo 0-11 linear, then slow continued) · bear 1.0 → 1.08
Depth mult (dm, Mo 6+)1.0 + 0.3 × integration rate · base 0.65 / bear 0.45 (location + social integrations)
→ ARR Mo 12 (live)—
→ ARR Mo 24 (live)—
→ Cumulative payouts Mo 24—
→ $1B crossing—
The moat
A consent-based economy
isn’t viable without infrastructure.
01 — Lifetime Supply
Consumer trust in tech is a one-way door. Once mymodel becomes the consent marketplace people join, supply consolidates — and brands follow supply.
02 — The Lock
Incumbents can’t pay users without breaking their business.
03 — The Scope
Advertising is just the first use of this infrastructure.
The Unlock
$2M. Three phases.
First revenue in 90 days.
The platform is built. The pilot is structured.
Here’s what $2M sets in motion.
PHASE 1 · ASSEMBLE
Q2 2026
Key hires, content engine, product polish, outbound systems.
Output
Operational foundation — team, tooling, and pipeline.
PHASE 2 · PROVE
Q2–Q3 2026
Design partners live.
Real campaigns, real data.
Case studies in hand.
Output
Case studies and a 150K+ waitlist.
PHASE 3 · LAUNCH
Q3 2026
Public launch.
Waitlist converts.
Outbound engine fires.
Output
Revenue generating.
Budget Allocation
Where the $2M goes.
$2M Allocation (expandable)
Team & Hiring Timeline
Early Contributors
Built without capital.
Full-time engineering, 11 months in
Core platform built
4 advisors under NDA
The $2M doesn’t start the build.
It unlocks the distribution.
Chris Madison · Founding Engineer
8 months · full-time
Built the core platform, matching engine, and data pipeline from scratch. Production-ready before the raise.
Domain Advisors — Under NDA, No Equity
Jordan Cushman
Dir. SMB Sales, Reddit Ads
Brand Sales & GTM
Chris Anzalone
3× Founder, CEO — NextLM
AI & Behavioral Systems
Shahin Zangenehpour
Principal Mobile Eng · Neuro PhD
iOS & Neuroscience
Lynn Liss
Social Impact & Fintech Leader
Fintech & Impact Investing
Full domain coverage across ad-tech, consumer data, DTC growth, and behavioral AI — every advisor chose to commit before a dollar was raised.
Design Partnerships
Built around industry leaders.
Replicable across the rest.
Category exclusivity for the biggest operators
in the highest-saturation niches of our ICP.
The Moat
First in your category on granted data. Pioneer status — with the public-facing credibility and saturated-market differentiation that compound with it.
Tangible ROI
Every dollar hits verified intent. Reclaim the budget your current targeting is burning.
Case studies in hand before public launch.
Returns
What $2M compounds to.
Your Check
$250K
$50K$1.5M
2.08% ownership
Pre-money
$10M
Raise
$2M
Post-money
$12M
The Round
Who we’re taking calls with.
Data Rights & Consent
Marketplace & Network Effects
Consumer & Gen Z
Ad Tech & MarTech
Impact & Social Good
Contrarian & Frontier
The round is open.
jordan@getmymodel.com