Audience Activation as a Service

Turning world-class data into world-class outcomes.

Strategic Partnership Proposal

The gap between strategic intent
and execution is widening.

Market Context

$200B+

Annual programmatic spend. CTV growing 15% YoY. Brands demanding deterministic precision over "black box" segments.

The Problem

Data providers possess world-class identity graphs, but translating business goals into activated segments is manual and friction-heavy.

The Opportunity

We're building a "Consultative AI Layer" on top of your existing data. New revenue channel — zero operational burden.

The Problem

The "Translation Layer" is currently
a human bottleneck.

Sales Call
Strategist
Ideation
Data Ops
Query
Back & Forth
Iteration
Delivery
Total Time: 15–20 Days

Converting business goals into audience segments currently costs time, money, and opportunity. It relies on manual SQL tickets, file exports, and validation. This friction slows revenue and customer enthusiasm, reducing value and utilization of high-fidelity data.

The Solution

Audience Strategy as a Service

A new product category. Cycle compressed from weeks to hours.

01
Discovery

AI Strategist translates natural language business goals into creative concepts grounded in proprietary semantics.

02
Generation

System auto-generates precise SQL against your specific identity graph schema.

03
Validation

Real-time Snowflake execution provides actual counts, sample records, and match rates instantly.

04
Activation

Integrate with ad platforms via rETL (Hightouch/Census). These are living segments that auto-refresh, not stale CSVs.

Architecture

Warehouse-Native. Zero Operational Burden.

Data Partner
🔒
Our AI Platform
Audiences Results
DSP
Social
CRM
Zero Data Movement
Uses Snowflake Secure Share. Your data never leaves your infrastructure.
Zero Storage Overhead
No copying of data. No SQL tickets. Queries run on our compute.
High Performance
High fidelity deterministic addressability and a virtuous learning loop — results feed back into the AI layer to sharpen every future audience.
Before & After

Changing the Unit Economics of Data Sales

MetricTraditional SalesThis Partnership
Audience Ideation Manual strategist work AI-generated concepts in minutes
Query Translation Data ops writes SQL Natural language to SQL automatically
Validation Hope and pray Real-time counts & sample records
Time to Activation 15–20 Days 24-48 Hours
Partner Effort 5+ touches per request 0 touches per request
Go-to-Market

Capturing the Mid-Market Opportunity

The Target Persona

Heads of Performance & Programmatic Leads at mid-market brands ($50M–$500M revenue) in Travel, Home Services and other high LTV verticals.

These brands are too large for generic segments but lack the in-house data science to build custom Snowflake-native graphs. They are "starved" for high-fidelity strategy that they can execute tomorrow.

Distribution: The Direct-to-Seat API Protocol

Eliminating marketplace latency and the "onboarder tax."

01

API-Driven Activation

We utilize Reverse ETL to push deterministic identifiers (UID2, MAIDs, Hashed Emails) directly into the client's DSP/Social/CTV seats via First-Party Data APIs.

02

Instant Market Access

Bypass the 60-day "Data Provider" approval cycles. Audiences appear in the client's targeting menu within 24 hours, maintaining the momentum of the AI discovery session.

03

Deterministic Match Rates

By mapping directly to the endpoint, we eliminate the 30%+ match loss common in third-party marketplaces.

04

Clean Revenue Capture

Direct monthly invoicing based on Snowflake record counts. No "tech tax" from the DSP, and 100% transparency for all parties.

Revenue Model

A Hybrid Pricing Model for Scale

Phase 1 · Months 1–12

Records-Delivered Pricing
CPM on delivered audience size. Simple, predictable, no complex DSP approvals.

Phase 2 · Months 12+

Platform + Usage
Monthly platform fee for AI access + usage fee on activated records. Recurring service revenue.

Phase 1: Transactional Phase 2: Service 0 6 12 18 mo $

Shift from data vendor to strategic partner.

Economics

High-Margin, Capital-Efficient

Margin Profile at $1.5M ARR:

Revenue$1,500,000100%
Data Partner ShareNegotiableTBD
Tooling / Infrastructure–$75,0005%
Salaries (3 - 4 FTE)–$500,00033%
EBITDA$500K+33%+

We carry the costs: customer acquisition, compute, product development, and tooling.

You share the revenue.

Negotiable based on value-add and beta support. Aligned to your existing agency/reseller economics.

AI replaces the strategist. rETL replaces the engineers.

Growth becomes a sales opportunity, not a staffing problem.

Execution

A 'Special Ops' Team Structure

We bring the hustle and the tech; you bring the data.

Product

Owns AI application and data partner relationship.

Growth

Focuses on client acquisition and high-LTV verticals.

Data Services

Handles rETL operations and destination configs.

AI automates the strategist. rETL automates the engineer.
The team stays radically focused on selling and fulfilling.

Validation Plan

The First 90 Days

Month 1

Setup & Beta

Tech setup. Beta client onboarding. Validate end-to-end workflow from discovery through activation.

Month 2

Performance Proof

Prove model. Measure performance lift vs. standard vendor audiences.

Month 3

Commercial Scale

Publish case study. Validate pricing model. Define the scale plan.

Risk Analysis

A Low-Cost Option on the Future

Our Risk

  • Capital investment
  • Customer acquisition cost
  • Development effort
  • Reputation

Your Risk

  • Secure Share access

If we fail, you learned something for free.
If we succeed, you have a new strategic capability.

The Ask

What We Need to Validate This

01

Access

Snowflake Secure Share (read-only, revocable) + 2-4 hours data ops support for initial schema mapping and share configuration.

02

Beta Validation

2-3 mid-market brands from your pipeline who've expressed interest but haven't scaled. We handle all fulfillment.

03

Commercials

Revenue share aligned to your existing agency/reseller agreements. Open to partnership, white-label, or JV structure depending on strategic fit.

If successful, explore co-development of vertical-specific semantic modules (financial services, automotive, etc.)

Let's build the future of
audience activation.

By 2026, AI-native activation will be the standard.
Let's own it today.

Next step → Draft revenue share terms and technical requirements.
Partnership Structure Options

Flexible on How This Scales Up

My priority is proving the model in 90 days. Once we validate operationals and client economics, we can optimize structure based on what we learn together.

Potential Structures

01

Revenue Share Partnership

Simple commission model. You provide data access, I handle operations, we split revenue.

02

White-Label License

You use the platform with your branding for direct client relationships. I provide platform + support.

03

Joint Venture

Co-owned entity with shared equity, governance, and upside. Deeper strategic alignment.

04

Acquisition + Transition

You acquire the IP and platform, I stay on to run it as part of your team.

If We Pursued a JV (Strawman Framework)

Inputs:

  • You bring: Identity graph access (Secure Share), data ops capacity for initial setup, co-marketing support (case studies, ecosystem intros)
  • I bring: Semantic intelligence layer (proprietary IP), AI platform, product roadmap, initial customer acquisition

Equity Structure:

  • → 60/40 or 50/50 depending on co-marketing and ecosystem access contribution (not sales infrastructure)
  • → 3-year vest, 1-year cliff (standard)

Governance:

  • → I run product/ops (CEO or Chief Product Officer)
  • → You appoint board seat, control data partnership
  • → Key decisions require both parties

This is a Month 4 conversation, not Month 1. Let's prove unit economics first.

Preview