The missing layer between strategy and activation

Objective-first marketing
built on real purchase behavior.

Geminus turns a business objective into who to reach — using individual cross-retailer transaction data — and measures whether that objective actually moved.

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The problem

Campaigns start with audiences.
Businesses start with objectives.

Brands care about outcomes: new households, category growth, trade-up, reactivation, product adoption. But campaigns typically begin with whatever audience is easiest to activate — a retailer's definition of "new," a CRM list, a platform interest segment, a third-party cohort.

Execution can be excellent. The starting point is often disconnected from how purchasing actually happens — across retailers, in stores, over time.

Most automation in marketing begins after the audience is already chosen. The upstream decision — who to reach for this objective — is still largely a guess.

How it works

An automated loop, end to end — from objective to measured outcome.

01
Define the objective
State the business goal in plain language — new households, category growth, product launch, trade-up, reactivation.
02
Strategies & audience definitions
The objective is translated automatically into executable strategies and precise audience eligibility definitions.
03
Campaign-specific ML models
Multiple supervised models train automatically on individual cross-retailer transaction data — one per strategy and audience.
04
Scored cohorts & holdout
The eligible population is ranked and split into treatment and holdout by design — before activation, not after.
05
Activation
Cohorts activate through existing platforms — LiveRamp, TTD, DSPs, retail media networks. Geminus does not buy media.
06
Lift measurement
Outcomes are read in the same cross-retailer transaction data. Not clicks. Not platform attribution. Real purchase behavior.

Why Geminus

⚡
Objective-native, not seed-native
No starting segment required. The audience is derived from the objective itself — households that achieved that outcome historically, based on their pre-outcome behavior.
🔁
Multiple automated models per campaign
Each strategy gets its own supervised model — NTB propensity, repeat conversion, winback hazard, trade-up propensity — trained automatically per campaign.
🏪
Individual cross-retailer transactions
Training signal is individual observed purchase behavior across retailers — not modeled proxies, not single-retailer data, not platform behavioral signals.
📐
Holdout by design
The control group is defined before activation. Incrementality is built into the workflow, not retrofitted after the fact.
🔌
Execution stays where it is
Geminus sits upstream. Campaigns run through DSPs, retail media networks, walled gardens, and CTV. Nothing in the execution layer changes.
📊
Outcomes in the same data
Results are read in the same cross-retailer transaction layer the model trained on. The question and the answer live in the same data.

Where it fits

Geminus strengthens the starting point for every activation platform.

LiveRamp
Objective → cohorts → identity resolution → activation across all destinations → lift in transaction data
The Trade Desk
Geminus improves the starting audience. TTD execution unchanged. Lift in purchase data, not platform attribution.
Retail Media Networks
Market-level objective and cross-retailer measurement without retailers sharing raw transaction data.
Commerce DSPs
Geminus defines the audience upstream. DSP execution is strong. The gap is market-level objective → audience + cross-retailer lift.
Agencies
A repeatable bridge between strategy and activation — with market-level proof of impact beyond platform metrics.
CPG Brands
Start from the business objective grounded in cross-retailer purchase behavior. Measure whether it moved.

If the objective matters, the starting point matters.

Built by operators who spent twenty years inside the execution layer — and know exactly where it breaks upstream.

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