How Lifesight Works

Lifesight turns fragmented marketing data into a single causal decision system, so every budget move, from quarterly allocation to daily optimization, is grounded in what your marketing actually caused.

It does this with two AI layers working as one, on top of a data foundation built for speed. Data OS gets your data model-ready.

The Causal AI layer unifies causal Marketing Mix Modeling (MMM), geo incrementality testing, and causal attribution, so each method calibrates the others. The Agentic AI layer then turns that measurement into decisions through the cockpit, ambient cues, and artifacts.

The result is one loop where every insight can be proven, every plan simulated, and every daily decision aligned to business outcomes.

One Loop From Data to Decisions

Everything in Lifesight runs through one continuous loop that connects strategy, testing, and execution:

Prepare: Data OS ingests, transforms, and orchestrates your data so it's model-ready.
Model: Quantify what's driving incremental results across all channels and contexts.
Test: Prove causal lift where it matters most.
Calibrate: Feed experimental results into your models and reporting to correct bias.
Plan: Build forecasted budget scenarios and recommendations you can act on.
Optimize: Make day-to-day decisions with incrementality-adjusted KPIs.
Monitor: Track realized versus forecasted results while agents watch for what changed, then refresh the loop.

Agents take part at every step rather than sitting at the end, so you never have to choose between speed and causal truth.

Read next: How MMM Works (High Level) · Designing a Geo-Lift Test · From Attribution to Incrementality-Adjusted KPIs · Scenario Planning & Optimization

The loop runs on four connected parts of the platform, each one feeding the next.

Four Layers, One System

Data OS: Ready in Days

Roughly 80% of MMM complexity lives in data prep. Data OS removes that bottleneck and becomes the foundation every model, test, and agent reads from.

Sources: Media platforms, web and app analytics, ecommerce and POS (point-of-sale), CRM and subscriptions, price and promo calendars, product and inventory, and context such as seasonality, geography, and macro signals.
Seamless integrations: Native integrations keep growing, and custom integrations are managed just as easily, with no engineering bottlenecks.
Flexible transformation layer: Map schemas, define dimensions and metrics, apply taxonomy, group data, and build data models in one place, with QA checks that keep data comparable across channels and time.
Unified orchestration: One coordinated flow from raw data to model-ready inputs across every tool in your stack.
Continuous refreshes: Ongoing feed ingestion keeps models current, at a refresh cadence the category hasn't supported before.
Privacy-first: Aggregated and pseudonymized where appropriate, with no dependency on third-party cookies.

With model-ready data in place, the Causal AI layer takes over.

Causal AI: Proof You Can Defend

The Causal AI layer combines three methods into one engine, supercharged by Data OS and AI-guided model assistance for enterprise-grade rigor with startup-speed time to value.

Causal MMM: Long and short-term effects, incremental contribution, and marginal returns across every channel. Adaptive baselines with regime detection (automatically spotting when your business has shifted into a new pattern, so the model doesn't keep reading the old one) keep reads accurate as your business changes, and mediation, interaction, and cross-dimensional planning come out of the box.
Geo incrementality testing: Controlled, scalable tests that measure causal lift for channels, tactics (specific ways of running a channel, such as prospecting or retargeting), audiences, and offers, by comparing test markets against matched control markets.
Causal attribution: Granular, always-on reporting calibrated with MMM and test-measured lift, so daily reads reflect causal reality rather than platform-reported numbers.

Measurement only creates value when it changes a decision. That is the job of the next layer.

Agentic AI: Faster Decisions

The Agentic AI layer is built on the Lifesight Agent Harness and Marketing Context Graph, so agents understand your business, your data, and your measurement guardrails.

Every response is grounded in your measurement system, with no guesswork, and every recommendation is causality-first, built to drive outcomes rather than vanity metrics. Agents work on demand when you ask and in the background 24/7 when you don't.

Cockpit: Ask questions in plain language and get grounded answers with the evidence attached.
Ambient cues: Alerts on what changed, why it matters, and what to do next, shaped by your persona.
Artifacts: Readouts, plans, and recommendations in a form you can share and act on.
Scenario Planner: Forecast the outcomes of different budget mixes and pacing strategies, with confidence intervals on every forecast.
Recommendations and guardrails: Response curves and lift evidence translated into channel and tactic guidance that respects your constraints, such as floors, ceilings, and lead times.
Exports and integrations: Move plans into your buying stack and workflows with full auditability.

Because decisions move faster, every one of them needs to be traceable. That is where governance comes in.

Governance: Full Traceability

Dashboards: Executive through analyst-level views linking spend, incremental contribution, and progress to targets.
Versioning and audit trails: Every model, test, and plan traces back to its inputs and assumptions.
Refresh and drift monitoring: A set cadence for retraining models and retesting, so guidance stays accurate.
Enterprise-grade access controls: Roles control access to modules and features, and personas shape the agent cues and notifications each person receives.

What each method contributes

Causal MMM → strategic clarity. Sets up quarterly planning, shows diminishing returns and saturation, and identifies the biggest levers to move.
Geo tests → causal proof. Validate what's incremental, de-risk new channels, and quantify lift to correct bias in models and platform reports.
Causal attribution → daily control. Keeps channel and campaign teams aligned to causal truth while they optimize creative, audiences, and bids.
Data OS → faster time to value. Removes the data prep that holds most measurement programs back.
Agents → action. Close the gap between a result existing and someone acting on it.

These reinforce one another. Tests sharpen the models, models identify where to test next, attribution inherits the truth from both, and agents keep the loop moving.

Together, these methods turn the data you bring into the decisions you need to make.

Inputs In, Decisions Out

What you bring

Minimum viable: Spend by channel, outcome KPIs (orders, revenue, subscriptions), core site and app metrics, and seasonality.
Better with: Price and promo calendars, retail and POS roll-ups, product and inventory, creative and placement metadata, geography, and macro signals.

Data OS maps, cleans, and structures whatever you bring, including custom sources, so you can start lean and add depth over time.

Decisions you can make

Budget allocation: Where to move the next dollar for the highest incremental return.
Channel and tactic scaling: Which plays to scale, cap, or pause based on measured lift and marginal returns.
New channel launches: Test design, expected outcomes, and success criteria before you scale.
Creative and audience priorities: Day-to-day optimization using incrementality-adjusted KPIs instead of platform-reported metrics.

Making these decisions well depends on making them at the right rhythm.

A Cadence That Keeps Improving

Quarterly: Refresh MMM, re-forecast, set profit-aligned budgets, and kick off one or two flagship geo tests.
Monthly: Compare realized versus forecast, run scenario updates, adjust constraints, and review test results.
Weekly and daily: Optimize campaigns using causal attribution and monitor pacing against plan.
Annually: Revisit your learning agenda, long-term effects, and portfolio strategy.
Continuously: Data OS refreshes run in the background, and agents monitor 24/7 for cues worth acting on.

Run this cadence consistently, and here is what your measurement program starts to look like.

What good looks like

One source of decision truth: The same numbers guide the CMO, CFO, and channel leads, at the right level of detail for each.
Measured lift informs every move: Tests confirm what's incremental, and their lift calibrates both models and daily KPIs.
Plans with confidence intervals: Recommendations carry ranges and trade-offs, not false precision.
Tight feedback loops: Realized versus forecasted results are reviewed routinely, and the loop refreshes.
Nothing waiting to be noticed: Cues reach the right person while the decision still matters.

All of this rests on a platform built to be privacy-safe, transparent, and reliable.

Privacy and Trust, Built In

Privacy-safe by design: Works on aggregated and pseudonymized data, with no reliance on cross-site identifiers.
Transparent methods: Clear assumptions, versioned models, and documented test designs.
Operational resilience: Data quality checks, anomaly detection, and rollback paths for plans and models.
Bounded agents: Agents operate inside Data OS and your permissions, and every answer traces back to its source.