What is Lifesight

Lifesight is an agentic Unified Marketing Measurement (UMM) platform that helps you grow by showing the incremental impact of every channel, so you invest in what actually drives revenue, not what ad platforms report.

Every marketing team faces the same question: what is the incremental return on the next dollar? Platform-reported ROAS, last-click attribution, and black-box models all give different answers, and by the time a reliable read arrives, the planning window has often closed.

Lifesight answers that question with causal evidence, in time for you to act on it.

Two AI layers work as one to get you there. Causal AI runs Marketing Mix Modeling (MMM), geo incrementality testing, and causal attribution as one orchestrated system. Lift tests calibrate the MMM, the MMM and test results calibrate attribution, and your numbers stay consistent from quarterly planning down to daily optimization.

Agentic AI is built on that same calibrated system, so agents can act on it directly, surfacing what changed and recommending your next budget move.

What this means for your team:

• Budgets you can plan with confidence
• Incrementality you can prove to the board
• Day-to-day spend you can optimize without guesswork

At a Glance: One Causal Story From Spend to Profit

The outcomes you can expect

When your measurement tells one consistent, causal story, each benefit builds on the last.

It starts with clear line of sight from spend to profit. Once you can see the incremental contribution of each channel, smarter budget allocation follows naturally, because spend moves away from over-attributed channels and toward those with the strongest iROAS and headroom on the response curve.

That shift delivers higher marketing efficiency and incremental revenue lift above your baseline. Because every test and model feeds the next decision, you also get faster learning cycles, so each quarter's plan is better calibrated than the last.

Built for brands like yours

These outcomes matter most to advertisers running multi-channel media mixes who need to justify every dollar:

• DTC and ecommerce brands
• Omnichannel retail and CPG
• Consumer apps and subscriptions

Causal Clarity in Hours, Incrementality You Can Prove

What sets Lifesight apart is that unification. It is not another single-method tool, and it is not AI added onto a reporting stack. Lifesight 4.0 is built on agentic AI from the ground up.

Causal AI orchestrates causal models, experiments, and causal attribution so each improves the others. You get marginal ROI you can act on, confidence ranges and reconciliation to P&L that hold up in front of the board, and privacy-first measurement on aggregate and pseudonymized data.

Agentic AI, built on the Lifesight Agent Harness and Marketing Context Graph, understands your business, data, and measurement guardrails. Agents answer on demand and run in the background 24/7, with every response grounded in your measurement, not guesswork.

The result: causal clarity in hours, not weeks, and incrementality you can prove to the board.

From Raw Data to Your Next Budget Move

Each capability delivers an outcome and feeds the next:

• Model-ready data in days with Data OS, without engineering bottlenecks
• The true value of every channel with Causal MMM, which quantifies iROAS, mROAS (the return on your next dollar), and diminishing returns
• Proven lift before you scale with geo incrementality testing, which also calibrates the MMM with experimental evidence
• Daily reads you can trust with causal attribution, which carries calibrated results into granular performance
• Budget decisions with the risk visible with scenario planning, which turns what you have learned into budget scenarios with confidence intervals on every forecast
• Answers and next steps around the clock with agents that surface what changed, why it matters, and what to do next

Because Lifesight orchestrates these methods instead of offering any one on its own, your results stay consistent, explainable, and actionable for the C-suite and channel owners alike.

Where Measurement Breaks Down, and What It Costs You

To see why a triangulated approach matters, it helps to look at where marketing measurement usually breaks down. These problems rarely show up alone. One tends to cause the next.

• Fragmented truth: It begins when platform-reported ROAS, last-click attribution, and black-box models all disagree, and your team loses a single source of truth.
• Wasted spend: Without that source of truth, budget follows whichever number looks best. Lower-funnel channels such as retargeting and brand search get over-credited, while upper-funnel channels and creative get under-invested.
• Slow learning: Teams run lift tests to find out, but one-off tests rarely feed back into planning, and MMMs are seldom calibrated with experimental results. The same questions come back every quarter.
• Trust gap: Meanwhile, finance wants proof of incrementality and marketers need timely, granular guidance. Most tools serve one side, not both.
• Data prep that eats the timeline: Even when a team commits to better measurement, roughly 80% of modeling complexity sits in ingestion, transformation, and orchestration. Measurement stalls before a model is ever run.
• Insight that never becomes action: And when readouts finally arrive, they land in decks and dashboards, and nothing changes.

How Lifesight Turns Measurement Into Growth

Each capability below is one step in the measurement loop, in the order the loop runs. It starts with your data, moves through the Causal AI layer that models, tests, and calibrates, and ends with the Agentic AI layer that turns results into decisions.

1. Data OS: Get Model-Ready Data in Days, Not Weeks

The loop starts with your data, because that is where measurement usually stalls. Around 80% of MMM complexity sits in ingestion, transformation, and orchestration.

Data OS removes that bottleneck, getting your data model-ready in days instead of weeks and keeping your models on a continuous refresh without engineering support.

What it covers

• Seamless integrations: Native integrations keep growing, and custom integrations are just as easy to manage, with no engineering ticket required.
• Flexible transformation layer: Map schemas, define dimensions and metrics, apply taxonomy, group data, and build data models in one place. This becomes the data foundation for every model and every downstream measurement system.
• Unified orchestration: One coordinated flow across the full measurement loop, from raw data to model-ready inputs, across every tool in your stack.

What you get

• Faster data onboarding: Weeks to days, with fewer blockers.
• Simpler data operations: Transformation, taxonomization, and data model preparation in one unified layer.
• Continuous data refreshes: Ongoing feed ingestion keeps models current, supporting a refresh cadence the category hasn't supported before.

Use it for: cutting onboarding from weeks to days, and keeping models live rather than periodically rebuilt.

Data OS is also where the agents work. They don't sit outside it. They build context on it and maintain that context over time, which is why their answers stay grounded in your measurement.

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

2. Causal MMM: Know the True Value of Every Channel

Causal MMM gives you a top-down view of your full media mix and measures the incremental contribution of each channel. You set budgets and targets on iROAS and marginal returns rather than platform-attributed conversions, with enterprise-grade rigor and startup-speed time to value.

What it covers

• Weekly and monthly econometric models across paid, owned, and earned media
• Non-media drivers, including price, promos, retail and market factors, seasonality, and competitive and contextual drivers
• Short and long-term effects: adstock and lag (how long a channel's impact carries over after spend), saturation and diminishing returns, and base sales
• 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)
• Mediation, interaction, and cross-dimensional planning out of the box
• AI-guided model assistance, powered by Data OS, for inference that keeps pace with your business

What you get

• iROAS by channel
• mROAS by channel
• Incremental contribution
• Saturation points
• Response curves and forecast and scenario curves by channel and tactic (a specific way of running a channel, such as prospecting or retargeting)

Use it for: quarterly and annual budget setting, channel mix rebalancing, target setting, and long-range planning.

MMM estimates what each channel is worth. The next step is validating it with experiments.

3. Geo Incrementality Testing: Prove Lift Before You Scale

Geo incrementality testing measures causal lift by comparing test markets against matched control markets, built for the realities of how brands actually run media.

It validates channel performance before you commit more budget, and feeds experimental results back into your MMM as calibration priors, so the model gets more accurate over time.

What it covers

• Test design and execution for scale-up, holdout, and multi-cell tests at DMA, state, or city level
• Robust market matching and synthetic control (a modeled stand-in for what would have happened without the test) for reliable counterfactuals
• Power analysis and MDE (minimum detectable effect), so every test is sized to detect lift at an acceptable level of risk and opportunity cost

What you get

• Causal lift
• Incremental revenue
• iROAS by channel, tactic, and geo
• Guidance on test duration and spillover controls

Use it for: proving incrementality, de-risking new channels such as CTV, retail media, and influencer, and validating platform claims.

With MMM and lift tests aligned, you have a calibrated causal view of performance. The next step is bringing that view into the numbers your team optimizes against every day.

4. Causal Attribution: Optimize Daily in Line With Your Plan

Causal attribution applies what your MMM and lift tests have learned to daily, granular performance reads. It closes the loop from spend to outcome, so your in-flight optimization stays consistent with your strategic planning.

What it covers

• Unifies platform and first-party data
• Calibrates touch-level credit using MMM temporal factors and test-measured lift
• Deduplicates conversions across channels
• Corrects over-attribution in retargeting and brand search

What you get

• Daily, granular performance reads aligned with incrementality

Use it for: day-to-day budget shifts, bidding thresholds, and creative and audience tests.

Once your reads are calibrated at every level, you can use them to decide what to do next.

5. Scenario Planning: Commit Budget With the Trade-offs in View

Scenario planning turns everything the Causal AI layer has measured into forward-looking budget decisions. You can model reallocations before committing to them, see forecasted incremental revenue and profit for each, and choose with confidence.

What it covers

• What-if planner: Shift spend across channels and see forecasted revenue and profit, with confidence intervals on every forecast.
• Constraints-aware optimization: Recommendations that respect floors and ceilings, pacing, lead times, retail windows, and your business rules.

Use it for: monthly and quarterly re-plans, efficiency versus growth trade-offs, and CFO alignment.

The Causal AI layer tells you what is working and what to do about it. The Agentic AI layer makes sure those answers reach the right people at the right time.

6. Agents: Go From Measurement Result to Budget Decision Faster

Agents close the loop. Built on the Lifesight Agent Harness and Marketing Context Graph, they understand your business, your data, and your measurement guardrails.

Every response is grounded in your measurement system, with no hallucinations and no guesswork, and every recommendation is causality-first, built to drive outcomes rather than vanity metrics.

Agents work on demand when you ask, and run quietly in the background 24/7 when you don't.

What it covers

• Cockpit: The decision surface where you ask questions in plain language and get grounded answers with the evidence attached.
• Ambient cues: Agents flag what changed, what it means, and what to do about it, shaped by your persona so each team member sees what matters to their role.
• Artifacts: Readouts, plans, and recommendations produced in a form you can share and act on.

Use it for: shortening the distance between a measurement result and a budget decision.

Together, these layers give you one connected measurement system: model-ready data, calibrated MMM, proven lift, causal attribution, optimized plans, and agents that keep it all moving. To see how it can work for your brand, book a demo with our team.

Measure With Confidence, Without Compromising Privacy

• Data minimization: Only the data required for measurement use cases.
• Privacy-safe modeling: MMM and geo testing operate on aggregate signals; attribution uses calibrated, policy-compliant inputs.
• Governance: Role-based access, auditable changes, environment segregation, and documented model versions.

Key Terms at a Glance

• Incrementality: The causal lift attributable to an intervention versus a valid counterfactual.
• iROAS vs. mROAS: Average ROI across historical spend vs. marginal ROI for the next dollar. Use mROAS for scaling decisions.
• Calibration: Using causal lift and MMM temporal factors to adjust platform and attribution numbers so daily reads line up with reality.
• Saturation: The point at which additional spend yields diminishing marginal returns (where mROAS drops below your hurdle rate).

Signs Lifesight Is Right for You

• You need one version of the truth across marketing, finance, and agencies.
• Budgets are shifting into new channels like CTV, retail media, and influencer, and you must prove what works.
• You want a repeatable cadence: refresh models, run smart tests, recalibrate, re-plan, and act, quarter after quarter.
  
  

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