What does Lifesight do?

Lifesight is an Agentic Unified Marketing Measurement (UMM) platform that helps consumer brands grow by measuring what their marketing incrementally drives, not what ad platforms claim. It runs causal Marketing Mix Modeling (MMM), incrementality testing, and causal attribution as one orchestrated system in its Causal AI layer, with an Agentic AI layer on top that turns every result into answers, recommendations, and next steps.

What is unified marketing measurement?

Unified marketing measurement combines multiple measurement methods, such as MMM, incrementality testing, and causal attribution, so each one calibrates the others. Instead of three tools giving three different answers, you get one causal view of marketing's incremental contribution to business growth, from quarterly planning down to daily optimization.

Who is Lifesight for?

Lifesight is built for consumer brands and agencies that need causal, finance-ready marketing decisions: DTC and ecommerce, omnichannel retail and CPG, and consumer apps and subscriptions. You don't need a data science team to get started. Data OS handles ingestion, transformation, and orchestration, and agents make the results accessible to everyone from the CMO to channel owners.

Why do I need it?

No single measurement method has all the answers. MMM shows long-term, cross-channel effects but needs experimental calibration. Geo tests prove causal lift but answer one question at a time. Attribution gives daily granularity but over-credits lower-funnel channels. Lifesight triangulates all three so each fills the others' gaps, giving you incrementality you can prove to finance and the board, and spend decisions you can act on with confidence.

How does it work?

Lifesight works in one continuous loop:

• Data OS connects your marketing, business, and customer data and gets it model-ready in days, not weeks.
• The Causal AI layer models incremental contribution with causal MMM, proves lift with geo incrementality tests, and calibrates attribution with both, supported by AI-guided model assistance.
• The Agentic AI layer, built on the Lifesight Agent Harness and Marketing Context Graph, turns that measurement into decisions through the cockpit, ambient cues, and artifacts.

What is Data OS?

Data OS is the data foundation of Lifesight. It brings native and custom integrations, a flexible transformation layer (schema mapping, dimensions and metrics, taxonomy, grouping, and data modeling), unified orchestration, and continuous refreshes into one place. Roughly 80% of MMM complexity lives in data prep, and Data OS removes that bottleneck without engineering support. It's also where the agents work, reading from and building context on your data.

What do Lifesight agents do?

Agents shorten the distance between a measurement result and a budget decision. They 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, what it means, and what to do next, shaped by your persona.
• Artifacts: readouts, plans, and recommendations you can share and act on.

Can I trust what the agents tell me?

Yes. Agents run inside the Lifesight Agent Harness and draw on the Marketing Context Graph, which captures your business, your data, and your measurement guardrails. Every answer is grounded in your own measurement system, not a general-purpose model's guess, and every recommendation is causality-first, built to drive outcomes rather than vanity metrics.

What do I need to get started?

You need access to your marketing, business, and customer data. Data OS connects and structures it, and our marketing science team works with you on model setup and strategic guidance. For MMM, we recommend two years of aggregated sales and marketing data at daily or weekly granularity. Additional context such as competitor pricing, new product launches, and macroeconomic signals can be added at any time to sharpen the model.

How will my measurement be affected after third-party cookie deprecation?

It won't. Lifesight is privacy-safe by design. MMM and geo incrementality testing run on aggregated data, and causal attribution is calibrated with incrementality rather than dependent on cookies or cross-site identifiers.

Why can't I just use my current attribution stack?

Most attribution tools rely on user-level tracking and correlation-based rules. Signal loss makes them less complete, and correlation doesn't tell you what your spend caused. Lifesight keeps the daily granularity of attribution but calibrates it with MMM and test-measured lift, so your daily reads reflect incrementality.

Why is GA4 not enough for measurement?

GA4 is valuable for site and app analytics, but it doesn't measure incremental impact across all channels, including offline ones like CTV, OOH, and in-store. Lifesight complements GA4 by reconciling it with causal measurement, so you understand what your marketing is incrementally driving.

Why are Multi-Touch Attribution platforms not accurate?

MTA assigns credit using rules or correlations across tracked touchpoints. It can't see untracked or offline exposure, and it can't separate conversions that would have happened anyway from those marketing caused. Lifesight's causal attribution calibrates touch-level credit with MMM and experimental lift, correcting over-attribution in channels like retargeting and brand search.

Why can't I just use in-platform attribution and reporting?

Every platform grades its own homework. In-platform reporting doesn't account for other channels and tends to claim credit for conversions that would have happened anyway, so platform-reported numbers often add up to more revenue than you actually have. Lifesight gives you an independent, incrementality-based view across all online and offline channels.

Which platforms can I integrate into Lifesight?

Lifesight connects through Data OS to advertising and retail media platforms, commerce and CRM systems, analytics tools, data warehouses, marketing automation, subscriptions, support, forms, returns, shipping, and loyalty platforms, plus offline sources. Native integrations keep growing, and custom integrations are managed just as easily, with no engineering bottlenecks.

What is Marketing Mix Modeling?

Marketing Mix Modeling (MMM) is an econometric technique that quantifies the incremental impact of marketing and non-marketing drivers on sales, and forecasts the impact of future budget choices. Lifesight's causal MMM measures incremental contribution, iROAS, mROAS, and saturation by channel and tactic to guide budget allocation.

How does MMM work?

MMM analyzes historical spend and outcome data alongside drivers such as price, promos, seasonality, and macro factors, then isolates each channel's incremental effect. Lifesight's causal MMM also models adstock and lag, saturation and diminishing returns, and base sales, with adaptive baselines and regime detection (automatically spotting when your business has shifted into a new pattern) and built-in mediation, interaction, and cross-dimensional planning.

How do I know that my MMM is accurate?

Lifesight validates models statistically and calibrates them with Experiments. Measured lift from geo tests feeds back into the model, and regular refreshes with drift monitoring keep it accurate as market conditions and consumer behavior change.

Which channels and KPIs can Lifesight measure?

Lifesight measures any online or offline channel, including search, social, CTV, OOH, retail media, affiliates, influencer, and in-store. It reports KPIs such as iROAS, mROAS, ROAS, CPA, CAC, and incremental revenue.

How much does it cost?

The platform fee starts at $20,000 per year to measure up to $5M in annual marketing spend. Custom pricing depends on your number of channels and media spend. To get a quote tailored to your needs, contact our team.

How is Lifesight different from other platforms?

Lifesight is not a single-method tool, and it is not AI added onto a reporting stack. It triangulates causal MMM, incrementality testing, and causal attribution in one engine so results stay consistent and explainable, then puts agents on top so every answer is grounded in your own measurement. Data OS gets you there faster by removing the data prep that stalls most measurement programs.

How long does it take to set up?

Data OS gets your data model-ready in days rather than weeks. Integrating data, building and calibrating the model, and validating it before handover generally takes under five weeks, depending on the complexity of your data. Our marketing science team supports you throughout implementation.

Is the platform privacy compliant?

Yes. Lifesight is GDPR, CCPA, SOC 2, and ISO 27001 compliant. It works on aggregated first-party and server-side data collected from native platforms, with no dependency on third-party cookies.

Do you offer your platform to agencies?

Yes. Agencies can plan, optimize, and report to incrementality for their clients, run geo tests, and prove ROI. White-labeled solutions are also available for agencies to resell to clients.

Can you manage my measurement for me?

Yes. You choose how much you want to operate yourself. Alongside the platform, Lifesight offers managed services where our experts run measurement for you, so your team can focus on strategy and execution. Managed services are priced separately from the platform fee.

Where can I go to get help if I get stuck?

Lifesight offers a help center, a resource library, and direct access to our marketing science and data science teams. You can also ask questions in the cockpit and get grounded answers from your own measurement.

How can Lifesight help my business grow?

By showing what each channel incrementally drives, Lifesight helps you move spend away from over-attributed channels and toward those with the highest incremental return. That means higher marketing efficiency, incremental revenue lift, and a clear line of sight from spend to profit.

How does Lifesight help optimize media spend?

Causal MMM shows incremental contribution and marginal returns by channel, Experiments prove lift, and causal attribution carries that truth into daily reads. The Scenario Planner and Budget Optimizer then recommend where to invest, and agents flag when the mix drifts off plan.

How does Lifesight ensure accuracy in its insights?

Lifesight combines data QA checks in Data OS, model validation, experimental calibration, versioned models with audit trails, and drift monitoring. Because three methods check each other, results stay consistent and explainable.

How does Lifesight stay ahead of emerging trends in marketing measurement?

Lifesight continuously builds on advances in causal inference, machine learning, and agentic AI, and adapts to privacy changes. Lifesight 4.0 brings Data OS, AI-guided model assistance, and agents into one platform so measurement moves at the speed of decisions.

How does Lifesight ensure data security and confidentiality?

Lifesight is ISO 27001 and SOC 2 certified and complies with GDPR and CCPA. We use data localization, encryption, and secure data handling. Roles control access to modules and features, and agents operate only within Data OS and your permissions.

What is ROAS?

ROAS (Return on Ad Spend) measures revenue generated per dollar spent on advertising, calculated as revenue divided by ad spend. Platform-reported ROAS often includes conversions that would have happened anyway, which is why Lifesight focuses on iROAS.

What is Marginal ROAS or mROAS?

Marginal ROAS (mROAS) measures the additional revenue generated by the next dollar spent on a channel. It shows where more budget will still pay off and where a channel is approaching saturation.

What is Incrementality or iROAS?

Incrementality is the additional sales or conversions that wouldn't have happened without a specific marketing effort. Incremental Return on Ad Spend (iROAS) expresses that as incremental revenue per dollar spent.

What is Incrementality Testing?

Incrementality testing uses controlled experiments to measure the incremental impact of a channel, campaign, or tactic, separating conversions marketing caused from those that would have happened organically.

What is a Profit Curve or Diminishing Returns Curve?

A Profit Curve or Diminishing Returns Curve is a response curve showing how returns grow at a decreasing rate as spend increases in a channel. It shows the point where additional spend delivers minimal incremental gain.

What is geo testing?

Geo testing splits geographic markets into test and control groups. By comparing performance between matched markets, it measures the causal lift of a marketing change in a real-world environment.

How customizable is Lifesight?

Highly. Metrics, reporting, and insights can be tailored to your business, and the transformation layer in Data OS lets you define your own dimensions, metrics, and taxonomy. The platform is fully transparent, giving marketing scientists and data scientists control over model hyperparameters, visualizations, and data integration.

How do Lifesight Profit Curves or Diminishing Returns Curves drive optimization?

Diminishing Returns Curves show where each channel is on its response curve. By identifying where additional spend yields minimal incremental gain, they tell you where to cap spend and where there's headroom to scale, so budget goes where the next dollar earns the most.

How often does Lifesight update data?

Data OS runs continuous refreshes through ongoing feed ingestion, so models stay current rather than being periodically rebuilt. Refresh frequency depends on your data sources. You can check the last sync in attribution status or in MMM refresh.

Does Lifesight provide analytics and reporting to track website and marketing performance?

Yes. Lifesight tracks website and marketing performance across channels, with insights into customer behavior, incremental performance, and progress to targets. Dashboards range from executive to analyst-level views.

Does Lifesight provide training and support?

Yes. Onboarding includes four weeks of training with our marketing science team, and we offer 24/7 email and chat support, tutorials, and documentation. The cockpit also lets you ask questions about your measurement in plain language at any time.

What is the incremental contribution of each channel?

Create and run an MMM model to see incremental returns by channel and tactic. You'll find incremental, marginal, and contribution metrics for each channel in the Channel Breakdown table.

How do I allocate the optimal budget to each channel?

Your MMM model shows which channels are driving incremental results and which are saturated or delivering negative marginal returns. Based on this, the model recommends the optimal allocation to hit your planned goals.

How much budget do I need to achieve my goal?

Enter your target goal (revenue, orders, signups, leads, and more) in the Scenario Planner, and it calculates the budget required. It also shows where to invest, how much, and at what pace, with confidence intervals on the forecast.

How do I reallocate my budget if something changes?

If you launch a new campaign, refresh your MMM model with the new data to recalculate the recommended allocation. If you launch a new channel, you'll need enough data to add it to the model. If data is limited, run an Experiment to measure its incrementality and use the results to calibrate your MMM. Ambient cues also flag when performance shifts enough to warrant a re-plan.

How much can I scale a channel before it's saturated?

Check mROAS or mCPA in the Contribution table in MMM Insights. Lifesight also recommends media allocation automatically through AI-powered Recommendations.

How much is my brand contributing?

Lifesight's MMM separates base sales from marketing-driven sales and quantifies the effect of factors such as direct traffic, holidays, seasonality, and trends. You can view your baseline trend in MMM Insights.

What is the probability of hitting my goal?

The Goals feature tracks your marketing goals and spend, calculates the probability of hitting each goal, and updates the completion rate every time the model refreshes.

Where should I invest additional budget?

The Budget Optimizer distributes additional budget across your media mix based on marginal returns, so new spend doesn't go into saturated channels.

Experiments: Prove Incremental Lift Before You Scale

Experiments are how Lifesight turns model estimates into measured proof, and how it keeps your MMM and attribution calibrated.

What are Experiments?

Experiments are controlled tests of marketing hypotheses, such as:

• What is the incremental impact of my current media investment on business KPIs?
• How much can I scale before hitting diminishing returns?
• Which ad creative delivers the highest incremental return?

They compare outcomes with and without a marketing activity to measure its incremental impact.

What do Experiment results help with?

• Isolate a channel's incremental lift from external factors
• Compare channels fairly based on measured incrementality
• Allocate budget based on incremental lift
• Avoid mistaking correlation for causation
• Calibrate MMM models so they're causally grounded

Limitations of Experiments

Experiments require time, resources, and budget. They are best suited to measuring short-term lift and are less suited to long-term effects, which is where MMM complements them. External factors can also affect results if the test isn't designed to control for them.

How long should I run Experiments for?

Duration depends on the lift you want to detect (your MDE), your spend level, and your purchase cycle. Most geo tests run for several weeks so they capture at least one full purchase cycle and reach sufficient power. Lifesight recommends test duration as part of every test plan.

Why should I run Experiments?

Experiments are the most direct way to prove what your marketing causes. They validate platform claims, de-risk new channels such as CTV, retail media, and influencer, settle internal debates, and calibrate your MMM and attribution so every downstream decision is grounded in measured lift.

How do external factors impact the results of a geo test?

Local events, weather, promotions, or competitor activity can affect test and control markets differently and distort results. Lifesight reduces this risk with robust market matching, synthetic control, pre-period validation, and spillover controls, so the measured lift reflects your marketing change rather than outside noise.

Why is it important to run split tests with sufficient traffic?

Without enough traffic, a test can't reliably tell a real effect from random variation, and results may be inconclusive or misleading. Power analysis before launch tells you how much traffic and time you need to detect the lift you care about.

How do I ensure statistical significance in a geo test?

Run power analysis before launch to set your MDE and required duration, and aim for at least 80% power. Use well-matched test and control markets, avoid major changes in either group during the test, and read results only at the planned end date. Lifesight builds these steps into every test design.

Geo test vs. split test vs. time test

Advantages of Lifesight Experiments

For every recommended test, Lifesight specifies the Experiment type, spend type, timing and duration, expected lift, and expected cost, so you can make informed testing decisions.

Attribution: From Platform Credit to Causal Truth

Attribution gives you daily granularity. Lifesight's causal attribution makes sure that granularity reflects incrementality.

What is attribution?

Attribution assigns credit to marketing touchpoints for an outcome such as a conversion or purchase, showing how each channel, campaign, or touchpoint contributes to results.

Why is attribution important?

Attribution gives teams the daily, campaign-level reads they need to optimize bids, budgets, creative, and audiences. Calibrated with incrementality, it tells you which campaigns are truly driving results, so you can focus spend where it has the most impact.

What types of attribution models does Lifesight support?

• First-touch: credits the first interaction with your brand.
• Last-touch: credits the final interaction before conversion.
• Linear: splits credit equally across all touchpoints.
• Time decay: gives more credit to touchpoints closer to conversion.
• Custom: lets you define a model tailored to your business.

Any of these can be calibrated with incrementality through causal attribution.

How is data collected for attribution?

Lifesight collects data from web, mobile, social, email, and other channels through Data OS, then links it to user actions to trace the journey and apply your chosen attribution model.

Can I customize my attribution model?

Yes. You can define how much weight each touchpoint receives based on your business goals or customer behavior. To upload your own attribution weights, contact our support team.

What is Multi-Touch Attribution?

Multi-Touch Attribution (MTA) spreads credit across multiple touchpoints in the journey, rather than giving it all to a single one. It gives a fuller view of the journey but, on its own, still measures correlation rather than incrementality.

How often is attribution data updated?

Attribution data updates in near real time, depending on the data source, so you always have current reads for day-to-day optimization.

Can I track offline conversions?

Yes. Lifesight supports offline conversion tracking, so you can combine offline touchpoints such as in-store purchases and phone calls with online data.

What are common challenges with attribution?

• Cross-device tracking: users switch devices, which can fragment the journey.
• Data silos: attribution depends on accurate, integrated data from multiple sources.
• Model bias: different models assign credit differently, and none measure incrementality on their own.

Lifesight addresses these with cross-channel and cross-device tracking, integrations through Data OS, and causal attribution that calibrates credit with measured lift.

How can I troubleshoot discrepancies in my attribution data?

Discrepancies usually come from:

• Data collection issues: make sure all touchpoints and channels are tracked.
• Inconsistent timeframes: compare the same time periods across platforms.
• Model differences: compare data within the same attribution model.

If issues persist, contact Lifesight support.

What reports are available in Lifesight attribution?

• Channel performance: how each channel contributes to conversions.
• Path analysis: the journeys and touchpoints leading to conversion.
• Conversion tracking: conversions attributed to campaigns and touchpoints.

Reports can be customized to your attribution model and business needs.

What is causal attribution?

Causal attribution applies incrementality factors from MMM and Experiments to attribution reports, calibrating touch-level credit so it reflects what marketing actually caused. It deduplicates conversions across channels and corrects over-attribution in channels like retargeting and brand search, giving you daily, granular reads aligned with causal truth.

What is the timeline to implement MMM?

MMM typically takes about 30 days from the day your data is available. View the timeline.