“Lifesight combines causal MMM, incrementality testing and incrementality-adjusted attribution into one connected system. Growth agents then turn the results into recommendations your team can review and act on.”
“Attribution tools credit what happened. Lifesight measures what your marketing actually caused. Experiments calibrate the model, and the model calibrates attribution, so your quarterly plan and daily decisions tell the same story.”
“Only if you allow it. Budget changes need your team's sign-off, and nothing changes on your platforms until you approve the action, unless you've set rules that allow Lifesight to make those changes. Every action is logged and traced to evidence.”
“No. Your team can ask questions in plain language and get answers grounded in your own measurement. Lifesight experts are available for model design, experiments and interpretation when you want them.”
“Most teams connect their data in days, with no engineering work needed. Timelines depend on your data sources and the scope of your program.”
“Every engagement is scoped individually. Together, we agree the measurement work, expert involvement, responsibilities, deliverables and level of ongoing support.”
“Yes. You can operate supported workflows with your own team and AI agents. Agree the scope and authorization of each workflow, and bring Lifesight experts into the work where needed.”
“An engagement can include expert help with interpretation, planning, workflow operation and execution. The exact responsibilities and supported actions are agreed during scoping.”
“MCP connects your Lifesight context to supported AI assistants. It gives your team another way to access the platform; available operations depend on the connection and permissions.”
“Timing depends on scope and data readiness. As a planning guide, connectors can be established in days, attribution may take around one to two weeks, and an initial MMM around four to six weeks. Agree milestones with the team for your engagement.”
“Discuss changes as the program develops. Your team may take on more work, add expert support or expand supported agent workflows. Agree revised scope and responsibilities before expanding.”
“Lifesight estimates what each channel is adding and how returns change as spend increases. You can compare allocations against your growth goal, budget and constraints, and review the forecast range and assumptions behind each plan before committing to one.”
“Lifesight reports incremental revenue and iROAS by channel, separate from platform-reported credit. Financial views use the cost and margin assumptions you agree with Finance, so the discussion is about contribution rather than credited conversions.”
“Yes, where the data supports it. Causal MMM estimates contribution across online and offline channels, including effects that carry over after the campaign runs. Where a brand question is too big to settle from the model alone, a geo test can add direct evidence.”
“Models are checked three ways: their causal assumptions are explicit, their predictions are tested on held-out data, and experiment results inform the next model run. Your analysts can inspect the inputs, diagnostics and uncertainty behind each estimate.”
“Experiment findings inform the next MMM run through calibration priors. Attribution-layer adjustments are a separate mechanism.”
“Out-of-sample performance, holdout backtesting, residual analysis and appropriate goodness-of-fit measures, alongside experiment evidence.”
“Yes. Use deployment scoping to agree data, assumptions, method fit and the decisions the implementation is intended to support.”
“MMM refreshes weekly with new data and time-varying coefficients. Retraining happens when needed, such as for new variables, material drift or experiment evidence, often around quarterly.”
“Supported APIs, BI connections and reverse ETL can make outputs available elsewhere. Confirm the fields, permissions and delivery pattern for your workflow.”
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