Lifesight combines causal MMM, incrementality testing and attribution with AI agents for budget decisions. Pricing is custom, by annual subscription.
Researched and fact-checked by Bartu Cavusoglu · Updated October 26, 2026
Lifesight is an agentic unified marketing measurement platform. It combines causal marketing mix modeling, incrementality testing and incrementality-adjusted attribution on one data foundation. It is built for mid-market and enterprise brands that want to know what their marketing caused, not only what ad platforms report. The company says it supports the teams behind 300+ brands.
AI agents sit on top of the measurement layer. They investigate changes, compare budget scenarios and draft plans or briefs for the team to review. Lifesight contrasts itself with attribution tools: attribution credits what happened, while its approach estimates what marketing caused, with experiments calibrating the model and the model calibrating attribution.
Lifesight starts by agreeing the decision, outcome and data with the customer. Teams connect media and business data, then reconcile dates, currencies, coverage and taxonomy during onboarding before the model produces numbers used for decisions. The company describes this as a reviewed gate, not a promise that data issues resolve themselves.
The methods then work in a cycle. The model frames a worthwhile experiment, a geo test measures lift against a synthetic control, and relevant results enter the next MMM run as priors. The updated model is validated, finer channel context is reviewed, and allocations are compared. Agents surface new results and help prepare the next brief.
The measurement core includes Causal MMM, which compares channel response, carryover, diminishing returns and marginal versus average returns. Geo incrementality tests return a lift estimate with its range. Incrementality-adjusted attribution shows platform-reported and incremental ROAS side by side and updates daily to support weekly reviews, while the site notes the limits of finer estimates.
On the planning side, Lifesight offers scenario planning, budget optimization, and forecasts with uncertainty ranges. Agents can flag new experiment results, investigate shifts in iROAS and draft allocation briefs. Lifesight MCP brings this context into assistants such as Claude and ChatGPT. Reporting covers unified media and sales data, creative insights and BI or API export.
Lifesight is an agentic unified marketing measurement platform. It combines causal marketing mix modeling, incrementality testing and incrementality-adjusted attribution, with AI agents that help teams plan budgets and act on results.
Lifesight does not publish prices. It offers Performance, Precision and Enterprise plans as annual subscriptions scoped to each business. Teams book a demo to receive a tailored proposal.
The sources do not mention a free trial of the platform. The site offers a free Measurement Gap Calculator tool, and a demo is the route to a proposal.
It is built for mid-market and enterprise brands. Its audience includes marketing, growth, finance and analytics teams, plus agencies supporting measurement and investment decisions.
Lifesight uses native connectors, supported warehouse sources such as Snowflake and BigQuery, and scheduled API or file feeds. Lifesight MCP connects it to assistants such as Claude and ChatGPT. A connector directory lists current availability.
Lifesight says attribution tools credit what happened, while it measures what marketing caused. Experiments calibrate the MMM, and the MMM calibrates attribution so planning and daily decisions use the same evidence.
Only if the team allows it. Budget changes need sign-off, and nothing changes on ad platforms until an action is approved, unless rules permit it. Actions are logged and traced to evidence.
No. Teams can ask questions in plain language and get answers based on their own measurement. Lifesight experts are available for model design, experiments and interpretation.