LiftLab

LiftLab is an enterprise marketing measurement platform that connects media analysis with budget planning. It is best suited to teams spending at least $5 million a year on brand and performance media. Its clearest strength is carrying measurement into constrained budget scenarios; teams should weigh that against a substantial historical-data requirement and custom pricing.

Overview

LiftLab brings marketing mix modeling, incrementality experiments, and budget decisions into one platform. That breadth is useful for organizations that want to use causal evidence and channel response estimates to guide annual investment, rather than keeping measurement and planning separate. It is a less natural fit for smaller advertisers or teams without a year or more of weekly spend history.

Key features

The Two-Stage Agile Marketing Mix Model separates ad-auction cost dynamics from consumer response when estimating channel response curves. This gives marketing teams a way to account for changing auction costs while assessing channel performance, an important distinction when translating media spend into budget decisions.

The Scenario Planner builds Conserve, Maintain, and Accelerate options within constraints such as channel caps, CAC ceilings, and committed spend. Annual planning, forecasting, ROI reporting, and approval workflows support a more structured route from analysis to budget allocation. The stated planning horizon is annual, so this is not a fit for teams seeking another defined horizon.

The Incrementality Testing Suite supports geo experiments and returns their causal results to the mix model for calibration. Methods include Stratified Random Sampling, Synthetic Controls, Switchback tests, Strategy experiments, and Go Dark with Pacing. That range can serve teams with different experiment designs, while the value depends on having the capacity to run experiments and use their results.

PlatformSense monitors daily performance shifts and uses live signals to inform reallocation recommendations, adding a shorter-term optimization layer to the annual planning capabilities. Miles, an AI-powered analyst, answers performance questions in plain English using LiftLab’s measurement methodology. The platform connects to more than 50 paid media platforms, including Meta, Google, TikTok, YouTube, Pinterest, and LinkedIn, alongside analytics, CRM, data warehouses, and first-party business data sources.

LiftLab’s minimum starting data is 12 to 24 months of weekly channel spend, one primary outcome KPI, and a promotional calendar. That requirement is a meaningful onboarding hurdle for newer businesses or teams with fragmented records. LiftLab says its modeling uses aggregate data rather than individual-level user data, and that it is SOC 2 compliant and holds an ISO 27001:2013 certificate. Its marketing science team handles configuration, calibration, and ongoing validation, with weekly model reviews described on its homepage.

Pricing

The LiftLab platform uses custom pricing: contact LiftLab for a demo and pricing. Subscription terms and fees are set in an Order Form. The platform includes enterprise marketing measurement, and the stated plan information gives no seat or usage cap to weigh against the quote. Prospective buyers should assess the custom terms alongside the platform’s $5 million annual media-spend fit and data prerequisites.

Platforms

LiftLab supports web and API access. This combination suits teams incorporating the platform into their digital workflow, though the stated platform coverage does not include a desktop application.

Who it's for

LiftLab is aimed at enterprise marketers measuring brand and performance media, particularly organizations spending $5 million or more annually. It is strongest for teams that can supply the required channel history and want measurement, experiments, optimization signals, and annual budget planning connected. Smaller advertisers and teams without the required data foundation should look elsewhere.

Pros and cons

• Pro: Modeling and incrementality results feed into budget scenarios, linking evidence to annual allocation decisions.
• Pro: A broad experiment-method set and more than 50 paid media connections can accommodate varied measurement programs and channel mixes.
• Pro: Aggregate-data modeling avoids processing individual-level user data, and LiftLab states SOC 2 compliance and ISO 27001:2013 certification.
• Con: The 12–24 months of weekly spend data plus a KPI and promotional calendar create a substantial entry requirement.
• Con: Custom pricing and the enterprise spend threshold make it difficult to assess fit as a small or low-spend team.
• Con: Annual is the stated planning horizon, limiting its relevance for teams needing a different planning cadence.

Alternatives

Google Meridian is a free, open-source option for teams that prioritize source-code and methodology transparency and can work across its API, Linux, macOS, self-hosted, or web platforms. Revup Marketing is worth considering for teams seeking a freemium web product, with a free tier limited to one plan, one user, and one team. PyMC-Marketing is a free Apache 2.0 open-source Python library for teams comfortable with Linux or self-hosted use. Robyn is another free option with API, desktop, and self-hosted platform support.

Brandsystems is a paid web-based alternative with demo-request pricing. Fintastic offers an enterprise subscription with unlimited users and data and support included; contact the maker for pricing. Measured is a paid enterprise platform with demo-request pricing. Planful uses pricing tailored to company size, user count, and selected modules. Browse marketing performance management software for more options.

Verdict

Choose LiftLab if your enterprise marketing team has the spend and data history to support its models and wants experiments, measurement, and budget planning connected in one workflow. Its mix of causal testing and constrained scenario planning is the main reason to consider it. Look elsewhere if your media spend is below its intended range, your historical data falls short, or you need clearer pricing before starting an evaluation.