LiftLab
LiftLab (LiftLab Analytics, Inc.) is a marketing measurement and capital allocation platform for enterprise marketing, analytics, and finance leaders. Its Two-Stage Agile Marketing Mix Model (AMM) separates ad marketplace auction dynamics — CPM/CPC volatility and competitive pressure — from true consumer demand response, producing channel response curves and marginal ROI (mROAS) that are not contaminated by marketplace noise. A geo-based Incrementality Testing Suite feeds causal results back into the model through the Trust Engine as permanent calibration inputs, PlatformSense applies live ad platform data to those stable curves for daily channel intelligence, the Scenario Planner turns model output into constraint-aware, finance-ready budget plans, and Miles AI answers natural-language questions grounded in the model. LiftLab serves D2C/ecommerce, CPG, and omnichannel retail brands including Pandora, Cinemark, Quicken, SKIMS, and Thrive Market. Headquartered in Oakland, CA; SOC 2 Type II and ISO 27001:2013 certified, GDPR and CCPA compliant. LiftLab publishes no public developer API or API documentation as of this profile.
LiftLab is profiled on the APIs.io network. Tagged areas include Company, Enterprise, Marketing, Marketing Measurement, and Marketing Mix Modeling.
LiftLab’s developer surface includes documentation, getting-started guide, support, engineering blog, signup flow, and 8 more developer resources.
Kin Score
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Get Started 2
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 1
Pagination, idempotency, versioning, errors, and events
Access & Security 3
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API