
Funnel
Centralize all your marketing data and unlock growth with a marketing intelligence platform.

Enterprise-grade causal AI for mapping marketing signals to revenue outcomes.

Alembic represents a shift from traditional correlation-based marketing analytics to a robust Causal AI architecture. Built to solve the attribution crisis caused by the death of third-party cookies and fragmented consumer journeys, Alembic utilizes spatiotemporal analysis to identify the direct relationship between marketing activities—including 'dark social' and offline signals like TV or out-of-home—and hard business outcomes such as revenue and retention. Its technical core moves beyond legacy Marketing Mix Modeling (MMM) by providing near real-time ingestion and processing of multi-channel data. By 2026, Alembic is positioned as a mission-critical infrastructure for CMOs and Data Science teams who require a 'source of truth' for budget allocation. The platform leverages advanced signal processing to filter out noise and identify 'halo effects' where top-of-funnel brand awareness directly catalyzes bottom-of-funnel conversions. This allows enterprises to move away from vanity metrics toward a deterministic understanding of their Return on Ad Spend (ROAS) and Customer Acquisition Cost (CAC) across the entire customer lifecycle.
Alembic represents a shift from traditional correlation-based marketing analytics to a robust Causal AI architecture.
Explore all tools that specialize in marketing mix modeling. This domain focus ensures Alembic delivers optimized results for this specific requirement.
Uses directed acyclic graphs (DAGs) to represent and calculate the causal relationships between variables, avoiding the common pitfalls of correlation-based models.
Analyzes the timing and location of marketing signals to connect offline events (like a localized TV spot) to online spikes in traffic or sales.
Applies signal processing algorithms to filter out market noise and isolate the specific impact of marketing spend on revenue.
A 'what-if' simulator that allows users to adjust budget allocations across channels to forecast revenue outcomes before spending.
Quantifies the long-term revenue value of brand-building activities that do not immediately result in a direct click.
A privacy-first approach that does not rely on PII (Personally Identifiable Information) or individual user tracking.
Automatically calculates how long the influence of a specific ad persists in a consumer's mind before a purchase.
Initial discovery session to define core business KPIs and conversion events.
Connection of historical data sources via native connectors (Salesforce, Snowflake, etc.).
Identification of offline data streams (TV schedules, radio, OOH) for spatiotemporal mapping.
Schema mapping and normalization of disparate data formats into the Alembic graph.
Baseline training period (typically 3-6 months of historical data) for causal model stabilization.
Configuration of attribution windows and decay parameters based on product lifecycle.
Validation phase comparing AI-generated insights against known historical anomalies.
User acceptance testing (UAT) for Data Science and Marketing leadership teams.
Real-time data pipeline activation for ongoing monitoring.
Bi-weekly optimization cycles to refine causal weights as market conditions change.
All Set
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Verified feedback from other users.
"Users praise Alembic for its ability to unify disparate data sources and provide clarity on 'untrackable' channels, though some note the high entry cost and intensive setup period."
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