Alembic, the leading holistic marketing attribution platform for enterprises, announced the general availability of the next generation of its platform, the first analytics platform to deploy and feature composite AI. Alembic can now mathematically demonstrate causality in large datasets, initially focusing on marketing ROI. Imagine being able to trace and understand the direct impact of a large brand spend, similar to reversing the butterfly effect. Alembic now provides the precise causality and ROI insights that have long been the elusive goal of marketing analytics.

“Causal artificial intelligence (AI) identifies and utilizes cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously. It includes different techniques, such as causal graphs and simulation, that help uncover causal relationships to improve decision making.”

Alembic is the first to precisely trace and prove the results of marketing programs and is the first to apply composite AI, causal AI, a graph neural network and advanced contact-tracing mathematics developed during the pandemic to marketing analytics. The result for enterprise marketing organizations is a crystal-clear understanding of the results and ROI of their various marketing programs and initiatives.

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“In my decades focused on brand marketing and advertising, I’ve spent billions of dollars, and the best we were able to do was guess. I just want to know, if I spend $1, do I get a result that’s worth $1? For that, Alembic is a real game changer,” said Jeffrey Katzenberg, founder of DreamWorks and WndrCo, the lead investor in Alembic’s series A funding round.

Almost all marketers struggle with attribution on brand spend. Alembic uses AI techniques developed for scientific research applications to predict ROI from marketing. It is an AI that curates billions of rows of data in real time to find causality and help companies drive more revenue. Alembic brings in data from the entire revenue funnel, from analyzing high-level unstructured data such as TV, radio, podcasts, sponsored and earned media, to mid-funnel web and digital metrics, to revenue-focused elements like e-commerce product performance or leads and opportunities in CRM.

Until now, marketing analytics was based on correlation rather than causality, and dashboards provided performance snapshots that required interpretation and planning based on imprecise data. The new Alembic release provides reasoning rather than only reporting. Its interface is an AI prompt rather than a dashboard, and it can answer any question in seconds regarding an enterprise’s marketing mix, including generating a complete ROI forecast or a marketing plan based on proven causality and real data.

“The new Alembic release is the first to use causality mathematics and composite AI to mathematically demonstrate causality in large datasets, initially focusing on marketing ROI. Imagine being able to trace and understand the direct impact of a brand’s spend, similar to reversing the butterfly effect of big brand moments,” said Tomás Puig, Alembic co-founder and CEO.

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Alembic solves marketing problems including:

• Inability to perform cross-domain and business line analysis
• Absence of proper attribution
• Proliferation of communication channels
• Limited developer resources
• Marketing mix modeling (MMM) that lags by three to nine months
• Stringent privacy regulations
• Significant cross-channel sponsorships
• Frequent changes in technology and publisher APIs

The importance and benefits of composite AI

Many analysts like Gartner state that composite AI is a key technological trend. Their research indicates that composite AI is not merely a combination of different AI technologies but a synergistic integration that results in systems that are more adaptive and capable of handling complex tasks. By combining various AI components, composite AI aims to create systems that can understand, learn and respond in a more sophisticated manner. The final goal is to create AI systems that will effectively augment human capabilities and drive transformation across industries. The application of composite and multi-model systems improves AI itself which brings us closer to that goal.

Alembic’s composite AI solution for marketing analytics includes seven major components:

1. Contextually aware ingestion (extract, transform and load): In marketing analysis, the ability to efficiently gather, process and analyze data from various sources is paramount. Alembic’s approach typically involves ingesting data in its raw state by first extracting and then loading it, followed by applying transformations tailored to specific needs, like time series reconstruction or signal processing.
2. Time-series reconstruction and classification: In marketing, lifetime value indicates the total value a customer brings over their entire relationship with a company. Time-series data show how that value changes day by day or month by month, helping marketers understand when and why the value goes up or down, which can guide future strategies.
3. Applied observability: According to Gartner, “The future is not about predicting; it’s about preparing. The value proposition of applied observability involves a shift from reactive to proactive. IT leaders’ highly orchestrated use of actual stakeholder actions, rather than intent or predictions, drives competitive advantage.”
4. Causal AI system: According to Gartner, “Causal artificial intelligence (AI) identifies and utilizes cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously. It includes different techniques, such as causal graphs and simulation, that help uncover causal relationships to improve decision making.”
5. Geometric data and graph construction: Using both geometric data and graphs together provides a fuller picture of the relationships between events. This can help marketers better understand if one event is causing another, or if they are related in some other way.
6. Graph neural network, prediction and reasoning layer: A graph neural network is a dynamic computational model. It can learn from patterns in the data and use this knowledge to make predictions about unseen parts of the graph. For businesses, this means being able to anticipate customer needs, market trends or optimal locations for expansion. Without predictive capabilities, companies are limited to reactive decision-making based on historical data.
7. Generative AI layer (LLM): Alembic’s strategic use of generative AI as a “voice and universal translator” for its data insights removes the possibility of AI “hallucinations,” ensuring the accuracy of metrics surfaced to the user.

Alembic is deploying an AI supercomputer to power its new composite and causality AI systems, enabling Alembic to infuse its applications with differentiated AI capabilities and the same level of power and sophistication that researchers use to advance climate science, digital biology and the future of AI.