The Alembic methodology
Making sense of complex data patterns demands advanced mathematics and a scientific approach. Alembic processes billions of interactions instantly while mapping complex cause and effect relationships. The Alembic Intelligence Platform then analyzes, observes, interprets and predicts the outcomes. The result? CMOs know exactly what levers to pull to maximize their investments in marketing.
MORE ON HOW ALEMBIC WORKS >Data ingestion
Data ingestion and normalization takes place across your entire funnel. This includes first-party data like Adobe Analytics, Google Analytics, social media and Salesforce along with third-party data like TV, radio, podcast, foot-traffic, survey data or other sources.
Detect anomalies with limited to no historical data
Detect anomalies with Alembic’s proprietary Spiking Neural Network (SNN) technology, which works alongside the sophisticated causal algorithm, catching occurrences conventional systems miss. The SNN is inspired by human brain architecture, able to analyze complex patterns across massive datasets to detect any outliers.
Compute causal inference and information transfer
Causal inference identified through causal chains made possible by sophisticated anomaly analysis. Alembic goes beyond mere correlation. Anomaly analysis identifies, isolates and establishes genuine cause-and-effect relationships that connect seemingly unrelated events across your ecosystem. This helps you reveal and understand the true drivers for performance improvements.
Understand patterns, commonalities, short and long-term effects
Result focused concise summaries are at your fingertips. Large language models (LLMs) convert insights and predictions into simple plain language, allowing CMOs and other marketers to know exactly what marketing, advertising and other engagement strategies generate and increase revenue.
Meaningful deviations come in many forms and we identify all types of outliers, not just the most visible ones
We employ advanced techniques to scrutinize every data point, uncovering hidden patterns and relationships.
Fading signal pattern
Definition
A pattern (P) that used to exist consistently until a certain point (j), then ceases to appear for the remainder of the observation window. Mathematically.
Example in marketing
An ad campaign that consistently drove traffic for several months but abruptly stops delivering leads (budget cut, API issue, etc.).
Why it is important
A model trained on the “always-on” pattern may miss that this signal has faded. By the time it re-trains, the pattern is gone and might be misclassified or go unnoticed.
Emergence pattern
Definition
The opposite of a fading pattern: a signal that didn’t exist at first but then consistently appears from time (i) onward.
Example in marketing
A new marketing channel that suddenly goes viral—e.g., a TikTok campaign that emerges at time (i) and remains effective afterward.
Why it is important
A static model might ignore this new pattern as a transient anomaly or take too long to re-train and recognize a “new normal.”
Pulse pattern
Definition
A signal that turns on and off multiple times, requiring at least three transitions at times (i), (j), and (k).
Example in marketing
A recurring seasonal promotion—active from (t=i) to (t=j), then off, then active again after (t=k). Or an influencer campaign that runs in short bursts.
Why it is important
Repeated “ups and downs” can either be mistaken for random noise or consistently flagged as anomalies, depending on how naive the detector is.
Constant pattern
Definition
A pattern that is always present throughout the entire observation period.
Example in marketing
A baseline brand awareness or mention volume that never drops below a certain threshold.
Why it is important
A persistent signal can become “invisible” to certain detectors; small changes within that baseline may be overlooked since the model sees it as always normal.
How we solve it
Even if the overall pattern is constant, spiking neurons can detect small internal fluctuations; TCEG can link this constant baseline to other channels to see if it causally drives or is driven by new events.
Flash pattern
Definition
A signal that appears exactly once at some time (i), never to be seen again.
Example in marketing
A one-off viral post or a sudden glitch driving a surge in website traffic or conversions—just once, then gone.
Why it is important
Single spikes can cause false alarms (if interpreted as a critical anomaly) or get labeled as irrelevant and vanish from further consideration.
We have the computer power to calculate every edge, no matter how granular
One of the world’s most powerful supercomputers. Alembic’s privately owned system features a 256-petaflop multi-node NVIDIA DGX cluster. This extraordinary computational capacity enables Alembic customers to benefit from the platform’s proprietary methods at unprecedented scale and speed. Some implementations process over 100 billion rows of data annually. Complexity overcome by computational power.
A privacy-first marketing platform
Privacy is ensured by processing only anonymous aggregate data
Traditional marketing platforms rely on individual user tracking. Alembic takes a fundamentally different and empowering, approach. Aggregating information from diverse sources delivers more comprehensive view of customer engagement and marketing performance without compromising privacy.We analyze collective behavior patterns, not individual journeys
Privacy-first methodology prioritizes aggregate insights over individual tracking. Combining and anonymizing data from multiple sources ensures complete customer anonymity while enabling sophisticated analysis that reveals meaningful patterns and trends across customer touchpoints.
Alembic’s causal AI can ingest and understand over 100 billion rows of data
Alembic’s Marketing Intelligence Platform instantly quantifies and interprets your information, delivering the important insights you need to evolve your marketing and drive more revenue.
The key benefits of Alembic
Automated Intelligence
Access real-time insights without manual dashboard analysis.
Budget Justification
Use clear ROI data for confident CFO conversations.
Campaign Optimization
Gain measurable insights to help refine marketing mix.
Compliance Assurance
No PII or tracking pixels ensures protection of data.
