Market Research For Institutional Users
Our PROCESS
Lium’s proprietary Data Synthesis methodology harvests, digests, back-tests, and refines big data from many sources across the broad energy marketplace.
Next, our models and algorithms turn this data into predictive research shared with members in dashboards, data downloads, and the written word.
Through selective distribution, we provide a more focused product that maximizes time spent building data, publishing research, and engaging with members.
The end result? A rich suite of research products that equip Lium members with unique tools to leverage the growing amount of big data in energy and industrials. Along with independently collected and verified data, members benefit from our team’s firsthand observations, human experience, and field-level insights.
Primary Data
Our team of data scientists build robust and heavily back-tested primary datasets in the energy and industrials markets
Predictive Modeling
Predictive algorithms are developed and applied to our big data to generate macro predictions and company revenue projections
Human Touch
Field level observations, channel checks, human experience, and common sense are blended into the big data equation to optimize conclusions about future outcomes
Practical Presentation
As former energy investors ourselves, our team thinks like our clients - Lium data interfaces and reports emphasize interaction, flexibility, visualization, and transparency
Before joining forces to create Lium, our two co-founders each independently built and successfully ran their own market research firms (Coras Research and Infill Thinking). Each of these platforms established long track records of accurate forecasts and influential publishing in energy markets. Lium’s approach is a hybrid research model incorporating a data service, equity research, and thematic market intelligence.
Lium’s coverage focuses on the holistic (and rapidly evolving) U.S. energy sector – from power and renewables to oil and gas.
At its core, Lium is a platform of “research idealists” and “data junkies” aligned around the singular goal of creating better returns in energy capital allocation decisions.