Electric Insights turns data into
explanation.
Most reporting tells you the answer. We also show what produced it — with explanatory models, plain-language results, and tools you can test yourself.
Live Case Studies
Interactive Tools
Domains
Shared Workflow
From Static Reporting to Interpretable Explanation
Many fields report topline outcomes without showing what drives them. Electric Insights helps close that gap through explanatory modeling, plain-language reporting, and interactive scenario testing.
Standard Reporting
- • Headline outcomes without explanatory depth
- • Limited visibility into what drives the result
- • Little or no way to test alternatives
The Electric Insights Approach
- • Model what drives the outcome
- • Translate results into outcome-level terms
- • Let users test “what if?” interactively
The Goal
- • More interpretable results
- • More transparent assumptions
- • More useful public and professional understanding
Start with the approach, then explore the case studies.
The JFK, NBA, and CPG examples use the same core logic: define the outcome, identify the drivers, translate results into plain terms, and let users test scenarios. The Approach page explains that shared workflow in one place.
Featured Case Studies
Three live examples show how the same explanatory approach can be applied to very different domains.
See the shared approach across all three case studiesJFK Approval
In November 1963, 57% of Americans approved of President Kennedy. What drove that number, and what might have changed it?
Dataset: Harris/Newsweek survey
Focus: explain headline approval and test scenario-based shifts
NBA 3-Point Shots
In the 2014–15 season, NBA players made about 35% of their three-point attempts. What shot conditions drove that number, and what might have changed it?
Dataset: 2014–15 SportVU shot logs (33,362 attempts)
Focus: explain league-wide shot success and test how shot conditions move the make rate
CPG Concept Test
In a beer-category consumer survey, 54.7% of respondents rated their purchase intent for a new package concept in the top two boxes. What drove that number, and what might have changed it?
Dataset: beer concept-test survey (n=803)
Focus: explain top-2 purchase intent and test package-reaction shifts
Building on a CPG concept test or brand study? See our overview for brand and insights teams.
Get In Touch
Interested in applying this approach to a live decision problem, a public dataset, or a current research question? Reach out directly.
Location
Las Vegas, NV