Electric Insights turns data into
explanation.
We build explanatory models and interactive tools that show what drives outcomes, how much those drivers matter, and what changes under alternative scenarios.
Live Case Studies
Tool Types
Domains Demonstrated
Shared Explanatory Approach
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
How It Works
A shared workflow for moving from a reported outcome to a more usable explanatory account
Define the Outcome
Start with the result that matters: approval, vote choice, shot success, brand choice, conversion, or another measurable outcome.
Identify the Drivers
Build a defensible explanatory model of the measured factors associated with that outcome.
Speak in Outcome-Level Terms
Report implications in plain units such as percentages, percentage-point shifts, rates, or probabilities rather than relying on technical coefficients alone.
Let People Test Scenarios
Turn the model into a browser-based tool so users can explore how the outcome changes under alternative conditions.
One Approach, Multiple Domains
The same explanatory workflow can be applied across public opinion, sports, market research, and other decision settings.
Public Opinion
Explain approval, vote choice, and other headline survey outcomes.
Sports Analytics
Explain outcome rates under changing play or shot conditions.
Market Research
Explain brand choice, evaluation, and simulated shifts in demand.
Strategic Decisions
Explain measurable outcomes and test plausible alternatives.
Start with the approach, then explore the case studies.
The JFK and NBA 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
Two live examples show how the same explanatory approach can be applied to very different domains.
See the shared approach behind both 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
Tools: simulator, explorer, model builder, and related analysis tools
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
Focus: explain league-wide shot success under varying conditions
Tools: all-or-nothing simulator, fine-tuning simulator, explorer, and model builder
The Kinds of Tools We Build
Different domains call for different content, but the underlying tool types travel well.
All-or-Nothing Simulators
Change one condition across the full dataset and see how the outcome shifts.
Fine-Tuning Simulators
Shift conditions gradually to model more realistic changes in the mix of cases.
Explorers
Inspect frequencies, distributions, crosstabs, and relationships in the source data.
Model Builders
Build, compare, and inspect explanatory models and their implications.
Start with a Live Case Study
Explore two working examples of the Electric Insights approach and see how explanatory modeling and interactive simulation look in practice.
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