CASE STUDIES · LIVE TOOLS · SHARED EXPLANATORY APPROACH
Case studies that show the approach in action.
A concept test, a 1963 presidential approval survey, and NBA shot logs — built with the same shared architecture across commercial research, public opinion, and operational data. Three domains, one workflow — proof that the pattern travels.
Start with the domain that interests you most, or compare across all three to see how the same underlying approach travels.
Commercial Research
Concept Test
In a 2025 craft-beer concept test, 54.7% of beer-buying consumers said they would purchase the brand. The case study asks what package perceptions drove that intent and how it shifts across income, region, and brand awareness.
Dataset
Beer concept-test consumer survey
Main outcome
Top-2-box purchase intent
Sample size
803 respondents
What you'll see
The full Mirror workflow on a commercial concept test, brand-team-ready
Five outcome models across the concept-test report (purchase intent, package appeal, brand fit, high-quality, premium)
Built in partnership with emi Research Solutions on anonymized survey data
Brand-team-ready: results in points and percentages, not log-odds
In the 2014–15 NBA season, players made about 35% of their three-point attempts. The case study asks what shot conditions drove that rate and what might have changed it.
Dataset
SportVU shot logs
Main outcome
Three-point make rate
Sample size
~75,000 attempts
What you'll see
The same workflow on a different data shape — event-level, no sampling
Season and data-provenance framing for the SportVU sample
Multiple ways to simulate shot-condition changes
Direct comparison of exploration, tuning, and model-building paths
Each case study follows the same broad sequence from outcome to explanation to scenario testing.
1
Start with the outcome
A visible topline anchors the study and gives users a concrete entry point.
2
Inspect the source data
Users can move from descriptive distributions to more focused analytical questions.
3
Model the drivers
The explanatory layer makes it easier to see what measured factors matter under stated rules.
4
Test scenarios
Interactive tools let users see how outcomes change when conditions are altered.
Working with Electric Insights
Want a case study like these for your question?
The tbree examples are built on public data. The Services page describes the three ways the same approach can be applied to your data, your question, and your decision.