Feature and roadmap prioritization
Quantify which attributes actually drive preference so teams stop prioritizing by opinion and start prioritizing by impact.
Choice Modeling
Model real trade-offs. Prioritize what wins.
AddMaple helps teams move from preference data to defensible decisions with conjoint, MaxDiff, and TURF in one connected workflow. Build scenarios, quantify likely outcomes, and publish stakeholder-ready outputs without spreadsheet gymnastics.
Built for insights teams and agencies that need rigor and speed together: statistically tested outputs, rapid iteration, and clear delivery to decision-makers.
Solutions teams use choice modeling for
Packaging and offer design
Model trade-offs across bundles and concepts, then simulate likely share outcomes across candidate offers.
Message and proposition testing
Test combinations of claims, positioning, and product elements to understand what creates the strongest pull.
Portfolio and segment strategy
Compare preference patterns by audience segment and identify where differentiated offers are most likely to win.
Why AddMaple is different
Super-fast proprietary stats engine
Choice models run on AddMaple’s proprietary statistical engine so teams can move from setup to outputs quickly, even when studies are complex.
Built for fast iteration and exploration
Test, compare, and refine scenarios rapidly. Explore what-if options without waiting through long analyst handoff cycles.
Made for practitioners, not just statisticians
You don’t need a stats degree to get value. AddMaple guides teams from raw data to insight-ready outputs in seconds.
Bring data from anywhere
Ingest survey and research data from multiple sources, then run choice modeling in one connected workflow.
What’s supported in the stats layer
The choice modeling workspace is backed by production statistical methods already available in AddMaple’s analytics engine.
| Method | Support | Core outputs |
|---|---|---|
| Conjoint | Aggregate log-odds conjoint with part-worths, attribute importance, and scenario simulation. | Part-worth estimates, importance share, scenario utility/share, confidence intervals, log-likelihood. |
| MaxDiff (Best-Worst Scaling) | Best/worst task modeling with utility/share estimation and ranking. | Net scores, utilities, shares, confidence intervals, item rank, task diagnostics. |
| TURF | Reach/frequency optimization over multi-select or binary sources with greedy or exhaustive search options. | Best combinations by size, incremental reach, item reach, evaluated combinations count. |
Modeling capabilities
- • Scenario simulation for concept and offer comparison
- • Jeffreys or uniform priors
- • Monte Carlo confidence intervals
- • Weighted respondent handling (total weighted sample)
- • Task completion and model diagnostics
- • Direct transition from model outputs to shareable dashboards
From models to decisions
Run models, compare scenarios, and move straight into AddMaple dashboards and stories for stakeholder review. Keep the full path visible from method to evidence to recommendation.
This is choice modeling built for teams that need both statistical rigor and delivery speed.
From data to decision in one loop
- 1Ingest choice study data and map model inputs quickly.
- 2Run conjoint, MaxDiff, or TURF with transparent configuration.
- 3Generate utilities, shares, and confidence ranges.
- 4Compare scenarios and segment differences in minutes.
- 5Publish decision-ready outputs to dashboards and stories.
Turn preference data into confident decisions.
See how your team can run choice modeling workflows end-to-end and deliver decision-ready outputs faster.