MaxDiff & conjoint — analyzed on the R engine

DataLion estimates MaxDiff three ways — down to individual-level utilities. Add relative importance, price sensitivity and segment analysis with significance. For conjoint, bring in the utilities and make the trade-offs visible. ISO 27001-certified hosting, GDPR-compliant, made in Munich.

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Interactive DataLion dashboard with importances and utilities from a choice study

DataLion analyzes MaxDiff directly on the R engine with three estimators: count (best−worst), aggregate logit, and random-parameter logit for individual-level utilities. Add relative-importance analysis, price sensitivity as a chart type, and segment breaks with significance. For full conjoint, you bring in the utilities and visualize the trade-offs.

  • 🇩🇪 Made in Munich
  • GDPR-compliant
  • DPA included
  • Hosted in Germany

Trusted by research, insights & media teams

  • GfK
  • L’Oréal
  • Deutsche Telekom
  • eBay
  • Hubert Burda Media
  • SevenOne Media
  • mobile.de
  • Psyma
  • 3 MaxDiff estimators
  • best − worst count-based scores
  • individual utilities (RP logit)
  • 80–99% significance levels

Why choice results often go unused

  • The utilities sit in a stats output nobody outside research reads.
  • It stops at overall scores — who wants what in which segment is never tested.
  • The charts get rebuilt by hand for every presentation.

Three ways to estimate MaxDiff

DataLion estimates MaxDiff on the R engine with three methods: count (best−worst subtraction: times chosen "most" minus "least"), aggregate logit for robust overall scores, and random-parameter logit for individual-level utilities per respondent.

That puts features, messages or claims into a clear, forced rank order — without the "everything is important" problem of rating scales.

  • Count: best−worst scores by subtraction
  • Aggregate logit: robust overall importances
  • Random-parameter logit: individual-level utilities
  • Forced trade-offs instead of "all important"
Ranking of importances from a MaxDiff analysis in DataLion

Bring your utilities, make trade-offs visible

Bring conjoint results from your fielding tool — part-worth utilities and relative importances — into DataLion and visualize the trade-offs between features and price, comparable by segment.

Recompute attribute importance directly with the relative-importance analysis, and show price sensitivity as its own chart type — a basis for pricing and product decisions.

  • Visualize part-worth utilities & relative importances
  • Recompute relative importance on R
  • Price sensitivity as a chart type
  • Compare trade-offs by segment
Overview of DataLion chart types for conjoint and price analysis

Utilities by segment — tested

Break importances and utilities down by audience with subcolumns and nested tables — and test differences between segments right in the table (80–99%, z/chi²/t).

So you see not just the overall ranking, but which feature truly matters for which segment.

  • Importances by segment via subcolumns
  • Significance at 80/90/95/99%
  • z, chi² and t-test, pairwise and complement
  • Nested tables for multi-dimensional views

Results stakeholders can explore themselves

Instead of a static results table, stakeholders get an interactive dashboard where they filter live by segment and compare importances and utilities themselves.

For the presentation, export natively to PowerPoint, Excel or PDF — the analysis stays the same, only the format changes.

  • Interactive dashboard instead of a static table
  • Filter live by segment
  • Native export to PowerPoint, Excel and PDF
  • Share by link or embed
Choice analysis exported to PowerPoint in DataLion

What you can build with DataLion

  • MaxDiff analysis

    Count, aggregate logit & individual-level utilities on R.

    See how →

  • Importance dashboard

    Relative importance interactively, comparable by segment.

    See how →

  • Price sensitivity

    Willingness to pay and price ranges as a chart type.

    See how →

See DataLion with your own data

Start a free trial or book a personal demo — from raw data to a finished dashboard.

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We now work much more efficiently, giving us more time to take care of the derivations and insights from the data for the customers.
Jens Falkenau, Vice President of Market Research · Nielsen Sports
Read the case study →

The platform in detail

Go deeper

Common questions about MaxDiff & conjoint

Which MaxDiff methods does DataLion compute?
Three: count (best−worst subtraction), aggregate logit for robust overall scores, and random-parameter logit for individual-level utilities per respondent — all on the R engine, with no R code to write.
Does DataLion run conjoint models too?
For conjoint, DataLion is the analysis and visualization layer: you bring in part-worth utilities and importances from your conjoint study and visualize the trade-offs, complemented by relative-importance analysis and price sensitivity as a chart type. A built-in HB choice-based estimator is not part of the predefined models.
Do I get individual-level utilities?
For MaxDiff, yes: the random-parameter logit method yields individual-level utilities per respondent, which you can then analyze by segment or feed into further analysis.
Can I test importances by segment?
Yes. Using subcolumns and nested tables you break importances and utilities down by segment; differences are tested at 80/90/95/99% via z, chi² or t-test.
How do I show price sensitivity?
Price sensitivity is shown as its own chart type — willingness to pay and accepted price ranges, comparable by segment — a basis for pricing decisions.

Ready to analyze your choice data?

Try DataLion free with your MaxDiff or conjoint data — from estimation to an interactive dashboard. Or book a personal demo.

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