Use case: conjoint & MaxDiff

MaxDiff & conjoint — analyzed on the R engine

DataLion estimates MaxDiff three ways — down to individual-level utilities. Choice-based conjoint runs fully natively: a balanced design, fielding through the survey, estimation on R and a preference-share simulator with willingness-to-pay. 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. Choice-based conjoint runs fully natively: a balanced design, fielding through the survey, part-worth estimation (logit/MNL) and a preference-share simulator with willingness-to-pay. Add relative importance, price sensitivity and segment breaks with significance.

  • 🇩🇪 Made in Munich
  • GDPR-compliant
  • DPA included
  • Hosted in Germany
  • 🌐 Interface in EN, DE, FR & NL

Trusted by research, insights & media teams

  • YouGov
  • SevenOne Media
  • Mediengruppe RTL Deutschland
  • Nielsen Sports
  • mobile.de
  • 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

Conjoint, natively: design, field, estimate, simulate

Choice-based conjoint runs fully natively in DataLion — no detour through a second tool. You define the attributes and levels, DataLion builds a balanced choice design, fields the choice tasks through the native survey, and estimates the part-worth utilities on the R engine (logit/MNL).

In the preference-share simulator you then pit products against each other and read off the preference shares; when a price attribute is present, DataLion outputs the willingness-to-pay. Results compare by segment.

  • Balanced choice design from attributes and levels
  • Field the choice tasks through the native survey
  • Part-worth estimation on R (logit/MNL, no HB)
  • Preference-share simulator with preference shares & willingness-to-pay

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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Top rated

4.5 out of 5 stars on G2 and OMR Reviews

What users say about DataLion

  • Very professional company, attentive to the customer needs, provider of a great software and service.
    Generoso M. CRM Analyst · Automotive via G2
  • The contacts at DataLion are very committed. If you have problems, you can count on help. DataLion reacts quickly to requests for new functions.
    Robert Q. Managing Director via G2
  • User-friendliness, especially for market research topics. Structured backend with many customization options.
    Verified user Market Research via G2
  • The embedding function allows us to generate insights of our data for our audience and customers by far less than half of the usual time needed before.
    Verified user Leisure, Travel & Tourism via G2
Read all 16 reviews on G2 →
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?
Yes. Choice-based conjoint runs fully natively in DataLion: you define attributes and levels, DataLion builds a balanced choice design, fields the choice tasks through the native survey, and estimates the part-worth utilities on the R engine (logit/MNL). In the preference-share simulator you read off preference shares, and with a price attribute DataLion outputs the willingness-to-pay. You can still import and visualize utilities from an external study. DataLion does not offer Hierarchical Bayes (HB) estimation — estimation is aggregate/mixed logit on R.
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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