Alternatives to IBM SPSS Conjoint Analysis

Published by Staff author in Alternatives · 22 December 2025
Tags: IBM, SPSS, Conjoint, Analysis, XLStat, R, conjoint

Introduction

While IBM SPSS Conjoint Analysis is long established in the metric (ratings-based) conjoint analysis software space, it is expensive. It also has various limitations: 1.) Its simulator ignores external effects. 2.) It's decision rules, while including the two most popular options - Max Utility and BTL - makes no attempt to relax the IIA assumption in BTL. 3.) Perhaps most difficult to deal with from a commercial point of view is that the simulator is not capable of estimating a 'None' share. The simulator is also fairly difficult to use when running alternative scenarios.

Pricing starts with a base subscription at $99 USD per user per month, but the conjoint module is included in the Premium edition or as an add-on for subscription plans. Perpetual licenses for the Premium version can reach $23,800 per user, with discounts available for students and educators via GradPack or Faculty Packs. A free trial is offered to test features.

In this article we look at alternatives to IBM SPSS which are also capable of offering a metric approach - an approach which has numerous advantages over choice-based conjoint.

1. Metrisim Conjoint Analysis Software

Metrisim is a cloud-based software suite specializing in metric (ratings-based) conjoint analysis, aimed at market researchers, analysts, and academics seeking cost-effective tools. it also offers a service to business users who don't have the time or expertise to use conjoint analysis software.

One of its key advantages is reduced survey costs. Lower costs are realized, since metric conjoint can estimate parameters at the individual level - as opposed to CBC which as a rule of thumb - demands at least 300 interviews in order to reliably estimate global parameters.

Metrisim is a full-suite including the 3 key modules needed for conjoint analysis: an experimental designer (d-efficient via a Federov approach), an estimator for partworth calculation and a simulator for market predictions, and segmentation for cluster analysis.

Core features encompass an ability to estimate a 'None' percentage in simulations, no vendor lock-in / data privacy approach where you keep everything needed to recreate a project on your own PC and simply upload CSV (spreadsheets) to regenerate a project, price simulations with interpolation capability and automated curve estimation, decision rules like Max Utility, BTL, BTL ds (with differential substitutability), and Prof. Paul Green’s Alpha exponent which allows for a continuum of adjustment between the extremes of Maximum Utility and BTL decision rules.

It also supports external effects modeling (either explicitly through relevant sets or indirectly through calibration), respondent weighting for improved representation of the target population and automated PowerPoint slide generation (places and centers charts on each slide). The estimator module estimates partworths by first estimating coefficients for a dummy coded version of the design using multiple regression and then mean centering these to ease interpretation – the intercept being adjusted to accommodate. It also includes R-square quality checks to exclude poor respondents. The compatibility with R conjoint package allows further flexibility.

Potential cons involve its unique spreadsheet (CSV) driven approach - which some may find takes getting used to - and fewer community resources compared to established tools.

Pricing offers a Demo Plan (free, restricted to demos), Simulator Only ($20/month), and Full Suite ($49/month). A service for study design, survey setup, analysis, and reporting is also offered (pricing on request).

Comparatively, Metrisim is more affordable and efficient than SPSS, more affordable than Sawtooth, and closer to XLStat in affordability - but cloud-native, and with better simulation features. It is more user friendly than R. Ultimately it's positioned for those valuing metric conjoint analysis advantages.

2. XLStat Conjoint Analysis

XLStat serves as an add-on to Microsoft Excel, transforming it into a statistical powerhouse with a dedicated conjoint module for marketing and product research. It supports full-profile conjoint analysis using ratings or rankings. The tool employs OLS-based analysis of variance and monotone ANOVA for handling monotonic response transformations, preserving individual heterogeneity in results.

Core features include calculation of partial utilities and importance measures per variable, individual-level outputs, and classification methods like agglomerative hierarchical clustering and k-means for segmenting respondents. It also offers market share simulations with four methods, customizable charts, and seamless Excel integration for data handling—no coding required. This makes it accessible for quick setups in small to medium studies.

Reviews on G2 and Software Advice highlight its intuitiveness, with scores around 4.6/5, praising excellent tutorials and practicality for other unrelated modules such as multivariate analysis. Pros encompass ease of use, affordability for students (with discounts), and strong value for decision-making in business contexts. Cons include dependency on Excel's constraints, and calls for lower prices or more discounts for loyal users.

Pricing is structured as a 12-month individual subscription, with editions from Basic ($295/year) to Premium ($1,495/year), making it cost-effective compared to enterprise tools. A free trial is available.

Comparatively, XLStat is more budget-friendly and user-friendly than SPSS or Sawtooth for Excel users, but less well featured than Metrisim. It's favored in academic and small business settings, though some debate its scalability for large datasets.

3. Sawtooth CVA

Sawtooth CVA (Conjoint Value Analysis) is a legacy component of Sawtooth Software's suite, and is capable of traditional ratings-based conjoint methods. In order to distinguish it from there later partial profile approaches they sometimes call it 'full profile' - which is confusing because CBC is also 'full profile'. It's traditionally claimed that ≤6 attributes should be used (although there is some academic literature showing as many as 17 have been used successfully for this type of conjoint analysis) perhaps to avoid cannibalizing its adaptive offerings (ACA and ACBC). It is more similar to traditional CBC in this regard which would also be limited based on similar cognitive load logic. It is suited to web, CAPI, or paper surveys.

It supports up to 30 attributes and 15 levels (text/graphics) in spite of its recommendation to limit this to 6 or less, with tools for single-concept or pairwise designs, D-efficient design, and utility estimation via OLS or hierarchical Bayes (HB). Advanced options include market simulators, sensitivity analysis, and customization like conditional pricing/HTML. It's part of Lighthouse Studio for broader conjoint types like CBC and ACBC.

G2 reviews rate it highly (4.5/5+), commending the overall brand, free trials up to 50 responses, and dedicated support including conferences. Pros highlight robustness, flexibility for complex designs, and industry-standard status for consumer research. Cons note its legacy nature (a sense it has been neglected in favour of a push to sell its other offerings), higher pricing, and Windows/desktop focus for advanced versions.

Pricing requires contacting for quotes; Discover starts at $4,500/user/year, Lighthouse at $10,900/year, with no per-respondent fees and academic options. Free tier for small surveys.

In comparisons, Sawtooth outperforms others in conjoint specialization but is pricier than XLStat or R, with debates on its value for non-experts.

4. R Conjoint

The R 'conjoint' package implements traditional conjoint analysis, calculating utilities from stated preferences, and integrates with libraries like AlgDesign for experiments and ggplot2 for visuals. It's ideal for data scientists in academia.

Features include utility estimation, simulations, and custom models, with dependencies for clustering and plotting. No GUI however.

Community feedback on Stack Exchange and blogs appreciates its flexibility, with pros like zero cost, community support, and advanced customizations. Cons include the need for programming skills, potentially steep for beginners, and no means to account for external effects while decision rules are limited. It is also hard to run simulations in an interactive workflow as it is script based and therefore might not suit business users.

Pricing: Free under GPL license.

Comparisons position R as more adaptable than GUI tools but less accessible, suitable for custom needs over standardized workflows.

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