Choice-Based Conjoint is receiving a lot of attention lately. Researchers and clients alike value the realistic context of making choices from sets of available products. However, using CBC has been bittersweet. Because choices are an inefficient way to collect information about preferences, we have been compelled to use aggregate analysis.

Researchers and academics have argued that respondents are unique. The world does not consist of clean market segments. Aggregate models which assume no respondent differences cannot be optimal. Last year we made an important step toward recognizing respondent heterogeneity with the release of our Latent Class Module. But no software has been available for estimating individual-level utilities from Choice data until now.

The ICE (Individual Choice Estimation) Module is an add-on to the CBC system. With ICE, you'll get:

• Rapid estimation of individual-level utilities
• More accurate simulation models
• A powerful market simulator (as used in ACA/CVA), featuring,
  • an easy-to-use user interface with pull-down menus
  • automatic sensitivity analysis and multi-scenario batch processing
  • respondent-level weighting segmentation scenarios without having to re-run logit multiple times

Our goal at Sawtooth Software is to provide sophisticated research tools for those who are not necessarily specialists in statistics. The ICE Module upholds that tradition. As with all our products, the ICE Module comes with a 60-day money-back guarantee.

Ten Reasons Why You Need ICE

1. Increased Model Accuracy. If your respondents have unique preferences, you'll get more accurate market simulations with ICE. We've analyzed dozens of real and synthetic data sets and confirmed that ICE generally improves predictions for holdout concepts relative to aggregate logit and latent class.

   One data set was collected by Huber and Zwerina (1996). They interviewed 50 MBA students and asked 30 choice questions. The best Mean Absolute Error (MAE) values for share predictions for aggregate logit, Lclass and ICE were 12.9%, 6.3%, and 2.7%. If we only use aggregate logit, our aggregate share predictions are off by an average of 12.9 share points. With ICE our predictions only miss by 2.7 share points. We confess that this data set is atypical, and represents the most dramatic margin of victory we've seen for ICE. Even so, results across many studies show ICE generally gives more accurate predictions.

   Another data set was contributed by Pathfinder Strategies, in Australia (1997). Sample size was 880, with 21 tasks per respondent. The best MAE results were 8.2% for aggregate logit, 8.1% for lclass and 7.9% for ICE. Diagnostics from lclass suggested this market was relatively homogeneous. Even so, ICE predictions were slightly more accurate than aggregate modeling.

2. Greater Flexibility in Simulations. Having individual-level utilities means not having to run logit separately for different segments before computing segment-based share predictions. ICE uses the same simulator as used in Sawtooth Software's ACA and CVA conjoint systems. This is an easy-to-use tool you can confidently give your clients. You can merge segmentation variables and toggle quickly between segments, process multiple scenarios, and even conduct automatic sensitivity analysis. ICE also gives capabilities for including customized calibration concepts (as offered in ACA) in your CBC interview. Calibration concepts scale the data for use in Purchase Likelihood simulations, which aren't available in standard CBC.

3. No Need to Be Limited to Small Designs. The ICE module leverages information from all respondents to estimate results for the individual. Designs don't need to be kept impractically small. A CBC study with six attributes and 30 total levels can be comfortably implemented if you ask around 20 to 30 tasks per individual. We have seen ICE estimation match or exceed aggregate modeling accuracy with as few as 8 tasks per respondent, though we would never suggest using so few tasks with ICE.

4. Estimation is Relatively Fast. Recently, leading academics and researchers have been investigating the use of Hierarchical Bayes for estimating individual-level utilities from Choice. Their run times have been measured in terms of dozens of hours for typical data sets. The typical ICE run takes about 30 minutes on a Pentium 166 MHz processor.

5. Reduces IIA Problems. A long-standing complaint against logit has been its IIA assumption, often referred to as the red bus/blue bus problem. Very similar products tend to capture too much net share in competitive logit simulations. With individual-level modeling, this effect is less problematic. If you use the First Choice model, it is entirely resolved.

6. Cleaner Data. Conjoint analysis can suffer from inconsistent respondents and from utilities which seem out of order (reversals) for variables such as price. ICE reports a goodness of fit for each individual, and the ICE simulator can discard those cases which don't meet your specified threshold. Utility constraints can also be imposed for attributes such as price which have a definite order of preference. Both of these safeguards can improve the quality of your data.

7. Individual-level Utilities Are Valuable for Additional Analyses. ICE utilities may be used in standard cross-tabs and for additional multivariate modeling using software of your choice.

8. Straightforward Significance Tests. With individual-level models, statistical tests based on sampling variation are straightforward. The market simulator reports standard errors for shares of choice. Additionally, individual-level utilities, attribute importances and shares of choice are available in ASCII files for performing t-tests.

9. If You Own Lclass, ICE Is the Logical Extension. Lclass users should be pleased to know that their Lclass Module still offers unique value to Choice analysis. Even though ICE can estimate utilities "from scratch," it usually benefits from a latent class starting point. We also believe that latent class is a robust approach for investigating market structure and assigning segment membership using choice data.

10. ICE Is Easy to Use. The ICE system has a simple menu system which leads you each step of the way. ICE uses your existing data file (studyname.CHO) and attribute definitions (studyname.ATT).

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