Learn how ACBC can make your conjoint more engaging for respondents and more insightful for researchers
What users of research need to know about Choice-Based Conjoint (Discrete Choice)
Describes a method for testing customer interest in competitive offerings and identifying those most vulnerable to churn
Describes the use of a conversion score to make market segmentation solutions more actionable
Discusses classification systems, such as CHAID and CART, used to analyze markets or find groups for targeted advertising
When your attributes don’t have levels, look outside conjoint to other methods such as MaxDiff Scaling
Total Unduplicated Reach and Frequency (TURF) helps understand the combination of items that will reach the largest proportion of your market
Reveals inconsistencies in the use of this pricing method; generally recommends avoiding its use
Technical methodological details of reverse segmentation analysis.
How reverse (object-based) segmentation helps segment based on both attitudes/behaviors and targeting variables
Discusses a flexible conjoint methodology that is useful for researching bundling, menu configuration, and multi-step choice processes
Using MaxDiff to obtain preference/importance scores for multiple items without scale bias
Discusses challenges identifying drivers of satisfaction and loyalty, and examines the Averaging Over Orderings (AOO) Regression method for improving outcomes
Explores how to quantify incremental value of individual product features via conjoint analysis
Reviews confidence level and margin of error to help you select the correct sample size for your study
Includes rules for cleaning online survey sample, including specific cleaning suggestions
Reviews clustering, factoring, modeling, behavioral, attitudinal, and a general how-to when considering a segmentation project
How Conjoint Handles Really Complicated Ideas: Adventures in Advanced Choice-Based Conjoint Applications
Discussions of alternative-specific designs, prohibitions, conditional pricing, summed pricing, Hierarchical Bayes, volumetric CBC, and more
The basics of choice-based conjoint analysis (discrete choice modeling)
A customized, interactive survey experience that “learns” from respondent choices, picks up non-compensatory decision making behavior, and allows flexible price modeling via summed pricing.
A review of both direct questioning techniques like willingness to pay, monadic designs, and van Westendorp, and trade-off techniques such as conjoint, for use in pricing research.