Price Attribute in Adaptive CBC

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Price is often included in choice studies and evidence to date suggests that ACBC is a strong approach for studying the impact of price on choice.  Although studies comparing ACBC and CBC have found very strong similarities in the respective part-worth utilities, the ACBC utilities tend to have more precision and price tends to get a bit more importance (relative to the other attributes) in ACBC relative to CBC.  


Summed Prices


In traditional conjoint studies, the researcher applies price as another attribute (factor) in the study design, and specifies typically 3 to 5 levels of price (e.g. $100, $150, $200, $250).  One certainly could take this approach with ACBC.  The problem with such an approach is that $100 is sometimes shown with a collection of high-end features and $250 is sometimes shown with low-end features.  With our standard CBC software, conditional pricing allowed the researcher to specify that certain attributes should be conditional on price and always include a premium or discount.  With ACBC, we have taken the idea of conditional pricing further, by allowing the researcher to specify incremental prices for up to all attributes in the study.  When displaying the total product price, we sum the prices associated with the levels across all attributes in the product concept, then we vary that summed price by a randomly drawn price variation (such as anywhere from -30% to +30%), as specified by the researcher.  The researcher can also indicate that prices (after being perturbed randomly) should be rounded to, say, the nearest $100, or $5, $1, or $0.10 (for whatever currency symbol is appropriate).  


To specify Summed Pricing (prices per attribute), do the following:


Add a level to your attributes list for the Price attribute

From the Attributes tab, use the drop-down dialog to indicate that the attribute is a "<Summed Pricing Attribute>"

Use the Pricing tab to specify any base price, plus prices per attribute levels in your study


Using the Summed Pricing approach leads to product concepts that show realistic prices (and therefore reflect higher utility balance across product concepts relative to traditional CBC).  Products with high-end features will generally carry higher prices; products with low-end features will generally carry lower prices.  Under Summed Pricing, thousands of potential unique prices will have been shown to respondents and the utility function is estimated by fitting a linear (or non-linear function), described further in the section entitled "Modeling the Price Function".  When treating price in this manner (due to the random shock we apply to summed prices), we are able to separate (partial-out) the effect of price from the effects of other attributes' levels.  Therefore, one can interpret the utilities for the other levels independent of the price increments that were associated with them (which one cannot do when using conditional price in our CBC software).


Choosing Price Increments


A challenge with using summed price is selecting appropriate price levels to associate with different attribute levels.  For example, for the attribute RAM, incremental prices might be:


1 GB RAM   +$0

2 GB RAM   +$200

4 GB RAM   +$500


This indicates that 2GB laptops should be shown at a price $200 higher (on average) than 1GB products.  This imbedded price for RAM will be transparent to respondents within the Screener, Choice Tasks, and Calibration Sections, as only the total price will be shown for the product concept.  However, within the BYO task, respondents will see that selecting 2GB RAM adds $200 to the base price of the product and that 4GB RAM adds $500 to the base price.  In essence, in the BYO (configurator) task respondents are trading off the value of improving RAM vs. increasing the price.  This provides one tradeoff of RAM vs. price among the many choice tasks trading off product features (including RAM) with price across the total ACBC survey.


Some attributes may not require associated levels of price, as the inclusion of one level or another really doesn't affect the price of the product in the real world.  Examples include colors, styles, and often brands.  Such attributes could be given incremental prices of zero, and choosing levels for these attributes within the BYO task would not change the overall price of the selected concept.


Researchers may be concerned that the price levels they choose to associate with levels in the study might have a significant effect on the outcome.  If we could instruct a computer to answer the ACBC questions according to known cutoff rules and known utilities (with some level of respondent error), the choice of price levels (within reason) attached to attributes should not bias the estimates of utility for attribute levels and the price function.  It would be ridiculous to use outlandish prices attached to RAM for a laptop such as it costing an extra $1,000 for each level of RAM.  Such a mistake would certainly affect the final utilities.  


In one of our methodological (split-sample) experiments, we varied the price levels attached to some of the attributes in our study to see if the resulting part-worth utilities and predictive ability (in terms of hit rates and share prediction accuracy) resulting from the two questionnaire versions would be affected.  Our experiment showed that after estimating price as a separate linear function, the remaining part-worths associated with the attributes that we manipulated were essentially the same.  The hit rates for one variant of the experiment vs. the other were not significantly different.  But, we need to see additional experiments on this point before we declare that it doesn't matter what prices (within reason) you associate with attribute levels when using summed pricing.


So, our recommendation is to select reasonable incremental prices for price levels, but not to be overly concerned if your incremental prices deviate from average willingness to pay for the different attribute levels.  If you find it very difficult to decide on incremental levels for some attributes, preliminary qualitative research could be conducted to help determine appropriate prices.


Specifying a Single Continuous Price Variable


For some projects you face, the idea of assigning price increments to specific attribute levels and showing these to respondents just doesn't seem to work.  Perhaps it's impossible to think about price premiums by feature, or specific prices cannot be agreed upon with the client.  If that is the case, you can specify a single base price for the product concept (along with the desired range of price variation to test, such as from -30% to +30%).  This gives you the ability to fit a multi-segmented piecewise price function to the continuous price variable, discovering the specific points of non-linearity for each respondent rather than having to specify specific price increments prior to fielding the study.  When the BYO question is displayed, ACBC recognizes that no level-based prices are attached to feature attributes, so the BYO question deletes the pricing column and simply shows a series of select questions asking respondents which levels are preferred (for each attribute except total price).  However, this might seem unusual for ordered attributes, where one level is clearly superior.  If that occurs, another option in the software is to omit the attribute from the BYO question.  In that case, the software assumes no BYO level has been selected and will sample equally across all levels of that attribute in the generation of near-neighbor concepts (see the section entitled How Concepts Are Chosen for more details).

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