Price Attribute in Adaptive CBC
Top  Previous  Next

Price is often included in choice studies, and initial evidence suggests that ACBC should be a strong approach for studying the impact of price on choice.


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 (up to 3) 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, and 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 disturbed randomly) should be rounded to, say, the nearest $100, or $5, $1, or $0.10 (for whatever currency symbol is appropriate).

To specify 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, and 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). Under summed pricing, we may estimate price as a continuous function (described further in the section entitled "Modeling the Price Function"). When treating price in this manner, we are able to partial-out the effect of price vis-a-vis 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 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 will naturally 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.


When Level-Based Prices Don't Make Sense

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 out. 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%). Then, you could use the BYO question as if it was simply a series of select questions asking respondents which levels are preferred. 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).