Using Conjoint Analysis for Product Marketing Strategy

Savvy marketers in all industries must continually evaluate their product offerings to ensure that they are aligned with changing customer preferences.�Fortunately for marketers, conjoint analysis provides a proactive and efficient way to determine the value customers place on individual product attributes as well as their willingness to pay for them. This technique is one of the most valuable research methods at a manager�s disposal for developing product marketing strategy.

Conjoint analysis defines products as a set of various attributes and then tests how individual consumers react to a number of alternatives so a marketer can figure out each attribute�s importance and most desired level. The technique evaluates how customers make tradeoffs between various product features, and outputs a numerical assessment of the relative importance customers attach to attributes of a product set.

Conjoint analysis has been widely adopted across many industries. Its use is widespread among managers concerned with product development strategy. The technique owes its popularity to the possibilities it creates since, after the contribution of each attribute to the consumer�s overall evaluation is determined through statistical analysis, the marketing researcher can:

  • Determine the product offering with the optimum combination of features.

  • Predict the market shares of alternative product concepts.

  • Adjust pricing based on what customers say they are willing to pay for a given feature.

  • Compute the relative value contribution of each attribute within the product offering.

Many companies only evaluate their product offerings using real-time purchase data from the marketplace. While this approach does yield important and valuable data, if marketers are not allowed to test product attributes before a product is launched, they are limited to reacting to customer tastes. Decisions based entirely on information obtained after a review of a product�s performance in the marketplace often come too late for most companies. At that point, there is no way to avoid the high costs associated with launching an unsuccessful product.

Without proactive product testing, there is also the possibility of a missed opportunity to launch a product that appeals to a previously undiscovered market segment. Furthermore, drawing conclusions based on results from sales data can become extremely complicated. Additional factors that influence the purchase decision, such as in-store placement, cloud the picture and make it extremely difficult to distinguish why one product offering is more successful than another.

Conjoint analysis could work well with service offerings, and there are at least two or three ways to conduct the analysis, depending on the specific information sought. The basic method is a powerful and cost-effective way for the researcher to proactively test how the market will react to concepts before they are launched.

Conjoint Analysis in Action

An ice cream maker would like to conduct research to determine the ideal combination of product size and price points for a new flavor. The company tests various combinations of three potential prices and sizes by surveying a representative sample of the consumer population for their opinions. The preferred combinations of price and size were ranked from 1 to 9, with 1 being the least preferred and 9 being the most preferred.

Using very basic conjoint analysis, researchers estimated the value consumers placed on each attribute, determining the degree to which each affected overall preference. In this example, increasing the size of the ice cream packaging had more of an effect than raising the price, judging by the average change in customer preference from the largest to the smallest attribute. The drop-off from the average worth of the product size attributes (7.7 - 2.3 = 5.4) is higher than the drop-off from the highest to the lowest price (6.7 - 3.3 = 3.4). Therefore, when we move from the largest product size to the smallest (half gallon to bars), the average part-worth that consumers prescribe to product size (5.4) is considerably larger than when we move from the highest price to the smallest (3.4). Based on this, the ice cream company now knows that it would best be served by offering larger sizes at higher price points.


Product Size




Avg. worth











Half gallon





Avg. worth




9 = most preferred

1 = least preferred

Now the company can create a product marketing strategy based on these results. It also can exploit various preference groups within the larger population, since the results allow marketers to segment respondents by combining known demographic data with their product preferences. From this, the ice cream maker could offer bundles of products to different groups of customers with offerings that match their stated preferences and a message that matches their demographics. Conjoint analysis has made all of these things possible, along with saving the company money by eliminating the need to launch products that were doomed to fail.

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