The Basics of Conjoint Analysis

The basics of conjoint analysis are easy to understand. It should only take about 20 minutes to introduce this topic so you can appreciate what conjoint analysis has to offer.

In order to understand conjoint analysis, let's look at a simple example. Suppose you wanted to book an airline flight and you had a choice of spending $400 or $700 for a ticket. If this were the only consideration then the choice is clear: the lower priced ticket is preferable. What if the only consideration in booking a flight was sitting in a regular or extra-wide seat? If seat size was the only consideration then you would probably prefer an extra-wide seat. Finally, suppose you can take either a direct flight which takes three hours or a flight that stops once and takes five hours. Virtually everyone would prefer the direct flight.

In a real purchase situation, however, consumers do not make choices based on a single attribute like comfort. Consumers examine a range of features or attributes and then make judgements or trade-offs to determine their final purchase choice. Conjoint analysis examines these trade- offs to determine the combination of attributes that will be most satisfying to the consumer. In other words, by using conjoint analysis a company can determine the optimal features for their product or service. In addition, conjoint analysis will identify the best advertising message by identifying the features that are most important in product choice.

In sum, the value of conjoint analysis is that it predicts what products or services people will chose and assesses the weight people give to various factors that underlie their decisions. As such, it is one of the most powerful, versatile and strategically important research techniques available.

A Practical Example of Conjoint Analysis

Given the above alternatives, product 4 is very likely the most preferred choice while product 5 is probably the least preferred product. The preference for the other choices is determined by what is important to that individual.

Conjoint analysis can be used to determine the relative importance of each attribute, attribute level, and combinations of attributes. If the most preferable product is not feasible for some reason (perhaps the airline simply cannot provide extra-wide seats and a 3 hour arrival time at a price of $400) then the conjoint analysis will identify the next most preferred alternative. If you have other information on travellers, such as background demographics, you might be able to identify market segments for which distinct products may be appealing. For example, the business traveller and the vacation traveller may have very different preferences which could be met by distinct flight offerings.

You can now see the value of conjoint analysis. Conjoint analysis allows the researcher to examine the trade-offs that people make in purchasing a product. This allows the reseacher to design products/services that will be most appealing to a specific market. In addition, because conjoint analysis identifies important attributes, it can be used to create advertising messages that will be most persuasive.

In evaluating products, consumers will always make trade-offs. A traveller may like the comfort and arrival time of a particular flight, but reject purchase due to the cost. In this case, cost has a high utility value. Utility can be defined as a number which represents the value that consumers place on an attribute. In other words, it represents the relative "worth" of the attribute. A low utility indicates less value; a high utility indicates more value.

The following figure presents a list of hypothetical utilities for an individual consumer:

Based on these utilities, we can make the following conclusions:

This consumer does not differ much in the value that he or she places on comfort. That is, the utilities are quite close (12 vs. 15).

These utilities also tell us the extent to which each of these attributes drives the decision to choose a particular flight. The importance of an attribute can be calculated by examining the range of utilities (that is, the difference between the lowest and highest utilities) across all levels of the attribute. That range represents the maximum impact that the attribute can contribute to a product.

Using the hypothetical utilities presented earlier, we can calculate the relative importance of each of the three attributes. The range for each attribute is given below:

 Duration:  Range  =   20   (42-22)



 Comfort :  Range  =    3   (15-12)



 Cost :     Range  =   56    (61-5)

These ranges tell us the relative importance of each attribute. Cost is the most important factor in product purchase as it has the highest range of utility values. Cost is followed in importance by the duration of the flight. Based on the range and value of the utilities, we can see that seat comfort is relatively unimportant to this consumer. Therefore, advertising which emphasizes seat comfort would be ineffective. This person will make his or her purchase choice based mainly on cost and then on the duration of the flight.

Marketers can use the information from utility values to design products and/or services which come closest to satisfying important consumer segments. Conjoint analysis will identify the relative contributions of each feature to the choice process. This technique, therefore, can be used to identify market opportunities by exploring the potential of product feature combinations that are not currently available.

Choice Simulations

In addition to providing information on the importance of product features, conjoint analysis provides the opportunity to conduct computer choice simulations. Choice simulations reveal consumer preference for specific products defined by the researcher. In this case, simulations will identify successful and unsuccessful flight packages before they are introduced to the market!

For example, let's say that the researcher defined three flights as follows:

The conjoint simulation will indicate the percentage of consumers that prefer each of the three flights. The simulation might show that consumers are willing to travel longer if they can pay less and are provided a meal. Simulations allows the researcher to estimate preference, sales and share for new flights before they come to market.

Simulations can be done interactively on a microcomputer to quickly and easily look at all possible options. The researcher may, for example, want to determine if a price change of $50, $100, or $150 will influence consumer's choice. Also, conjoint will let the researcher look at interactions among attributes. For example, consumers may be willing to pay $50 more for a flight on the condition that they are provided with a hot meal rather than a snack.

Data Collection

In order to conduct a conjoint analysis, information must be collected from a sample of consumers. This data can be conveniently collected in locations such as shopping centres or by the Internet. In the previous example, data collection could take place at a booth located in an airport or in the office of a travel agent.

A sample size of 200-250 is generally sufficient to provide reliable data for consumer products or services. Data collection involves showing respondents a series of cards that contain a written description of the product or service. If a consumer product is being tested then a picture of the product can be included along with a written description. A typical card examining the business traveller might look like the following:

 

"On your next business flight overseas how likely

would you be  to choose a flight that has all the

following characteristics?  Please circle the

appropriate number from 1 to 10 to indicate

your feelings."



      one stop en route

      extra-wide seats

      Departure time: before 8:00 am

      "double" mileage points

      $200 fee to change ticket





would never                     would definitely

choose this flight              choose this flight

1 2 3 4 5 6 7 8 9 10 Readers might be worried at this point about the total number of cards that need to be rated by a single respondent. Fortunately, we are able to use statistical manipulations to cut down on the number of cards. In a typical conjoint study, respondents only need to rate between 10-20 cards.

This data would be input to the conjoint analysis. Utilities can then be calculated and simulations can be performed to identify which products will be successful and which should be changed. Price simulations can also be conducted to determine sensitivity of the consumer to changes in prices.

What Types of Companies Have Used Conjoint Analysis?

A wide variety of companies and service organizations have successfully used conjoint analysis.

The Western Canada Lottery Corporation used conjoint analysis to determine which features of their games were most important to players. They conducted a number of choice simulations to determine whether modifications to existing games would enhance their attractiveness. Based on the results of this research, the Lottery Corporation made a number of moves to redesign existing products and introduce new lottery products. These including setting plans in motion for the introduction of a daily game and a larger jackpot game.

A natural gas utility used conjoint analysis to evaluate which advertising message would be most effective in convincing consumers to switch from other energies to natural gas. Previous research failed to discover customers specific priorities - it appeared that the trade-offs that people made were quite subtle.

A conjoint analysis was developed using a number of attributes such as saving on energy bill, efficiency rating of equipment, safety record of energy source, and dependability of energy source. The conjoint analysis identified that cost savings and efficiency were the main reasons for converting appliances to gas. The third most important reason was cleanliness of energy source. This information was used in marketing campaigns in order to have the greatest effect.

Finally, the maker of a well-known elastic bandage product used conjoint analysis to determine the price level and estimate share for a new "velcro" bandage.