Historically, researchers and marketers have address issues of market segmentation, brand strength, packaging and pricing, and competitive positioning independently.
However, as markets become increasingly competitive, the impact of making a single incorrect decision among the link set of thousands of choices to launch or reposition a product is often catastrophic.
Conjoint analysis provides researchers with the ability to measure the impact of individual product features and attributes without requiring consumers to evaluate each separately.
Through a simple rating, ranking or selection by consumers, conjoint analysis captures the essence of a product as experienc in the real world. Importantly, conjoint analysis decomposes the product, service or offer into its component parts, enabling researchers to: quantify the importance and value of each element; recombine elements to create products of interest to target audiences; and accurately prict share among a competitive set.
Going mainstream due to
The ability to develop sophisticat online surveys, conjoint analysis has become an important research tool because, as reality dictates, consumers purchase products – not individual features such as the color r or brand ABC. Using conjoint analysis, practitioners can present products, services or offers with thousands of potential combinations to respondents in a user-friendly manner.
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While attribute batteries and other traditional techniques were us in the past to understand basic consumer nes, these approaches are comparatively limit in scale and do not enable the researcher to effectively balance elements consumers find desirable (i.e., specific functionality, etc.) with those they do not (i.e., higher prices, etc.).
While marketers in the advertising and consumer products sectors have been conducting conjoint research for years, additional industries are embracing this technique. For instance, many financial service firms are using conjoint solutions to craft the perfectcrit card solicitation by customer segment, balancing, for example, cash-back or frequent-flier mile reward systems with higher annual fees or interest rates.
Developing an online conjoint
Survey is not difficult, but it does require planning. At the heart of any conjoint analysis is the design, which is most commonly present as a matrix of attributes and levels. The attributes represent the dimensions of the product, service or offer.
Consider a cell phone manufacturer seeking to introduce a new product compos of a monthly fee, includ minutes, scope of coverage, applicable roaming fees, whether or not the phone has Internet access, and brand of the company providing the service; these are the attributes of the design, of which there are six in this example (Table 1).
When establishing the design
It is important that the attributes be independent to the greatest extent possible. That is, removing one attribute from the design does not impact how the respondent will likely evaluate the levels of other attributes. To further develop the design, features that are typically represent as checkboxes in traditional research should be defin as an attribute with two levels – either yes/no, includ/exclud, or other similar choices.
By necessity, nearly all conjoint designs include a Price or Fee attribute enabling the respondent to trade-off price for various combinations of features.
The presence of a Price attribute is suggest if elasticities are to be calculat or should revenue/profit optimization be conduct. To measure a brand’s impact upon the purchase decision or to conduct competitive what-if scenarios, a Brand attribute should be includ in the design as well.
Once the researcher has identifi the attributes
They ne to turn their attention to defining the levels. There are two or more levels for each attribute. Levels represent the potential how to evaluate the effectiveness of contextual advertising values or enumerations for an attribute. In the above example, the Coverage attribute has three levels – local, regional and national. While levels for any given attribute may overlap, they must be in the same category.
A common challenge in developing a conjoint design is managing its size and complexity. A large design results in many potential products, increasing the number of offerings, or cards,a respondent must evaluate.
While the above example represents 4*5*3*3*2*5=1,800 unique cell phone offerings, conjoint analysis software ruces the number of cards a respondent must evaluate to a manageable level. To determine the minimum number of cards a design requires, subtract the number of attributes in the design from the sum of all levels and then add one.
The above example requires
A minimum of (4+5+3+3+2+5)-6+1=17 cards. (Note: It may often be desirable to employ more than the minimum number of cards to heighten the robustness of the design.The use of an Ideal-Point or Vector attribute may further ruce the number of requir cards.) To minimize respondent fatigue and drive research quality, it is often recommend that a survey not contain more than 25 products or cards.
When developing the design
A researcher should keep an eye school email list toward the challenges the conjoint model will be us to address. As such, it may be desirable to exclude or replace one or more of the cards generat by the conjoint analysis software. This should only occur when a card representing a completely unviable offering is generat.
Despite the temptation to eliminate all marginally marketable products, be aware it can be advantageous to have both highly desirable and undesirable offerings for survey participants to evaluate. Retaining these products tends to maintain the distributions of levels while increasing the quality of the conjoint design, accuracy of importance and utility calculations, and robustness of simulations.
Additionally, most conjoint analysis solutions provide the ability to select the typeof attribute – either Part-Worth, Ideal Point, or Vector – as indicat in Table 2.
Defining a continuous attribute as an
Ideal Point or Vector provides substantially greater flexibility when conducting what-if simulations – enabling levels not originally includ in the design to be us in subsequent simulations. For example, if Monthly Fee was defin as Part-Worth, the researcher would be restrict to simulations employing only the four levels specifi in the design.
However, if Monthly Fee was defin as either Ideal Point or Vector, any value between $19.99 and $149.99 could be enter into the simulation – enabling additional business opportunities to be evaluat without the ne to re-field the survey or collect additional data.
Without question
The power of any conjoint initiative resides within the simulation capabilities – driven by the underlying design. Online simulators (Figure 1) provide interactive decision support, enabling the practitioner to explore and understand the impact of consumer preferences beyond many traditional reporting methods.
The conjoint analysis design principles outlin above have been develop over years of conducting conjoint studies across many industries. We will discuss interpretation of results in a subsequent article.