Alternative-Specific Conjoint is one of the variations of the Choice-Based Conjoint (CBC) analysis widely utilized and recommended by Big Village.

Alternative-specific approach makes CBC designs more flexible, provides more realistic description of products or offerings in choice tasks, improves accuracy and quality of modeling in many complex studies.

Choice-Based Conjoint (CBC) analysis or Discrete Choice Modeling (DCM) is one or the most widely used and trusted tools for product development and price optimization in modern survey-based research. In a discrete choice experiment, each respondent is presented with a series of tasks or screens. Each task is a set of products or concepts, and a respondent is asked to choose between these options in each task. CBC tasks are generated using principles of experimental design to make sure maximum amount of information is elicited from respondents for the most accurate modeling.
One of the obvious advantages of CBC is that tasks in a discrete choice experiment are mimicking a natural behavior of consumers in a marketplace. A respondent is considering different products in a competitive context, evaluating the products’ features and prices, and making reasonable trade-offs. The data collected in a discrete choice exercise can be utilized in a Hierarchical Bayesian estimation to model preferences individually for each respondent and accurately describe the market heterogeneity. Using CBC, researchers can simulate consumers behavior in hypothetical scenarios, take into accounts interactions between different product attributes, simulate various scenarios on the market, optimize product features, and understand price sensitivity.

To generate a choice task for a CBC, a researcher is defining a set of attributes describing products or concepts they want to evaluate.

For example, if the researcher is studying laptops, the attributes could be the following: brand, type, screen size, processor, memory, battery life, price, etc. Each attribute is presented on different levels, for example, for a laptop brand, it could be Apple, HP, Dell, Lenovo, etc. With a standard CBC design, a single level of each attribute must appear in every product alternative presented in a choice task.

But sometimes, especially in complex tech or durables categories, products can have their unique sets of attributes. Let’s consider headphones or earbuds. These products have common attributes – brand, shape, color, price, etc. Levels of these attributes would be presented in every alternative in a CBC. Also, earbuds can be wired or wireless, and there will be attributes/features that are presented only for wireless or wired earbuds. For example, for wireless earbuds, these attributes could be charging time and battery life, and for wired earbuds it could be in-line volume control, plug type, etc. To model a category like headphone and earbuds using a CBC, researchers are utilizing alternative-specific designs. With this kind of design, every primary attribute (such as wireless or wired earbuds in the example above) will only be displayed with a relevant subset of conditional attributes (such as charging time and battery life for wireless earbuds). This would allow presenting only product alternatives that make sense for respondents in this category. Respondents would be able to make meaningful tradeoffs and choose between products with different sets of attributes. Based on the data collected in a choice experiment with alternative-specific design, an accurate model can be built in a category like headphones and earbuds, and it can be used for product development.

Choosing the right price for a product is crucial to its success, and alternative-specific conjoint could be the optimal methodology for pricing in many cases.

It has the same main advantages as a standard CBC. Respondents are reacting to realistic alternatives, price sensitivity is evaluated in in presence of competition, preferences for new products can be accurately estimated in various scenarios. In addition to that, alternative-specific conjoint allows analyzing price sensitivity in categories with a very large variation of prices. There are many tech and durables categories where the price can vary dramatically depending on the brand and other primary features/attributes of the products. Even relatively simple technology like a cell phone power bank or a portable charger can be priced anywhere between $10 and $200 depending on its characteristics. Alternative-specific conjoint allows evaluating products only in a relevant price interval, so respondents are reacting to realistic prices in the category and making more thoughtful choice. In addition, modeling and studying alternative-specific effects in pricing allows better understanding of willingness to pay for common primary and conditional attributes. Also, with alternative-specific designs, the whole wide price interval in a category does not have to be presented with reference price points in the CBC designs. It would be enough to only test reference points around the most typical price for each configuration or primary attribute. Therefore, alternative-specific designs help to minimize the number of parameters/utilities to be estimated in a CBC. More realistic and consistent choices and smaller number of price utilities to estimate make alternative-specific conjoint an attractive option for pricing of complex products and offerings in categories like tech, durables, services, travel and tourism, etc.

Overall, alternative-specific approach makes CBC designs more flexible and “compact” in studies involving complex products or offerings.

It provides more realistic description of alternatives and allow respondents to choose from the most relevant options, which improves quality of data. Classifying and presenting attributes as common, primary, and conditional better informs the estimation and ensures higher accuracy of modeling in CBC studies.

Utilizing advantages of alternative-specific conjoint, Big Village extensively uses this approach in a wide range of product development and pricing studies. This methodology is especially attractive for developing, pricing, and testing innovations in categories like tech, durables, financial services, telecoms, and many others. Contact us to learn more.

Written by Faina Shmulyian, VP Insights at Big Village