Implementing the Conjoint Technique in Quick Turn Projects
Conjoint is a robust technique often leveraged to create complex models for a wide range of business questions. With the ability to model products at a granular level, in-depth research into product and shelf set optimization can be conducted. However, this comes with trade-offs such as requiring larger sample sizes, longer exercises, and long timelines for design and analysis. Due to this, conjoint is often overlooked for quick turn projects.
While it can create complex and in-depth models, conjoint is a tool perfectly capable of working within an agile framework. By focusing on the key research objectives, the design of a conjoint can be scaled down to be more tactical in nature. One common example is a shelf set test where holistic concepts representing a product are created and rotated with different price levels. By doing so, you end up with a simple design that offers the key advantages of conjoint compared to more traditional pricing techniques. These advantages include the ability to examine the test product within the competitive shelf set.
Implementing Choice Based Conjoints (CBC) for Complex Configurations
Conjoint is a powerful marketing research technique that is often employed to tackle large product development questions. The technique is often used to create product and optimization decisions through a combination of attributes (e.g. color) and levels (e.g. red, green, or blue) for a given product or line of products. Choice Based Conjoint (CBC) is a flexible technique that allows for nuanced complexities by leveraging advanced methods and therefore, complex product and line configurations to be recreated in the analysis.
As with most products and configuration objectives, there are many nuances to account for in this approach. Techniques like alternative specific designs, which make the appearance of certain attributes conditional on the levels of another are key to a successful conjoint design and efficient survey experience for the respondent. While this ability to recreate products with such complexity allows for incredibly robust and deep analysis in product development, there are some trade-offs to doing so. The complexity often leads to an increase in time required to run the study, length of the exercise for respondents, and cost of the study overall because of robust sample needs.
Keeping Conjoints Simple and Efficient
Having the ability to dive into all the nuance and specifications of a product often leave researchers with a mindset that conjoint is a technique that is reserved for bigger projects with bigger budgets and timelines. While conjoints of that nature are certainly a key tool of a marketing researcher’s toolbox, the truth is that conjoint is a technique perfectly capable of being agile and tactical in nature. While you can add loads of complexity to a conjoint, you most certainly do not have to. In fact, there are many business questions that can be solved with a conjoint that is relatively simple, efficient, and designed for smaller budgets and quick timelines.
As with any agile research, focusing in on the business question being asked is the key. Designing and executing conjoint in a focused manner is not only desirable, but critical when the project has a limited budget and expedited timing. Traditionally, clients have been exposed to large and cumbersome designs that take weeks to finalize and caused lengthy timelines with large project costs that cause CBC to be overlooked.
One of the most common areas that cause a conjoint to balloon into a complex project is the belief that every single aspect of a product must be modeled individually within the framework exercise. However, when the business question at hand is not concerned with several of the product attributes, it is often possible to reduce the complexity through techniques like nesting attributes.
Nesting Attributes to Reduce Complexity in Conjoints
Nesting attributes of products into a more holistic attribute allows for the design to save on complexity by not worrying about the different combinations of the nested attributes and instead restricts them to the key scenarios that would exist in the marketplace. Say you are working on developing a new professional training course to teach leadership and communication skills to business professionals. A lot of effort can be spent thinking up the combinations of the numbers of time per week, hours per class, how many weeks, etc. that go into the course offering. The combination of these attributes adds further complexity and then some combinations may be illogical for the course, resulting in dramatically different overall course lengths. These course lengths must then be handled through techniques like prohibitions or alternative specific design. When this happens, the complexity of the design has already started to grow, and these attributes might not even be of primary interest to overall business question. If the business objective is around the optimization of the course for online/in-person learning, content covered in the class, and price, then these attributes related to class length might add unneeded complexity. Instead, focusing solely on realistic scenarios that comprise hours per class, classes per week and weeks per course into one “course length” attribute will allow the key scenarios to be covered while greatly reducing the complexity of the conjoint. This provides advantages like ensuring the respondents are evaluating relevant alternatives, reducing the model’s “effort” on lesser important aspects (to the business question at hand), and reducing the exercise length for respondents.
Using Shelf Set Style to Focus on Price Sensitivity
By being judicial in determining what are the key aspects to expand on and which aspects can be simplified or even removed entirely, the conjoint can be designed, executed, and analyzed quickly. Another prime example of how to leverage conjoint in a tactical manner is through shelf set style conjoints, which focus on price sensitivity – a useful technique when optimizing the product features is not important. For example, if the concept is almost ready for market, this type of conjoint narrows in specifically on optimizing pricing.
In a shelf set style conjoint, you are reducing the design to essentially two attributes: product and price. Aside from price, every other attribute that would traditionally be modeled in the conjoint is combined into holistic concepts based on the test product and the current shelf. By sacrificing the ability to optimize the product configuration and essentially isolate novel combinations of product and price, you can create a conjoint that is very simple, very robust, and offers a key advantage over traditional pricing techniques.
Most traditional pricing techniques like price laddering or Van Westendrop are great for examining the price sensitivity of a product in a vacuum, but leave a gap in the discussion. What is the price sensitivity of the product when it is on the shelf with competitors? By leveraging a simple shelf set style conjoint, we can answer this question much more easily than various traditional pricing techniques. Since the competitive set is included as concepts within the product attribute of the conjoint, we can simulate the addition of the test product to the shelf, and see how pricing affects it. We can even take a step further and start to gauge what happens if these competitors start to adjust their own prices based on the introduction of the test product!
Being Creative with the Conjoint Technique in Market Research
As is often the case in marketing research, there are trades offs to everything. In the above example, the shelf set test is only able to simulate products dictated in the design of the conjoint. The modeling will not be able to examine what happens if a competitor launches a new product in the category that copies your innovation. Determining how complex and flexible you want your analysis to be is a key consideration when conducting product development and must be weighed with the cost/timing of the research. Regardless of whether you want to quickly tackle a key question or dive deep into the nuance in designing a product, conjoint is a robust technique that can be leveraged in many different ways to gain insights around product development. Additionally, conjoint can be designed as an agile solution that accounts for limited budgets and speedy timelines.
Written by Tyler Dugan, Senior Data Analyst at Big Village Insights.