Conjoint analysis

Conjoint analysis allows you to determine the ideal combination of a limited number of attributes. A good conjoint analysis solution will help you answer "What if" questions about which attributes consumers consider the most influential.

Alida's conjoint analysis solution uses established mathematical and statistical methodologies to deliver data you can rely on. At the same time, the intuitive interface allows everyone, from less experienced survey authors to veteran market researchers, to focus on their project without having to worry about the intricacies of experimental design and analysis.

As a market research technique, conjoint analysis can help you:

• Identify consumer preferences.
• Analyze the trade-offs consumers would make.
• Understand the perceived importance and preference for each feature within the product or service.
• Determine the ideal combination of attributes for a new product or service.

Use cases for conjoint analysis include:

• Enhancing product development and prioritizing features.
• Estimating price sensitivity and the influence of cost on purchasing decisions.
• Gauging which employee benefits to offer.
• Determining software packaging.
• Determining marketing focus.

Choice-Based Conjoint presents participants with several profiles in a choice set. Each profile represents a bundle of product traits (or attributes) such as brand, color, size, price, and so on. Each attribute can have multiple values (or levels). Participants choose the most appealing profile from each set and repeat this task multiple times. Each time they choose a profile, they are making trade-offs about which levels they value over others.

Based on participants' selections, the application calculates the implicit valuation of all the levels. You can use these implicit valuations (or part-worth utilities) to estimate market share, revenue and even profitability of new designs. Part-worth utilities are also useful for determining the optimal bundle of traits that your offering should have.

Choice-Based Conjoint is the most popular conjoint methodology because it comes closest to how consumers approach purchasing decisions in the real world. Therefore, a Choice-Based Conjoint question feels more natural and presents less decision fatigue for participants.

For example, let's say participants are evaluating concepts for new cereals. It's much easier to ask participants to pick one cereal they want out of a set of 4-5 cereals, and to repeat that task over several rounds. This question structure resembles what participants would do if they were shopping in a supermarket: comparing one box of cereal to others in the same aisle. The mental load on the participant is minimized. At the same time, you get a wealth of comparative and inferential data, from which you can extrapolate what participants value most.

Other conjoint methodologies exist and have their advantages, but they also present a greater mental load for participants. What's more, they are either older methods, or methods that present complex experimental design challenges. Some of these other methodologies include:

• Traditional Ranking-Based Conjoint: Participants are asked to rank profiles from best to worst.

  In our cereal example, participants would be asked to rank all the cereal concepts from most preferred to least preferred.

• Self-Explicated Conjoint: Participants are asked to weight the attributes they consider most important in their decision-making.

  In our cereal example, participants might assign weight percentages out of 100% to price, ingredients, brand, and so on to denote their importance.

• Adaptive Conjoint: Participants are first asked to eliminate all levels they consider to be non-starters, then asked which levels they most prefer.

  In our cereal example, participants would evaluate all the levels in the Free-From Ingredients attribute (Sugar-Free, Nut-Free, Gluten-Free, Artificial Preservative-free, Artificial Dye-Free) and eliminate the ones that don't matter to them at all. For the remaining Free-From Ingredients levels, they would indicate which ones they prefer most and least.