How do you know your new product concept has the potential to succeed? To avoid a new product flop, you need to understand market fit, customer sentiment, and what buyers value before you go to market. Conducting a conjoint analysis is a way to gather this info, so you can strategically launch a new product with the most valuable features, at the right price point.
What is conjoint analysis?
Conjoint analysis is a statistical market research tactic that seeks to understand how customers make buying decisions about products or services. Using surveys and statistical analysis, market researchers aim to learn how consumers value different components or features and what they’re willing to pay for them. The data gathered using the conjoint analysis methodology answers four key questions:
- What does the target audience want?
- What is their thought process when buying products like ours?
- What informs buying decisions?
- What trade-offs are buyers willing to make regarding features and pricing?
The conjoint analysis technique classifies products or services based on individual attributes, each of which can affect a consumer’s perceived overall value of an offering. It then seeks to understand the relative importance consumers place on each feature to determine the best product bundle and the right pricing strategy.
When to use a conjoint analysis
Conjoint analysis requires market researchers to ask customers to compare different features, product bundles, and price points, and to share the value they place on each. Researchers analyze the survey responses to learn which features are viewed most positively or negatively. This market research technique is essential for companies at three distinct phases of go-to-market:
Developing a pricing strategy for a completed project
These valuable insights help you determine how much consumers are willing to pay for your product, what product attributes affect the amount.
Developing a sales and marketing strategy
Conjoint analysis provides a goldmine of information about what features consumers value most. Conjoint analysis data is crucial information for your sales and marketing efforts, helping you craft advertisements, marketing copy, and promotions to specifically pitch high-value features.
Conducting research and development
Insights from a conjoint analysis can directly inform your research and development pipeline, helping you prioritize new features and upgrades to your existing process to meet market demand. The results of conjoint analysis can help narrow the scope of your product development so you prioritize what is most valuable to consumers.
How to conduct a conjoint analysis
You can conduct a conjoint analysis internally with a team of market researchers or data scientists, potentially aided by software like Conjointly or SurveyMonkey. Or, you can outsource to a third-party consultant. The basic process for conducting a conjoint analysis is as follows:
1. Define choice sets
Before the survey phase begins, market researchers break a product or service down into its individual components. For example, consider a company looking to understand the value consumers place on the following features in a Bluetooth speaker:
- Microphone for voice commands
When planning a conjoint analysis experiment, the company would create multiple feature bundles—called choice sets—with various combinations of those features at differing price points.
For example, the highest-priced choice set might include a large, cylindrical speaker with a microphone and waterproof body for $200. Another feature set might offer the same, minus the microphone, for $150.
Adding and subtracting specific features from each choice set—and adjusting the price accordingly—will provide a cross section of feature options consumers can weigh and select.
2. Conduct survey
Researchers then send a conjoint analysis survey to target respondents and ask them to rank their interest and willingness to pay for each bundle of features. The format for those questions is a critical factor and depends on the type of conjoint study, and researchers will choose either choice-based conjoint analysis or adaptive conjoint analysis—more on this later.
3. Analyze data
Once researchers receive the conjoint data, statistical and mathematical analysis can begin. With these conjoint analysis results, it’s possible to calculate a numerical value to measure how individual product features influenced the respondents’ choices. This lets researchers assign a preference score, indicating how much value consumers place on each feature and what the optimal price is for each feature set.
Types of conjoint analysis
There are two main types of conjoint analysis market researcher experts can use:
Here’s an overview of both conjoint analysis methods:
Choice-based conjoint (CBC) analysis
Choice-based conjoint analysis—also known as discrete choice conjoint analysis—is the most popular form of conjoint experiment. The questions in these surveys mimic the real-world reasoning and trade-offs buyers go through when selecting a product. A CBC survey prompts respondents to decide, based on a set of features and prices, which product they would buy.
Questions in a CBC survey typically include prompts like “would you rather” or “which would you choose.” The survey respondents are then presented with a handful of product combinations and price points and asked to select the one that resonates most.
Alternatively, researchers could present the respondent with a series of side-by-side comparison questions and ask them to choose between two feature bundles.
Looking back at the conjoint analysis example for a Bluetooth speaker, the product bundles, or choice sets, would be:
- Bundle A: Cylindrical shape, no microphone, not waterproof, $100
- Bundle B: Cylindrical shape, no microphone, waterproof, $150
- Bundle C: Cylindrical shape, microphone, not waterproof, $150
- Bundle D: Cylindrical shape, microphone, waterproof, $200
Researchers could then ask respondents to select the most appealing bundle. Or they could pose a series of questions asking respondents to select between Bundle A or Bundle B, Bundle B or Bundle C, and so on.
The survey data collected from these questions helps researchers estimate the tradeoffs buyers are willing to make between actual features and what they are willing to pay. The CBC method’s key benefit is simplicity.
Adaptive conjoint analysis (ACA)
Adaptive conjoint analysis (ACA) is a conjoint model to evaluate numerous attributes simultaneously when there are too many product features and choice sets to easily conduct a choice-based survey. For example, a complex computer system will likely have dozens of unique features. The number of feature set combinations is too high for a choice-based approach, so ACA serves as an alternative.
There are two distinct features of an adaptive conjoint analysis. First, each respondent receives a unique set of questions depending on how the respondent answers qualifying questions at the start of the conjoint survey. This helps researchers attribute importance to specific features.
Second, ACA surveys use a Likert scale, asking respondents to rank feature sets from “most likely” to purchase to “least likely” to purchase. Respondents show their preference for each specific feature by showing how likely or unlikely they are to buy each product variant. The answers are then processed and weighted to determine which features have the highest demand.
Let’s go back to the Bluetooth speaker example. This time, additional attributes increase the number of feature sets available.
- Device size and shape: puck shape (4″ width, 3″ height), cylindrical shape (3″ width, 6″ height)
- Microphone option: yes, no
- Waterproof: yes, no
- Color: black, chrome
- Speakerphone option: yes, no
Here’s an example of the feature sets you could create from this combination of features. This list isn’t comprehensive and you can expand it significantly to include every conceivable combination of product features. As you add more features, the number of potential product variations increases exponentially. This high number of product variants makes it difficult to survey respondents using the CBC method.
Using the ACA method, respondents are presented with a few bundles and asked to select their favorite. They are presented more choices, seeing options based on the priorities indicated by their previous choices. For example, if a respondent indicates a preference for a chrome speaker, the computer will focus on comparing variants of the chrome speaker going forward. This minimizes the likelihood of decision fatigue.
With the results of your survey, you could then determine how much of the target market values the device’s size, microphone options, color, speakerphone options, or waterproof technology and adapt product development and marketing materials accordingly.
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Conjoint analysis FAQ
How can conjoint analysis benefit my business?
Conjoint analysis can help you understand what features your target market values most and what they are willing to pay for them. With these market researcher’s insights, you can focus your R&D and product management on the most in-demand features, develop tailored sales and marketing messaging, understand brand price trade-off, and set a pricing structure aligned with your customers’ values and priorities.
What’s the difference between choice-based conjoint (CBC) and adaptive conjoint analysis (ACA)?
Choice-based conjoint (CBC) and adaptive conjoint analysis (ACA) both help businesses understand consumer preference around features and pricing. They differ in how they present product bundles and in the survey methodology. CBC asks users to select a favorite combination of product features. ACA uses an adaptive approach to show which features respondents value most.
When should I use conjoint analysis?
You should use conjoint analysis before introducing a new product, when creating a new pricing strategy, doing sales and marketing research for new campaigns, conducting product positioning research, or establishing your R&D priorities or future product roadmap. Conjoint analysis is a powerful tool for product and pricing research, offering quantifiable feedback from in-market consumers.