What is a Conjoint Analysis?

What is Conjoint Analysis?

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences. It uses that information to help select product features, assess sensitivity to price, forecast marketing shares, and predict adoption of new products or services.

Conjoint analysis is frequently used across different marketing industries for all types of products. Such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel.

Businesses of all sizes can benefit from conjoint analysis. Including even local grocery stores and restaurants — and its scope is not just limited to profit motives, for example, charities can use conjoint analysis’ techniques to find out donor preferences.

One of the primary purposes for using conjoint analysis is for gaining strategic insights. And in making better business decisions relating to product pricing, product feature development, branding and package design, marketing messaging validation, and more.

Conjoint surveys don’t let respondents say that everything is important. It systematically varies product attribute levels to create competing, realistic product profiles and then records what people choose.

Choose the right survey type

The first stage is to decide on the correct survey type. There are several ways to do a conjoint analysis — here are the main methods.

• Ratings-based conjoint analysis. This is where participants give each attribute a rating, for example on a scale of 1-100.

• Ranking-based conjoint analysis. This is where participants rank the attributes in order from best to worst. There is also best vs worst analysis, where participants simply pick their favourite and least favourite attributes out of the selection.

• Choice-based conjoint analysis (CBC). This is the most commonly used model and the one this guide will focus on. It presents combinations of attributes to participants and asks them to choose which they prefer.

Design the questionnaire

Screener questions

Most Surveys start with some screener questions. These are general questions around demographics like the respondent’s age, job title, or purchase habits. The goal is to filter out those who won’t be a good fit for the survey based on the people you’re trying to target.

Here are some guidelines to follow:

• Questions should follow on from one another logically and grouped together intuitively. It’s best not to confuse your participants by ordering your questions in a confusing way.

• People often give more accurate and useful answers when you use situational questions g. For example, instead of asking, “Which phone would you buy”, ask something like, “Thinking back to the last time you purchased a phone — if you had the following options instead, which would you have picked?”

• Finish with some demographic questions so that you can further understand your customer base and analyse the results by demographic to understand any meaningful differences.

How does conjoint analysis work?

Step 1: Break products into attributes and levels

To run a conjoint study, you break up the product or service you intend to research into its components, called attributes and levels. In simple terms, the conjoint survey design algorithm generates a balanced survey design with product profiles. Also known as concepts, formulated to have ideal statistical properties (such as level balance and independence of attributes).

Each profile made up of multiple conjoined product features (hence, conjoint analysis), such as brand, type, engine, and color, each with systematically varied levels.

These product concepts then included in a series of questions, usually 8-20, called choice tasks, that make up the conjoint analysis portion of the survey.

Step 2: Survey respondents choose their preferred concept in each choice task

At this point, we field the conjoint experiment design and invite respondents to complete the survey. Rather than ask respondents what they subjectively prefer in a product. Or which attributes and levels are most important (as when using traditional rating scales and standard survey questions), conjoint analysis employs the more realistic context of asking respondents to choose digital products from available options.

Step 3: The survey analyst builds a model that quantifies market preferences

The final step in the process is where we get to make discoveries and realize insights from the data that we collected. The conjoint software includes a statistical model that considers the available product options and importantly which alternatives the respondents chose. It statistically deduces which marketing product features are most desired and which attributes have the most impact on customer choice.