# What is 'Conjoint Analysis' in Marketing Analytics?

## Conjoint Analysis and  Marketing Analytics

Conjoint analysis is an advanced market research analysis method that attempts to understand how people make complex choices. We make choices that require trade-offs every day. Even simple decisions like choosing a laundry detergent to buy or deciding to book a flight for an upcoming trip contains multiple elements that ultimately lead us to our choice.
When we are buying a product we subconsciously select based on the price, features or some other criteria. Taking all these choices in to consideration is the core subject of conjoint analysis.

For example: Let's assume a scenario where a product marketer needs to measure the impact of individual product features on the estimated market share or sales revenue.

In this conjoint analysis example, we'll assume the product is tablets, perhaps a competitor to the Apple iPad and Samsung Galaxy. The organization needs to understand how different customers value Attributes such as Size, Brand, Price, and Battery Length. Armed with this information, they can create their product range to match consumer preferences.
Conjoint Analysis assigns values to these product Attributes and Levels by creating realistic choices and asking people to evaluate them. Math is then used to calculate what the underlying values are.
Conjoint Analysis enables businesses to mathematically analyse consumer or client behaviour and make decisions based on real insights from customer data. This allows them to better cater to consumer needs and develop business strategies that provide a competitive edge. To fulfil customer wishes in a profitable way requires companies to fully understand which aspects of their product and service are most valued by the customer.

### Conjoint design:-

It consists of different steps, they are
1. Determine the type of study
2. Identify the relevant attributes
3. Specify the attributes' levels
4. Design questionnaire

#### 1. Determine the type of study

There are different types of study that can be chosen:
·         Ranking-based conjoint
·         Rating-based conjoint
·         Choice-based conjoint

#### 2. Identify the relevant attributes

Attributes in conjoint analysis should:
·         be relevant for the management,
·         have varying levels in real-life,
·         be expected to influence preferences,
·         be clearly defined and communicable,
·         preferably not exhibit strong correlations (price and brand are an exception).

#### 3. Specify the attributes' levels

Levels of attributes should be:
·         interesting for management,
·         unambiguous,
·         unbiased
·         separated enough,
·         realistic,
·         such that no attribute can a priori be expected to be a clear winner.

#### 4. Design questionnaire

As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data are available for statistical analysis, resulting in a carefully controlled set of "profiles" for the respondent to consider.

• Estimates psychological tradeoffs that consumers make when evaluating several attributes together
• Measures preferences at the individual level
• Uncovers real or hidden drivers which may not be apparent to the respondent themselves
• Realistic choice or shopping task
• Able to use physical objects
• If appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation

• Designing conjoint studies can be complex
• With too many options, respondents resort to simplification strategies
• Difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
• Respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to
• Poorly designed studies may over-value emotional/preference variables and undervalue concrete variables
• Does not take into account the number items per purchase so it can give a poor reading of market share