Conjoint Analysis: Definition, Types, Benefits, Examples

Conjoint Analysis is a sophisticated statistical technique used in market research to decipher how consumers value different attributes of a product or service. By presenting respondents with a series of choices that include various combinations of product features, Conjoint Analysis helps identify the most influential attributes that drive consumer preferences and purchasing decisions. This method can be considered a more specialised form of Key Drivers Analysis, which identifies the most impactful factors influencing outcomes, and Latent Class Analysis, which uncovers hidden subgroups within a dataset.

This method breaks down consumer preferences by asking them to evaluate different product configurations or product features. For example, consumers might be presented with various combinations of features for a new smartphone, such as battery life, camera quality, and price. By analysing these choices, businesses can uncover the relative importance of each attribute and understand which combinations are most appealing to different consumer groups. This process of tailoring the model to different groups is akin to the segmentation techniques used in Segmentation Analysis, where consumer groups are categorised based on shared characteristics.

Conjoint Analysis is invaluable in product development and pricing strategy. It provides actionable insights into which features should be prioritised and how much consumers are willing to pay for them. This ensures that new products are designed to meet consumer needs and are priced appropriately to maximise market acceptance and profitability. For instance, if the analysis reveals that customers place a high value on battery life over camera quality, a smartphone manufacturer might focus on enhancing battery performance in their next model.

In real-world applications, Conjoint Analysis has been used by companies across various industries to optimise their offerings. For example, in the automotive industry, manufacturers use it to balance features like fuel efficiency, safety, and design. In the tech industry, companies leverage it to fine-tune features and pricing of hardware and software. This method helps businesses stay competitive by aligning their products more closely with consumer desires.

History of conjoint analysis

Conjoint Analysis has a rich history that traces back to the early 1970s when it was introduced as a method to better understand consumer preferences and decision-making processes. The technique was developed by marketing researchers Paul Green and Vithala Rao, who sought to create a more sophisticated approach to market research than traditional survey methods.

Initially, Conjoint Analysis was used primarily in academic settings to study consumer behaviour and preferences. However, its practical applications quickly became apparent, and it soon gained traction in various industries. The early methods involved presenting respondents with a set of hypothetical products and asking them to rank or rate their preferences. These responses were then analysed to determine the relative importance of different attributes.

Over the years, advancements in computer technology and statistical methods have significantly enhanced the complexity and capabilities of Conjoint Analysis. Modern techniques allow for more detailed and accurate analyses, including the ability to handle larger datasets and more attributes. Software advancements have also made it easier to design and administer Conjoint Analysis surveys, broadening its accessibility and application. Techniques from Survey Weighting are often incorporated into Conjoint Analysis to correct biases in the data and ensure accuracy. These techniques also facilitate specialised conjoint methods, such as the constant sum patient allocation exercise, where choice exercises are framed in terms of, for example, the percentage of patients prescribed a treatment.

The evolution of Conjoint Analysis has led to the development of various methodologies, including Discrete Choice-Based Conjoint (CBC), MaxDiff, and Best-Worst Scaling, each offering unique advantages for specific research needs. These advancements have solidified Conjoint Analysis as a vital tool in marketing research, product development, and pricing strategy, helping businesses make data-driven decisions that align with consumer preferences.

Although Conjoint Analysis is the popular term for this family of techniques it is also referred to in academic circles and literature as Discrete Choice Modelling. 

Types of Conjoint Analysis

At The Stats People, we specialise in several types of Conjoint Analysis, each designed to address specific research needs and provide actionable insights. Here’s an overview of our preferred methodologies and a critique of some common alternatives.

Choice Based Conjoint Analysis (CBCA)

Choice-Based Conjoint Analysis (CBCA) which we sometimes refer to in the Stats People as full Discrete Choice Modelling (DCM)  is a widely used method that presents respondents with a series of choice tasks. Each task includes a set of fully described product profiles, and respondents are asked to choose their preferred option. This approach closely mimics real-world purchasing decisions, providing robust data on consumer preferences and trade-offs. Choice-Based Conjoint Analysis is particularly effective in scenarios where predicting market or preference shares for the full product is more important than understanding the relative hierarchy of which attributes are driving the choice. The method’s realism and flexibility make it ideal for diverse applications, from product design to pricing strategy.

MaxDiff (Maximum Difference Scaling)

MaxDiff, or Maximum Difference Scaling, is a simple but powerful trade-off technique. It asks respondents to identify the most and least important alternatives from sets of simple options. These options can be product benefits, standalone 

Single-level features (sometimes referred to as simple attributes), or any other set of stimulus options we want to trade off. This scale free method is highly effective for determining the relative importance of each option. MaxDiff is particularly useful in product development and feature prioritisation.

Best-Worst Case 2

Best-Worst Case 2 (BWC2) is a half-way house between a full-DCM Choice Based Conjoint Analysis and a MaxDiff. Just like in a simplest trade-off model, respondents are presented with multiple sets of features and are asked to choose the best and worst in each set, however, unlike MaxDiff, BWC2 allows a full attribute grid, just like CBC would, with attributes and levels. We usually deploy BWC2 when the main goal is to understand what attributes and attribute levels are  driving the choice, rather than to estimate product share. Unlike a CBC it allows the attributes’ utilities for all attributes and levels to be estimated and compared on a single scale. This method provides a deeper understanding of consumer decision-making processes and helps refine product features to align more closely with market demands.

Critique of Other Approaches

While other methods like Adaptive Choice-Based Conjoint (ACBC), Menu-Based Conjoint (MBC), and Adaptive Conjoint Analysis (ACA) are popular, we prefer our tailored methodologies. ACBC and ACA, for instance, adapt the survey based on previous responses, which can introduce complexity and respondent fatigue and requires the use of specialist survey hosting software which isn’t a requirement for CBC, MaxDiff and BWC2. 

MBC allows respondents to select combinations of features from a menu, which can be useful but often complicates the analysis due to the vast number of possible combinations. We find that our chosen methods provide clearer, more actionable insights without the added complexity.

Benefits of Conjoint Analysis

Conjoint Analysis offers several key benefits that can significantly enhance business strategy, product development, and market positioning. Here’s how:

1. Understanding Consumer Preferences

Conjoint Analysis provides deep insights into consumer preferences by breaking down how customers value different product attributes. This understanding helps businesses tailor their offerings to better meet customer needs and desires. For example, a company can determine whether specific groups of consumers prioritise price, performance, or specific features, enabling them to design products that resonate more effectively with that group. This insight is also crucial in Segmentation Analysis, as understanding consumer preferences helps in categorising and targeting specific market segments.

2. Competitive Analysis

By revealing what attributes consumers value most, Conjoint Analysis helps businesses understand their competitive position. Companies can compare their offerings against competitors’ and identify areas where they have a competitive edge or need improvement. This insight allows for strategic adjustments that can enhance market share and overall competitiveness.

3. Optimising Product Value and Pricing Strategy

Conjoint Analysis is instrumental in determining the optimal pricing strategy for a product. By understanding the trade-offs consumers are willing to make, businesses can set prices that reflect the perceived value of different features. This leads to better pricing decisions that maximise revenue without sacrificing customer satisfaction.

4. Market Research Insights

The detailed data obtained from Conjoint Analysis in marketing research can inform broader market research efforts. It helps in segmenting the market based on preferences, allowing for more targeted marketing strategies. These insights can guide advertising campaigns, product launches, and other marketing activities to ensure they are aligned with consumer expectations and behaviours.

5. Product Development and Innovation

Insights from Conjoint Analysis guide product development by highlighting which features are most important to consumers. This allows businesses to focus their innovation efforts on areas that will deliver the most value. By prioritising features that consumers care about, companies can create products that are more likely to succeed in the market.

6. Improved Decision-Making

Conjoint Analysis supports data-driven decision-making by providing clear, quantifiable insights into consumer preferences. This helps businesses make informed choices about product features, pricing, and market positioning, reducing the risk of costly mistakes and increasing the likelihood of success.

7. Enhanced Customer Satisfaction

By aligning products and services with consumer preferences, businesses can significantly enhance customer satisfaction. When customers feel that a product meets their needs and preferences, they are more likely to be satisfied and loyal, leading to repeat business and positive word-of-mouth.

Case Studies (Examples of successful Conjoint Analysis studies)

Conjoint Analysis has been effectively employed by The Stats People to help various clients enhance their product development and pricing strategies. Here are two notable case studies that illustrate its successful application and provide a conjoint analysis example:

1. Choice Modelling for Medical Research Agency

The Stats People were engaged by a leading medical research agency to perform a complex Choice Modelling study on trade-offs between branded and biosimilar ophthalmology products across multiple countries. This study required customised approaches for each market, incorporating various attributes like discounts and existing prescribing practices.

Project Execution:

The project began with detailed planning to define the requirements for each hypothetical scenario and product. The Stats People then developed a customised simulator integrating all parameters and included a dashboard interface. This required extensive data handling and iterative adjustments to meet client specifications.

Challenges:

The key challenge was adapting to ongoing client requests that did not fit the conventional Choice Modelling framework. The team had to find practical solutions to incorporate these new requirements into the existing model.

Outcome:

The final deliverable was a highly customised Excel-based simulator with a dashboard add-in, successfully meeting all the client’s objectives and providing a powerful tool for analysing market trade-offs.

2. Best-Worst Case 2 (BWC2) for Remuneration Benefits Analysis

The Stats People were enlisted by a company to analyse employee preferences for various remuneration and benefits packages using the Best-Worst Case 2 (BWC2) method. This approach was chosen to understand which elements of compensation were most valued by employees and how the company could optimise its offerings to enhance satisfaction and retention.

Project Execution:

The project started with identifying key attributes of the remuneration and benefits packages, such as salary increments, bonuses, health insurance, and flexible working hours. The BWC2 method was then employed to present these attributes in sets, asking employees to select the most and least preferred options in each set. This approach allowed The Stats People to gather detailed insights into the relative importance of each benefit component.

Challenges:

A significant challenge was ensuring that the BWC2 model accurately reflected the diverse preferences of the employee base. The team had to manage and interpret a large volume of complex data, requiring careful calibration of the model to ensure that it provided clear and actionable insights.

Outcome:

The analysis resulted in a highly detailed understanding of employee preferences. Flexible working hours and health insurance emerged as top priorities, while bonuses and salary increments were less critical. These findings enabled the company to restructure its benefits package to align more closely with employee values, leading to increased job satisfaction and reduced turnover.

These case studies highlight how conjoint analysis can provide valuable insights that drive strategic decisions, leading to enhanced customer satisfaction, better product development, and optimised conjoint analysis pricing strategies.