What Is Conjoint Analysis?

Conjoint Analysis Tutorial Overview

Welcome to the conjoint analysis tutorial, which provides an interactive tool to teach you about conjoint analysis. Successful completion of the material will prepare you for designing, collecting, and using conjoint analysis customer data in practice. This tutorial is part of a comprehensive toolkit on conjoint analysis that includes two additional components:

• A set of exercises to practice using conjoint analysis data, with the aid of a market choice predictor to make business decisions ('Marketing Simulation: Using Conjoint Analysis for Business Decisions', HBP Courseware #515-713).
• A Do-It-Yourself (DIY) guide that provides a step-by-step explanation for how to construct, run and analyze a conjoint analysis study. The DIY guide also provides access to a customizable choice predictor, allowing you to use your own conjoint data for evaluating business decisions ('Conjoint Analysis: A Do-it-Yourself Guide', HBP Courseware: #515024)

Introduction

What  Who  When  and  Why

In this module you will learn the basics of conjoint analysis, specifically: what conjoint anlaysis is, who tends to use it, and when and why it is applied.

What is Conjoint Analysis?

Most decisions we make in the real world involve trade-offs. For instance, consumers make trade-offs when purchasing products: Should the premium car with all-wheel drive be purchased or should the economy car with better gas mileage be purchased? Companies must consider these consumer trade-offs when developing new products and services, allowing them to make better strategic decisions. For example: what features should be included in the product and which features left out in order to maximize profits? How should a new service with more advanced capabilities be priced to grow market share? To help managers understand how customers make trade-offs among various product or service characteristics, a method of market research called conjoint analysis has been developed.

Conjoint analysis allows researchers to quantify consumer preferences for various features and characteristics of products or services. It essentially builds a mathematical model of consumer preferences, thus allowing managers to predict how consumers would choose between products and services that are or may become available on the market.

Conjoint analysis is used in a range of industries and for a number of strategic decisions such as: product design, product line optimization, pricing, segmentation, and market-share prediction.

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Who Uses Conjoint and When?

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Questions for which conjoint analysis is relevant to address include:

• Which features should we include in our products/services?
• How many different products/services should we offer?
• How much are consumers willing to pay for each feature of our product/service? How much would they be willing to pay extra for an improvement on an existing characteristic?
• How should we price our products/services?
• What market share should we expect to obtain if we launch product/service X?
• How do consumers differ in their preferences? Which segment(s) of consumers should we focus on serving? What product(s) would appeal to this (these) segment(s) relative to existing alternatives in the marketplace?

Why Use Conjoint?

One approach for collecting information on consumer preferences is to conduct interviews, focus groups, or other qualitative techniques. However, these approaches do not lend themselves to quantifying preferences and predicting how consumers would react to specific changes in product design and/or pricing; particularly when there are multiple competitors and alternatives to choose from in the marketplace.

Another approach is to directly ask consumers to specify their preferences for various features and characteristics of a product or service. However, consumers are usually unable to quantify how they would trade off one feature for another, and when asked, many state that "everything is important." Consequently, these approaches are fraught with problems and difficult to rely on for making business decisions.