For businesses seeking a comprehensive understanding of the consumer decision-making process, exploring real-life conjoint analysis examples are essential.
Why? Well, as consumers, we make choices daily that require difficult tradeoffs between the features, quality, and price of the products and services we buy. Business owners face similar tradeoffs in product design, pricing, and marketing. While traditional market research techniques can be helpful (i.e., ratings, ranking data), they often fail to capture the complexity behind consumer decision-making. That's where conjoint analysis comes in. Conjoint analysis is a proven way to examine tradeoffs between features, quality, and price.
In this article, I'll share some examples that highlight the powerful insights and strategic guidance that can be derived from this methodology, stepping through:
What is Conjoint Analysis?
Conjoint analysis is a technique that originated in mathematical psychology (Luce and Tukey 1964) and was first applied to marketing problems by University of Pennsylvania Wharton School Professor Paul E. Green (1971). This technique helps businesses measure the tradeoffs consumers make when choosing between alternatives. Usually conducted within an online survey, a conjoint experiment breaks down products and services into their key attributes, like price, features, quality, brand, etc. Survey takers are then presented with multiple alternatives of the product or service that combine different levels of these attributes and, in a choice-based conjoint, are asked to choose their preferred option.
By analyzing people's choices, researchers can determine the relative importance of each attribute, assign a value to them if needed, and determine how consumers make tradeoffs between them. Conjoint analysis is particularly helpful in marketing, product management/development, and operations research.
Questions Conjoint Analysis Answers
Conjoint analysis provides uniquely actionable insights by quantifying customers' tradeoffs between product attributes. By forcing survey respondents to choose between product profiles, we can uncover what they truly value. This enables businesses to answer critical questions such as:
How much more will customers pay for additional product features or higher quality?
Conjoint quantifies willingness to pay for additional features, quality, or benefits. You can determine how much more revenue premium offerings will generate or how much market share would be lost if prices increased.
Which attributes are deal breakers versus nice-to-haves?
Not all product attributes are deal breakers. Conjoint exposes which features customers will compromise on versus those they require. This avoids over-engineering products.
How price-sensitive are different customer segments?
Willingness to pay often varies across customer segments. Conjoint can identify price-sensitive segments based on the tradeoffs they make. This will help companies decide whether they need to have a budget offering, a premium offering, or both.
What is the revenue-maximizing combination of price, features, and quality?
A product with all the best features at the lowest price will certainly be preferred - but typically not optimal from a revenue standpoint. With the data from a conjoint model, we can use algorithms to search the entire realm of possible product configurations to find the set of features that attract just enough people at the highest price they are willing to pay to maximize the revenue generated for the business. If costing information is provided, the recommendation could turn into a profit-maximizing combination. Knowing the configuration that will drive the most revenue and/or profit will help companies avoid underpricing (leaving money on the table) or overpricing (potentially losing sales).
Varying product configurations with conjoint identifies the price, feature set, quality level, and other attributes that maximize appeal. And it is only because we are forcing people to make tradeoffs that we can uncover what they truly value. The depth of insights unlocked by conjoint analysis is unmatched by traditional survey techniques.
Conjoint Analysis Example: Daily Tradeoffs Consumers Face
When booking a flight, consumers compare departure times, number of stops, airline loyalty programs, and ticket prices. While the cheapest nonstop on a preferred airline is ideal, it is rare that a person finds the perfect combination and, often times, needs to compromise based on what's most important for that particular trip.
Think back to the last time you bought a new smartphone. There are so many options to choose from. We must consider different brands, screen sizes, camera quality, battery life, storage space, and price. Since we likely can't maximize every feature within our budget, we select the phone with the combination of attributes that we value most.
As a business, you'll face similar dilemmas in product design and pricing:
Do you run the airline company? You'll have to balance ticket prices, flight schedules, aircraft sizes, amenities, and payouts of the loyalty programs. Having too few flight options or too many overpriced flight options could result in lost customers. But too many underpriced seats will hurt your profitability.
Do you work at a smartphone manufacturer? You can't change your brand, but you can build multiple skus, with different features and sizes and specs and material to try and attract more buyers. But too many skus with too few buyers will result in high manufacturing costs which may hurt your bottom line.
Manage a quick-service restaurant? You may want to understand the most desirable menu items, portion sizes, and pricing. Having a lot of menu options requires more ingredients, kitchen space, and staff training to ensure that each dish is prepared consistently and to the restaurant's standards. Not to mention, a broad menu can lead to higher levels of food waste. But a limited menu may not cater to the preferences of your potential customers, causing them to dine elsewhere.
And these examples just skim the surface! Businesses should consider conjoint analysis any time you want your strategic decisions to be informed by consumer preferences.
Next, let's dive into how conjoint analysis actually works so that you can uncover which features drive consumer preferences.
Overview of How Conjoint Analysis Works
First, identify the key attributes of your product or service that drive customer choice, like price, features, quality, or brand. Then, define levels for each of the attributes. For example, the price attribute may have three levels - $49, $79 and $99. The brand attribute may have four levels - AeroGlow Tech, StellarBlend, LuxoPeak, and ZeniFlora.
Next, you will need to generate an experimental design that combines the levels into different profiles. Each profile represents a product variant. In an online survey, you'll typically present respondents with 3 or 4 profiles at a time and ask them to pick their preferred option. You'll repeat this process over a series of screens, typically 10-12. After the data is collected, you'll build a model that analyzes people's choices and creates a utility value for every level of every attribute. Then, because conjoint believes the total utility of a product is the sum of its parts, you can use market simulations to determine which features your product should include and the optimal pricing for that product.
The beauty of conjoint analysis lies in its ability to quantify consumer tradeoffs. Respondents reveal preferences they can't articulate in traditional questioning by being forced to choose between product profiles.
Ways Conjoint Analysis Benefits Businesses
Conjoint analysis empowers smarter decisions across industries by quantifying consumer tradeoffs. Here are some examples of high-impact business applications:
Smartphone makers can identify the optimal combination of features, specs, and price points across their model lineup. Doing so maximizes appeal across customer segments while minimizing cannibalization.
Cloud software companies can quantify their willingness to pay for different tiers of services. They can then set pricing and feature bundles to maximize revenue.
Auto manufacturers can determine the relative impact of MPG, safety ratings, brand, and options on vehicle choice. They can then focus design resources on features that matter most to consumers.
Consumer packaged goods companies can figure out which new product attributes like organic, gluten-free, and fair trade will command higher prices or drive trial.
The applications of conjoint analysis in business decisions are endless. Conjoint provides a robust framework to enhance product, pricing, and positioning decisions in any industry and produces data-driven answers to questions that impact the top and bottom lines.
Tips for Designing a Conjoint-Based Survey
While conjoint analysis is a powerful technique, it does require careful design and execution.
When designing your first conjoint experiment, consider:
Limiting your attributes and levels to the main drivers of choice. Typically between 5-9 attributes, each with 2-7 levels is ideal.
Asking enough conjoint questions to enable robust analysis but not so many that respondents experience survey fatigue. Typically 10-14 questions is ideal, but you can always increase your sample size in exchange for increasing the number of questions.
Accommodating the entire range of prices. Will your product or your competitors ever go on sale? Be sure to include the absolute minimum, and maximum, either you or your competitor will be priced at. One should not plan to extrapolate consumer preferences outside the price range tested.
Use clear language and define any technical terms, but don't "seed the witness" by playing up the new feature. Consider a hybrid approach by adding a quick qualitative phase to test your descriptions and the respondent experience. Quality data begets quality models; poor data ...
By simulating the tradeoff decisions people face in the real-world, conjoint analysis provides unique and actionable data on consumer preferences otherwise hidden from view. While designing and fielding a robust conjoint study requires expertise, the payoff for manufacturers, marketers, and product developers is immense. Conjoint transforms product and pricing decisions from a guessing game to a data-driven strategic capability.