At Illuminas, we have spent the last 25 years studying the impact of technological advancements on buyer expectations and market preference. Technology transformation is increasingly led by software and service enablement, which accelerates the speed of product development. From activity tracking on a smartwatch to “everything as a service” cloud computing, rapid change is made possible through downloads rather than manufacturing.

A typical approach to researching product pricing and packaging involves a conjoint methodology, most often implementing a “Choice-Based Conjoint” (CBC). Technology-enabled products can also benefit from conjoint analysis, but must accommodate different conditions than most out-of-the-box methodologies can support.

Why Conjoint Analysis?

Conjoint analysis is a sophisticated market research technique that helps businesses determine how people value different attributes (features, functions, or benefits) that make up an individual product or service. This experimental design technique simulates purchasing scenarios to reveal how consumers make complex choices. By presenting respondents with a series of product profiles that vary in features and price, conjoint analysis allows researchers to decompose customer preferences and quantify the importance of each attribute.

One of the key strengths of conjoint analysis is its ability to convert choice into share estimates, and ultimately optimize product configurations. By analyzing trade-offs that consumers make in their purchasing decisions, businesses can identify the most attractive combination of features across different price points. This information is invaluable for product development, pricing strategies, and market segmentation. Moreover, conjoint analysis can help companies understand price sensitivity, willingness to pay for specific features, and potential cannibalization effects between different product offerings in their portfolio.

However, a traditional conjoint methodology has some weaknesses when addressing today’s rapidly evolving technology products and services. For instance, consider how the wristwatch and fitness tracking have converged into devices that include predictive algorithms and AI coaching, not to mention smartphone functionality delivered to your wrist. Credit cards are now vehicles for a variety of services, ranging from basic financial management up to a deluxe combination of rewards and personalized support. In the B2B realm, on-site computing is giving way to cloud services, which has opened the floodgates to SaaS offerings.  

These examples underscore the rapid pace at which technology is being integrated into products, turning ordinary offerings into versatile, high-demand solutions that cater to a broad spectrum of customer needs. This evolution pushes companies to continuously analyze how their products are used and what new functionalities can be integrated to enhance their value proposition.

In the fast-evolving landscape of technology and consumer expectations, companies often grapple with optimizing an extensive range of product features and pricing strategies—think about how smartwatches as a category reach casual users and hardcore fitness enthusiasts. When exploring new products and services, a brand must solicit product feedback to understand how its offerings address unique customer segments and a diverse set of competitors.

To navigate this complex product landscape, traditional implementations of conjoint analysis often fall short. There are a few challenges specific to the products described here.

Feature Density

• Capturing the full range of features that define the landscape will appear overwhelming in a choice matrix.
• The design space required to capture independent variation across numerous attributes is enormous.
• Because the attribute list spans a diverse product line, not every feature will be relevant to every individual.
• Respondents will adopt simplifying heuristics that may or may not be related to their purchase decision.

Packaging Dependencies

• Experimental designs inherently produce numerous concepts that are irrelevant, dominant, or impossible.
• Feature combinations should reflect the constraints and contextual positioning of a product within a portfolio.
• Standard approaches test the average product at the expense of extremely sparse or feature-rich products.

Price Relevance

• While “unbiased,” the measurement of utility is skewed when asking consumers to make unrealistic comparisons (e.g., assessing a premium feature with a budget price).
• In most cases, we really don’t need a full price curve across irrelevant alternatives. It is more important to capture incremental price sensitivity between related products.
• It is preferable to engineer “biased” observations that have much higher accuracy and predictive value.

Market Optimized Conjoint

Our solution at Illuminas is the Market Optimized Conjoint methodology, which is designed to overcome these challenges through a structured two-stage process. 

Stage 1

Initially, a "macro" component helps us to categorize the broad market segments and identify the right tier and product niche. This stage identifies key competitors, product classifications, and core differentiators into a “market-led” framework. This sets the context by aligning our research with the broader market dynamics and varying customer expectations.

Stage 2

Following the macro analysis, we delve into the "micro" component, where we focus on the specific attributes of each product type. Here, we employ the Choice-Based Conjoint (CBC) methodology but enhance it to suit the complex nature of modern markets. This involves detailed survey tasks that measure the utility and demand impact of different features and prices, ensuring we avoid unrealistic feature-price combinations that could skew the results.

The outcome of this rigorous methodology is a powerful simulator, allowing users to experiment with different product designs and pricing strategies in a controlled, data-driven environment. This tool not only supports strategic decision-making but also provides a clear visualization of how various configurations might perform in the real world.

By implementing the Market Optimized Conjoint approach, we help businesses adapt and thrive in competitive markets by making informed decisions that are grounded in comprehensive, realistic data analysis. This ensures that product offerings are not only well-received by targeted customer segments but are also competitive and financially viable.

The Market Optimized Conjoint methodology represents a significant leap forward in market research techniques, particularly for complex, tech-enabled products and services. By combining macro-level market segmentation with micro-level attribute analysis, this approach provides a nuanced and realistic view of consumer preferences and market dynamics. The resulting simulator empowers businesses to make data-driven decisions about product design, pricing, and market positioning that match the complexity of their go-to-market environment. 

As technology continues to reshape industries and consumer expectations evolve at a rapid pace, the Market Optimized Conjoint methodology stands as an essential tool for companies aiming to stay ahead of the curve. By leveraging this advanced approach, businesses can navigate the complexities of modern markets, optimize their product offerings, and ultimately drive growth and success in an increasingly competitive landscape.