What is a Choice-Based-Conjoint (CBC)?

TL;DR

Choice-Based-Conjoint (CBC) is a research method where people pick their favorite product or service options from a list. It helps businesses and their partners understand what features and prices matter most to customers. By seeing these choices, companies can create better offerings that partners can successfully sell, strengthening the entire ecosystem.

Key Insight

Choice-Based-Conjoint (CBC) offers critical data for optimizing a partner ecosystem. It helps businesses understand what features partners value in a partner program. This insight can refine partner enablement and co-selling strategies. It ensures offerings resonate with channel partner needs and market demands.

1. Introduction

Choice-Based Conjoint (CBC) stands as a powerful market research technique. Understanding customer and partner preferences becomes achievable for businesses through its application. CBC works by presenting respondents with a series of choices, each offering different product or service configurations. Configuration options thoughtfully vary in their features, benefits, and price points.

Respondents then select their preferred option from each presented set. The iterative process effectively reveals the relative importance of different attributes. Consequently, companies can make data-driven decisions, including optimizing product design and pricing strategies.

2. Context/Background

Traditional surveys often ask directly about feature importance, which can unfortunately lead to biased answers. Individuals may, perhaps unintentionally, overstate the significance of certain features. CBC, however, simulates real-world buying decisions, forcing necessary trade-offs that mirror actual purchasing behavior. This approach provides notably more accurate insights, proving crucial for building effective partner programs. Understanding channel partner needs remains vital for sustained success.

3. Core Principles

• Realistic Choices: Respondents choose from complete product or service bundles. Bundle options reflect real market offerings.
• Attribute Trade-offs: Each choice set requires respondents to weigh competing attributes. Weighing attributes reveals their true priorities.
• Statistical Analysis: Advanced statistical models analyze the choices. Model analysis quantifies the utility (value) of each attribute level.
• Preference Segmentation: Results can segment respondents into groups. Each group shares similar preferences.
• Predictive Power: The model can predict market share for new product configurations. Optimized offerings result from this.

4. Implementation

1. Define Attributes and Levels: Identify key features and their variations. For a partner program, this might include commission rates, partner portal features, or training options.
2. Design Choice Sets: Create multiple choice tasks. Each task presents 3-5 distinct product or service profiles.
3. Field the Survey: Distribute the CBC survey to target respondents. Potential channel partner candidates or existing partners are suitable recipients.
4. Collect Data: Gather responses on preferred choices. Ensure a sufficient sample size.
5. Analyze Data: Use specialized software to estimate utility scores. Utility scores show the value of each attribute.
6. Interpret Results: Understand which attributes drive preference. Use these insights for strategic decisions.

5. Best Practices vs Pitfalls

Best Practices:

• Keep Attributes Concise: Limit attributes to 5-7. Too many overwhelm respondents.
• Use Realistic Levels: Attribute levels should be believable. Avoid extreme or impossible options.
• Pilot Test Thoroughly: Run a small test to refine questions. Ensure clarity and understanding.
• Segment Results: Analyze preferences by different partner types. Tailored partner enablement results from this.
• Integrate with Business Goals: Link CBC findings directly to product or partner program objectives.

Pitfalls:

• Too Many Attributes: Causes respondent fatigue and poor data quality.
• Unrealistic Scenarios: Choices that do not reflect reality yield meaningless data.
• Insufficient Sample Size: Leads to statistically unreliable results.
• Ignoring Context: Not considering market dynamics or competitive offerings.
• Misinterpreting Utilities: Confusing statistical significance with practical importance.

6. Advanced Applications

1. New Product Development: Design products with optimal feature sets. Maximizing market appeal becomes possible.
2. Pricing Strategy: Determine ideal price points for various offerings. Understand price sensitivity.
3. Market Segmentation: Identify distinct customer or channel partner segments. Tailor marketing messages.
4. Competitive Analysis: Understand how competitors' offerings are perceived. Find market gaps.
5. Partner Program Optimization: Refine partner program benefits. Attracting and retaining top partners becomes easier.
6. Service Package Design: Create service bundles that meet specific partner needs. Improve co-selling success.

7. Ecosystem Integration

CBC strongly supports the Strategize and Recruit pillars of the Partner Ecosystem Operating Model (POEM). For strategizing, CBC provides invaluable data-driven insights, helping define the ideal partner program structure. Understanding precisely what benefits attract the most suitable partners is included in this. For recruiting, CBC informs the value proposition, assisting in crafting compelling offers for potential partners. Understanding attribute preferences can also guide partner enablement efforts, ensuring training focuses on what partners value most. Consequently, this method significantly enhances the overall partner relationship management strategy.

8. Conclusion

Choice-Based Conjoint remains a valuable tool, revealing true preferences by simulating real-world decisions. Businesses gain critical insights into what primarily drives choices, applying equally to both customers and channel partner networks.

Using CBC ultimately leads to better product design and stronger partner program offerings. It ensures resources are allocated effectively, helping build more successful and resilient partner ecosystem models.

Frequently Asked Questions

What is Choice-Based Conjoint (CBC)?

Choice-Based Conjoint (CBC) is a research method where people pick their favorite product or service from a set of options. Each option has different features and prices. This helps businesses learn what customers truly value, guiding them to create better products and services.

How does CBC help IT companies design software subscriptions?

IT companies use CBC to test different software plans. They present users with various combinations of features, support levels, and prices. By seeing which plans users choose, they can figure out the most desired package, balancing features with what customers are willing to pay.

Why is CBC useful for manufacturing businesses?

Manufacturing companies use CBC to design new products like industrial machines. It helps them understand which combinations of materials, warranty, delivery times, and service plans are most appealing to buyers. This ensures they build products that meet market demand.

When should a business use CBC research?

Businesses should use CBC when launching new products, redesigning existing ones, or setting prices. It's especially useful when there are many features or options, and you need to know which combinations are most attractive to your target customers or partners.

Who benefits from CBC research?

Both businesses and their customers benefit. Businesses gain insights to create more appealing and profitable offerings. Customers get products and services that are better tailored to their needs and preferences, leading to higher satisfaction.

Which types of decisions does CBC inform?

CBC informs decisions about product features, pricing strategies, market segmentation, and competitive positioning. It helps businesses understand trade-offs customers are willing to make, guiding them to optimize their offerings for maximum appeal.

How accurate is CBC in predicting customer choices?

CBC is highly accurate because it mimics real-world buying decisions. Instead of just asking what people like, it forces them to choose between realistic options, revealing their true preferences and trade-offs. This makes predictions more reliable.

Can CBC be used for B2B partner ecosystem development?

Yes, CBC is excellent for B2B partner ecosystems. It can help determine which partnership benefits, support structures, revenue share models, or integration features are most attractive to potential partners, encouraging stronger collaborations.

What kind of data does CBC collect?

CBC collects data on participants' choices from various product or service profiles. It doesn't ask direct preference questions but infers preferences by observing which bundles of features and prices are consistently chosen over others.

How is CBC different from simple surveys?

Simple surveys ask direct questions like 'Do you like feature X?' CBC is different because it makes people choose between full product options, forcing them to consider trade-offs, much like they do when making a real purchase.

What are the key components of a CBC study?

The key components of a CBC study include defining the product's features (attributes), listing the different levels for each feature (e.g., price levels, material types), creating various combinations (profiles), and presenting these choices to participants.

Does CBC work for both physical products and services?

Yes, CBC works effectively for both physical products (like a new car or industrial equipment) and services (like a software subscription or a consulting package). Its strength lies in evaluating feature and price combinations across any offering.