Imagine trying a new flavor of ice cream. You could decide if you like it in two different ways. First, you could look at it objectively: noting the texture, sweetness, or even the creaminess—measurable factors that influence quality. But then there’s the subjective side, where it’s all about how the ice cream makes you feel. Does it bring back memories of summer afternoons? Does the taste spark joy? These emotional reactions offer a personal layer that numbers alone can’t capture.
In research, this balance of objective analysis and subjective insight is just as essential. In this blog, we’ll break down when to use each approach and how they work together to reveal the full picture.

Kicking off with Key Differences

When conducting research, one of the most important decisions a researcher must make is choosing between a subjective or objective approach. These two research methods offer distinct perspectives on how data is collected, analyzed, and interpreted. 

Imagine you want to figure out why people prefer one brand of sneakers over another. There are two ways you could go about it. First, you could ask customers what they like about the shoes—some might say they love the design or that the shoes make them feel more confident. This gives you personal, emotional insights. On the other hand, you could look at sales data—like which models are selling the most and during what season. Both approaches will give you valuable information but focus on completely different aspects of the same question.

In this situation, choosing the right approach—subjective or objective—is crucial. If you only rely on objective data, you might miss out on the "why" behind user behaviors. If you only conduct subjective research, you may lack the hard data needed to validate your decisions. Knowing when and how to use each method is the key to gaining actionable insights that guide product improvements, marketing strategies, and business decisions. Let us dive into each one of these methods separately to get better clarity.

What is Subjective Research?

Subjective research refers to a method of data collection and analysis that is shaped by personal opinions, feelings, or interpretations. In this approach, the researcher’s or participants’ perspectives play a central role in shaping the findings. Subjective research is often used in social sciences, humanities, and fields where understanding human experiences, motivations, and behaviors is essential.

Characteristics of Subjective Research:

• Personal Interpretation: The data is influenced by the researcher’s views or the participants’ personal experiences.
• Open-Ended Data: Data collected is usually qualitative and open-ended, allowing for deeper insights into thoughts and feelings.
• Examples: Interviews, focus groups, case studies, and ethnography.

While subjective research provides a richer, more in-depth understanding of individual experiences, it can introduce bias, making it crucial to recognize its limitations in producing generalized conclusions.

What is Objective Research?

Objective research, on the other hand, aims to eliminate bias by focusing strictly on observable, measurable facts. It is grounded in the belief that reality exists independently of our perceptions. Objectivity in research seeks to remove any personal or emotional influence, providing results that can be universally replicated and verified by others. This approach is often used in scientific studies, psychology, and empirical research.

Characteristics of Objective Research:

• Neutral Stance: The researcher strives to remain detached and unbiased in the collection and analysis of data.
• Quantifiable Data: Data is usually quantitative and based on measurable factors like numbers, statistics, and facts.
• Examples: Surveys, experiments, and standardized tests.

Objective research is highly valued for its reliability and ability to produce consistent, replicable results, but it may lack a nuanced understanding of human emotions or experiences.

Subjective vs Objective in Research: When to Use Each Approach

      Choosing between subjective and objective research depends largely on your research goals and the type of data you need. Both approaches have their strengths and weaknesses, and often, the best research incorporates elements of both.

• Subjective Research, often qualitative, is perfect for uncovering the why behind human behavior. These methods, such as interviews or focus groups, focus on participants’ emotions, thoughts, and perceptions. They provide in-depth insights into why users feel a certain way about a product, service, or experience.

• Objective Research typically quantitative, focuses on gathering measurable, replicable data. This approach is crucial when you need evidence-based insights, such as tracking user behaviour, identifying trends, or testing hypotheses with concrete data.

How Decode can help?

Decode by Entropik offers versatile support for both objective and subjective research methods with its AI-powered Insights features, making it an essential tool for CX researchers aiming to assess brand perception and customer journeys holistically. 

Read more: Emotions AI: a Key to Improve CX

Here’s how Decode supports both approaches within the CX research context:

Objective Research with Decode:

Decode equips researchers with tools to capture quantifiable, unbiased insights that accurately reflect customer behavior. Here are some ways Decode supports objective CX research:

1. Facial Coding: Decode’s facial coding technology detects and analyzes micro-expressions like happiness, surprise, and confusion in real-time, providing objective insights into how customers respond to touchpoints. By quantifying emotional reactions, researchers can assess specific aspects of the experience without relying solely on feedback.
2. Voice Tonality Analysis: Decode’s voice analysis examines tone, pitch, and intonations to detect nuances such as confidence or hesitation. This objective data helps researchers identify moments where customers may feel assured or uncertain, providing a clearer understanding of emotional engagement throughout the customer journey.
3. Eye Tracking: Eye-tracking data from Decode reveals where customers focus their attention, highlighting key points of interest or areas of confusion. Heatmaps and pathing data allow researchers to objectively assess which elements of the brand experience attract attention, optimizing customer touchpoints.
4. Objective CX Metrics Integration: Decode integrates key metrics such as Net Promoter Score (NPS), Customer Lifetime Value (CLV), and churn rates. These performance metrics allow researchers to quantify customer loyalty and retention, offering a clear view of how well experiences drive business outcomes. 

Read more: Why Customer Insights Matter?

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Subjective Research with Decode:

Decode complements its objective features with tools that capture subjective insights, enriching CX research with emotional and experience-driven data. 

1. Emotion Tracking: Decode’s ability to recognize and track distinct emotions gives researchers deeper, subjective insights into customers' emotional responses during interactions. This feature supports CX research by helping teams understand the emotional factors that drive satisfaction, brand loyalty, and engagement.
2. Surveys & Feedback: Decode allows researchers to gather open-ended feedback through surveys and post-experience questionnaires, capturing personal experiences in customers' own words. This subjective data enhances researchers' understanding of specific feelings, motivations, or concerns, which are invaluable for designing more personalized experiences.
3. Sentiment Analysis: Through analysis of voice and text feedback, Decode uncovers subjective attitudes that reveal brand loyalty and satisfaction. Sentiment analysis, combined with Customer Satisfaction Scores (CSAT), allows researchers to gain a nuanced understanding of customer perceptions and how they evolve.

Read more: CX Research is the New Recipe for Marketing Success

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The Power of Combining Objective and Subjective Data with Decode

By integrating both objective metrics and subjective insights, Decode provides CX researchers with a well-rounded view of customer journeys. Objective data lays the groundwork for identifying trends and assessing the impact of specific experiences, while subjective insights uncover the emotions and motivations driving these trends. This dual approach allows researchers to understand not only how customers behave but why, making Decode an invaluable tool for crafting customer journeys that are both effective and emotionally engaging.

The Importance of Balance in Research

In many cases, a combination of both subjective and objective methods—known as mixed-method research—provides the most comprehensive view. By using both approaches, you can gain deep insights from subjective data while ensuring accuracy and reliability with objective data.

Mixed-method research is essential in both B2B and B2C contexts, as it combines subjective insights with objective data to deliver a fuller picture of customer needs, preferences, and behaviors. Here’s how it works in real-world applications across both domains:

1. Product Development for SaaS Platforms: A B2B SaaS provider might use subjective interviews and surveys to gather detailed client feedback on the usability of a newly introduced feature. They’ll then validate these insights with objective usage data, such as login frequency or feature adoption rates, to understand whether clients are finding real value in the feature and if it aligns with their workflows.
2. Account Management in Professional Services: A B2B consultancy firm could conduct subjective interviews with key clients to capture their satisfaction with project deliverables and team interactions. To ensure this aligns with performance, they’ll analyze objective data like project timelines, client retention rates, and repeat business metrics to confirm that the service quality meets expectations.
3. Customer Experience in E-commerce: An e-commerce platform might gather subjective data from post-purchase surveys to capture customer sentiments around shipping speed, product quality, or website usability. This can be supplemented with objective data, such as return rates and average time spent on checkout pages, to see if feedback aligns with behavioral trends and identify areas for improvement.
4. Product Launch in Retail: A retail brand launching a new product might conduct focus groups and surveys to gauge customer reactions to packaging, messaging, and product features (subjective insights). Then, they’d analyze objective sales data, website engagement metrics, and cart abandonment rates to assess if customers’ stated preferences match actual purchasing behavior.

Conclusion

Ultimately, the choice between subjective and objective research depends on your goals. If your research aims to uncover feelings, perceptions, or personal experiences, subjective research is your go-to approach. But if you need factual, repeatable, and verifiable data, objective research is the way to go. For the best results, combining both approaches ensures a comprehensive understanding of your subject, enabling more informed, impactful decision-making. By knowing when to use each method—or when to combine them—you can ensure your research is both insightful and reliable.

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FAQs:

1. What is the key difference between subjective and objective research?
Subjective research focuses on personal feelings and opinions, while objective research is based on measurable, verifiable facts.

2. Can subjective research be reliable?
Yes, subjective research can be reliable for understanding individual perspectives, but it’s important to recognize that it may not always provide generalized, replicable results.

3. Is it possible to combine subjective and objective research?
Absolutely! Mixed-method research combines the depth of subjective insights with the reliability of objective data to create a comprehensive understanding.

4. When should I use objective research methods?
Objective methods are best used when you need factual, quantifiable data, such as in scientific studies or when measuring customer behavior.