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Qualz.ai

Case Study
Research
Impact Study

Unlocking Brand Health with AI-Driven Quantification of Qualitative Interviews

brandhealth case study

Background

Traditional qualitative research faces challenges in cost, labor, and turning subjective feedback into actionable metrics in today’s competitive market. BrandInsights.org, in partnership with Qualz.ai, uses AI to automate the analysis of qualitative interviews, delivering precise, quantifiable insights into consumer perceptions and brand health for faster, data-driven decisions. 

Challenge 

Understanding consumer behavior is essential for brands, but extracting actionable insights from subjective qualitative data remains a challenge. Traditional analysis methods are time-consuming, prone to biases, and lack scalability, leading to missed opportunities and slower decision-making. Brands need a flexible, efficient framework to convert qualitative feedback into reliable, data-driven insights. 

Methodology 

To address the challenge of extracting quantifiable insights from qualitative data, BrandInsights.org partnered with Qualz.ai to create AI-powered consumer personas for realistic, diverse interviews. Transcripts were processed using a Large Language Model to automate and quantify subjective feedback, providing accurate, actionable insights. This approach identified key trends in consumer sentiment for brands like Samsung and Apple, enabling them to align strategies with evolving consumer expectations. 

Outcome 

The AI-powered analysis provided a deep understanding of consumer perceptions of Samsung and Apple, transforming qualitative feedback into quantifiable insights. Key metrics on brand health, customer loyalty, and digital engagement were uncovered, offering actionable perspectives to shape targeted strategies for growth and improvement. 

Here are the metrics for Apple and Samsung presented on BrandInsights.org and visualized through charts for clearer insights: 

Net Promotor Score (NPS)

NPS is a widely used metric to measure customer loyalty and satisfaction. It evaluates how likely customers are to recommend a product, service, or company to others.

 

Fig 1.1: Net Promoter Score Distribution

Fig 1.1 Illustrates the distribution of ratings given to each brand, with the Net Promoter Scores (NPS) displayed on the horizontal axis and the number of respondents corresponding to each score represented on the vertical axis.

Fig 1.2: Brand Awareness

Figure 1.2 Provides a detailed breakdown of the brand awareness levels among the research participants, categorizing their familiarity with the brands into high, medium, and low awareness. 

Fig 1.3: Brand Reputation

Fig 1.3 Showcases a comparative analysis of the reputation of three prominent tech brands: Apple, and Samsung. Each brand’s reputation is depicted using a bar graph, highlighting the frequency of specific attributes associated with each brand. 

Fig 1.4: Social Media Platforms

The charts show the popularity of social media platforms among Samsung and Apple users. 

Discussion 

LLMs transform qualitative data into quantifiable insights, reducing subjectivity and ensuring consistent brand health assessments. This approach provides a comprehensive view of consumer sentiment, helping brands identify strengths and weaknesses, and make informed, data-driven decisions. 

Conclusion 

AI-driven platforms like BrandInsights.org and Qualz.ai transform qualitative data into actionable, quantifiable insights, helping brands like Apple and Samsung align strategies with consumer expectations. This approach enhances accuracy, scalability, and efficiency, enabling faster, data-driven decision-making. 

 
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