Harnessing Emotion AI for Smarter Product Testing

In today’s competitive marketplace, creating physical/digital products that resonate with consumers requires more than technical innovation—it demands a deep understanding of how people feel when interacting with your products. Whether it’s a high-end smartwatch, a cosmetic line, or even a digital product like a software interface, emotion plays a pivotal role in shaping opinions, buying decisions, and long-term loyalty. Product testing with AI, particularly Emotion AI, offers brands a cutting-edge tool to tap into these emotional responses, revolutionizing product testing by providing actionable insights.

Why Product Testing?

Product testing evaluates a product’s performance, functionality, and overall user experience before it is launched to the market. It ensures that the product meets customer needs, operates as intended, and identifies areas for improvement by gathering direct feedback from real users. 

This process helps businesses refine their product, fix potential issues, and assess consumer reactions to its design, features, and usability, ultimately increasing the likelihood of market success.

Imentiv AI is a cutting-edge Emotion AI tool that analyzes video data to capture and provides an unfiltered, real-time view of how users emotionally respond to products through videos or live testing environments.

The Role of Emotion AI in Product Testing

In-Depth Emotional Analysis

With Emotion AI, brands can break down video footage of consumers testing physical or digital products, offering a detailed view of their emotional journey. 

Emotion AI proves valuable in testing a wide array of products, offering deep insights into subtle emotional responses like hesitation, satisfaction, or frustration that traditional testing might miss. 

In cosmetics, for instance, users' micro-expressions during application—such as a smile of delight or a frown of uncertainty—can reveal how well the product feels on the skin or if it meets expectations. 

Similarly, in consumer electronics, Emotion AI can track real-time emotional reactions when users interact with gadgets like smartwatches or smartphones, detecting moments of excitement or confusion as they explore features. 

Another key area is automotive testing, where Emotion AI can capture driver reactions (even during test drives) to new vehicle interfaces or autonomous features, identifying discomfort or satisfaction during real-world trials. 

Whether it’s cosmetics, tech devices, or even automotive design, the emotional cues picked up by our AI provide actionable insights, helping brands refine their products and ensure a deeper connection with users, both functionally and emotionally.

By analyzing three core emotional dimensions—valence (positive or negative emotions), arousal (the level of emotional stimulation), and intensity (the strength of the emotional reaction)—Emotion AI quantifies the overall emotional impact of a product. 

In the beauty industry, Emotion AI can effectively track consumers' emotional reactions to cosmetic products, capturing nuances like excitement or hesitation during application. By analyzing videos of users applying skincare or makeup, brands gain valuable insights into consumer feedback on product texture, scent, and finish. 

This technology allows brands to identify subtle emotional shifts—such as whether a foundation feels too heavy or if a lipstick shade is unexpectedly disappointing. This rich emotional data empowers brands to refine formulations and packaging, ensuring they resonate more deeply with users.

In the medical industry, where new medical devices or patient aids are being developed, understanding emotional responses can be crucial. For example, patient comfort or anxiety while interacting with a new medical aid can be analyzed, leading to design improvements that enhance usability and reduce patient stress. 

Software products and digital interfaces can be challenging to test through traditional feedback mechanisms. Users may not always articulate where they’re getting stuck or frustrated while interacting with a new design. Emotion AI, however, can detect subtle facial expressions and changes in emotional intensity that reveal frustration points in a user’s journey through a website or app. This data is invaluable for improving user experience (UX) and optimizing software design.

One of the key strengths of Emotion AI is its ability to capture authentic reactions in real-world settings, where users are likely to respond more naturally. 

The Rubik's Cube, introduced in the 1970s, quickly evolved from a simple puzzle to a global best-seller due to its unique features that captivated many. Its complexity kept people engaged, making them eager to solve it. Imentiv AI analyzes how users engage with and react to a product, measuring their excitement and interest, much like the Rubik's Cube did through its design.

Benefits of Using Emotion AI in Product Testing

• Objective Emotional Insights: Rather than relying on subjective recall or biased feedback, Emotion AI provides objective data based on real-time emotional reactions. This gives companies a more accurate understanding of how consumers feel about their physical or digital products, allowing for data-driven decisions that optimize design, features, and usability.

• Efficiency and Scalability: Traditional product testing can be time-consuming and resource-intensive, often requiring numerous rounds of focus groups or surveys. Product testing with AI automates emotional analysis, speeding up the process and making it more scalable for large-scale projects.

In-Depth, Unbiased Feedback: Consumers don’t always articulate their true feelings in surveys. Emotion AI uncovers subtle emotional cues that might be missed through direct questioning, such as a fleeting look of surprise, a smile of approval, or even a frown of confusion. This allows companies to gather unbiased emotional data that can be more insightful than verbal feedback alone.

To illustrate the power of product testing with AI, let’s look at a YouTube Shorts video where a popular YouTuber tests the step-count accuracy of several smartwatches, including the Apple Watch Ultra 2, Samsung Galaxy Watch 3, Pixel Watch 2, and Garmin Phoenix 7, alongside a budget pedometer. This product testing/reviewing scenario offers valuable insights into how various smartwatches measure user steps.

Our Emotion AI analyzes emotions (using facial expressions), vocal tone, and body language,  providing deeper insights into how testers feel about the products beyond what is explicitly said.

Context of the Product Testing

In the video, the tester wears all of the smartwatches simultaneously and walks 1,000 steps, measuring the performance of each watch's step counter. This comparison gives viewers a clear idea of how accurate each device is in counting steps and highlights the performance gap between premium products and cheaper alternatives. The video culminates with a clear preference for the Pixel Watch 2, noting it as the most accurate device for arm-based step recording.

Epistemic Emotion: Cognitive Engagement and Curiosity Epistemic emotions are tied to cognitive processes such as learning, problem-solving, and gaining knowledge. In this video, the reviewer’s curiosity about the accuracy of step counters across different smartwatch brands drives the testing process. 

The reviewer demonstrates a reflective and inquisitive approach, focusing on gathering insights into product performance rather than expressing strong emotions like excitement or disappointment. This type of emotional engagement reflects a thoughtful evaluation, where the primary goal is understanding the product's capabilities, not just reacting emotionally to its design or branding.

Facial Emotions: Balance Between Happiness and Neutrality The reviewer exhibits both happiness and neutrality, as observed through the Facial Action Coding System (FACS):

• Happiness: Mild expressions of happiness are observed through AU 12 (Lip Corner Puller), indicating a slight smile. This is not an intense smile but rather one of polite satisfaction, suggesting a moderate level of approval for the product's performance. The mild AU 6 (Cheek Raiser) and AU 25 (Lips Part) further indicate that the smile is restrained and professional, rather than enthusiastic.

• Neutrality: During much of the video, the reviewer maintains a neutral facial expression, characterized by relaxed muscles and minimal facial activation. This neutrality suggests an objective and composed demeanor, signaling that the reviewer is maintaining a balance between positivity and professional restraint. The combination of these emotions reflects a nuanced reaction—while the reviewer is pleased with the product, they are careful to remain unbiased and measured in their response.

Valence: Slightly Positive (0.30) Valence, the measure of emotional positivity or negativity, sits at 0.30 in this analysis, indicating a slight leaning toward positive emotions. This aligns with the subtle expressions of happiness observed in the reviewer’s facial expressions. The emotional state is positive but subdued, reflecting mild contentment or approval rather than strong excitement. This is typical of product reviews, where the reviewer maintains a professional tone while subtly communicating satisfaction with the product’s performance.

Arousal: Low Intensity (0.19) Arousal, which measures emotional intensity or activation, is quite low at 0.19. This suggests that the reviewer is calm and composed throughout the testing process, rather than highly animated or emotionally charged. This low arousal level aligns with the epistemic emotion focus, as the reviewer is primarily engaged in critical thinking and problem-solving rather than reacting impulsively. This controlled emotional state enhances the credibility of the review, as it conveys a thoughtful and analytical approach to product testing.

Intensity: Moderate Emotional Expression (0.35) Emotional intensity, measured at 0.35, indicates that the reviewer’s emotional expressions are present but not overwhelming. The moderate intensity of emotions supports the idea that the reviewer is communicating their feelings—whether positive or neutral—in a restrained manner. This suggests that while the reviewer is emotionally engaged with the product, they are also intentionally tempering their reactions to maintain a balanced and professional evaluation.

Now, let's turn our attention to the text transcript of the same YouTube short video, where our Text Emotion AI analyzes the emotional shifts embedded in the dialogue. This AI-powered text-emotion analysis helps you to understand how the script impacts the viewer's emotional engagement.

Engagement & Excitement

Our Multimodal Emotion Recognition technology analyzes YouTuber’s tone and facial expressions to measure excitement or enthusiasm at different stages of the test. 

For example, during the counting of steps, the tester displays a sense of amusement, humorously commenting, "I feel like Mr Beast now."

_{Text Emotion Recognition identifies ‘curiosity’ as the dominant emotion in the first section of video transcript (YouTube Shorts video)}

This comment indicates a playful mood, and through Emotion AI, you can quantify the level of enjoyment and engagement, which may suggest a positive user experience.

Frustration or Satisfaction

Toward the end, when the tester comments on the cheap $9 pedometer, saying "don't buy one of these," there is clear dissatisfaction. Our Video Emotion AI tool detects facial expressions that signal frustration, which would confirm the negative emotional response to the budget product. Understanding this frustration can guide improvements in cheaper alternatives or highlight areas where premium products outperform.

Brand Comparisons in Influencer Reviews

When influencers or YouTubers test multiple products from different brands, Emotion AI can be a game-changer in identifying which product evokes the strongest emotional response. If the YouTuber in our smartwatch example consistently shows more positive emotions (such as smiles, surprise, or engagement) toward one brand over another, the insights can be used by that brand to emphasize key differentiating features in their marketing campaigns.

Let's explore some key psychological theories that enhance our understanding of emotional responses:

Psychological Theories and Principles

• Cognitive Evaluation Theory (Deci & Ryan, 1985): This theory suggests that external events, such as product performance, influence intrinsic motivation. In this case, the reviewer’s epistemic emotions indicate that they are motivated by curiosity and a desire to evaluate the product’s functionality. The low-arousal and neutral demeanor reflect an analytical mindset, where the focus is on knowledge acquisition rather than emotional attachment to the product.

• Facial Feedback Hypothesis (Ekman, 1972): According to this hypothesis, facial expressions can influence emotional experiences. The combination of mild happiness and neutrality in the reviewer’s expressions suggests that they are internally balancing positive feelings about the product with an objective evaluation. The restrained smile may help maintain a neutral emotional state, further reinforcing the credibility of the review.

• Appraisal Theory (Lazarus, 1991): This theory posits that emotions are generated from evaluations of events. In this video, the reviewer’s appraisal of the product’s features leads to moderate positive emotions, such as contentment or satisfaction, but not enough to elicit high-arousal responses. The focus is on thoughtful assessment rather than emotional reactions.

Impact on the Audience 

The balanced combination of epistemic engagement, low arousal, and restrained positive emotions likely conveys a sense of reliability and credibility to the audience. The reviewer’s mild smile creates a subtle sense of satisfaction, while the overall neutral tone helps maintain a professional and impartial stance. This kind of review style may resonate with an audience seeking thoughtful, objective assessments of products. The video suggests that the product is reliable and interesting, but not groundbreaking or emotionally captivating.

Personality Analysis of the Video

Openness (63.53%) Openness, a personality trait associated with creativity, curiosity, and the willingness to explore new ideas, is measured at 63.53% in this review. This relatively high score indicates that the reviewer is intellectually engaged and approaches the product with curiosity. The reviewer is likely open to exploring the product’s features and is intrigued by its performance, aligning with the epistemic emotion focus. Their openness allows for a thorough evaluation, free from preconceived biases, making the review more insightful and comprehensive.

• Curiosity and Exploration: The high openness score suggests that the reviewer is genuinely curious about the product and eager to explore its functionalities. This mindset fosters a sense of discovery, which may be reflected in the reviewer’s focus on the accuracy of the step counters and their willingness to experiment with different brands.

• Willingness to Consider New Ideas: The reviewer’s openness also indicates a receptiveness to innovative or unique product features. Rather than approaching the review with a fixed mindset, the reviewer seems open to assessing the product’s novelty and performance objectively.

Impact on Review and Audience 

The high openness score enhances the depth and thoughtfulness of the review. It indicates a reviewer who is not only curious but also willing to explore the product in detail. This trait appeals to an audience looking for in-depth, unbiased product assessments, especially in a context where product testing requires careful consideration of technical performance and user experience.

The integration of Emotion AI into both physical and digital product testing provides valuable feedback that can shape both the design process and marketing strategy. By understanding which elements of a product generate strong emotional responses, companies can fine-tune their product offerings before they hit the market. 

Additionally, knowing which emotions are triggered by certain features allows marketers to create more targeted campaigns that resonate with consumers on a deeper level.

With Emotion AI revolutionizing product testing, brands now have the ability to measure emotional responses, enabling more accurate targeted market segmentation. From cosmetics to smart devices, emotion recognition offers insights that ensure products don’t just meet functional needs but also emotionally resonate with consumers, paving the way for greater market success.

The Future of Product Testing with AI

As an Emotion AI technology company we are continually innovating and exploring new applications in product testing. Future developments may include:

• Real-time emotional analysis during in-store shopping experiences: Using AI to capture and analyze emotions in real-world retail settings.
• Integration of Emotion AI with other data sources: Combining emotional data with customer reviews and social media sentiment for a more comprehensive understanding of consumer behavior.

Find out how Emotion AI is unlocking new opportunities for innovation in the cosmetic industry!