Measuring Customer Love with Emaww

Love. With apologies to the Beatles, it may not be all you need. But it is absolutely what a brand wants every customer to feel for them.


by Kevin Hartman

5 min read



Love. With apologies to the Beatles, it may not be all you need. But it is absolutely what a brand wants every customer to feel for them.

Yet the difficult question remains: how does a Marketing Executive measure and quantify “love” in the context of their consumers? In our personal relationships, love doesn’t lend itself to objective analysis; that’s why it is the stuff of poems and country songs, not statistics and textbooks. But for brands, there is an answer from a company called Emaww.

I have had the pleasure of working with Emaww for about a year now. The company name is constructed from its mandate – Emotion + Awe. It is the brainchild of its brilliant CEO Dr. Alicia Heraz and is a non-intrusive emotion measuring AI that analyzes consumer gestures on devices to accurately detect emotion at scale. Emaww translates the way consumers scroll on their phones or move their desktop mouse into what they are feeling at the moment they experience something online. That something can be an ad, a website, or some content piece like a blog post.

The philosophy behind Emaww comes from the work of Dr. Manfred Clynes and his theory of movement as a vessel for emotion. In the 1970’s, Dr. Clynes discovered discernible patterns in the expression of emotions through movement. Heraz worked under the tenured mentorship of Dr. Clynes for years and translated his expertise into patented algorithms that measure the way emotions are expressed through digital and physical movements.

It is one of the most exciting breakthroughs I have seen in measuring consumer behavior, intent, and (above all) love for a brand.

Attention Measures: A Half-Step Forward

To date, consumer emotions have been measured through a poor set of proxy measures for a long, long, (too long) time. Views. Click-through rates. The kind of surface measures we can conveniently collect at scale. Yet these metrics don’t tell us anything about the underlying relationship that ads have fostered. What’s more, as online media and video inventory has fragmented, legacy, cookie-based metrics like these have become less reliable. These metrics were premised on the consumer’s opportunity or potential to view an ad, but they never did measure actual engagement. They were never able to explain how someone who experienced the content actually felt about it.

Attention-based metrics offer an improvement on behavior measurement by focusing on impact. That impact is not binary. People can pay more or less attention to a video or static content, and attention-based metrics help quantify the level of attention paid to determine which content has the most impact and creates the highest level of brand lift and engagement.

Extensive research shows that certain attributes of an ad determine the potential attention a user pays to that ad, which correlates to its impact. These attributes include:

  • Duration: How long was the ad on the user’s screen while it was playing?
  • Audibility: Was the sound on or did the user turn it on when the video ad played?
  • Player-size: What size video player did the ad appear in? Did that player resize during playback?
  • Location: Where on the page was the video player displayed? If the user scrolled, did the ad mover?

Certain metrics that are intuitively indicative of ad quality and potential attention have a high correlation to awareness and consideration. While I was at Google, our marketing team determined that CIVA (Complete In View and Audible) is the metric that consistently demonstrated the highest correlation to brand lift. Why is that? Because watching a video with sound (the A for “audible” in CIVA) is a more emotionally rich experience than what you get from a muted ad that scrolls through your feed.

Yet attention metrics, like traditional measures, are not enough. They are an improvement in the way that a rowboat and one good oar is to a person lost at sea. What an advertiser astray in an ocean of consumer data really needs is an understanding of consumers’ emotional response to the ads and content they produce.

This is where Emaww comes in.

AI-Driven Emotion Measurement: The Future

Emaww is an advanced emotion analytics system that uses machine learning to interpret and quantify the emotional experiences of users interacting with digital content. The system captures indicators such as gesture speed, direction, force, and other spatio-temporal features of finger and mouse movements, to gauge the depth and intensity of user emotions.

Through Emaww’s proprietary algorithms, we can use the essential components of all enduring emotions – interest, awe, excitement, boredom – to develop a quantifiable framework that provides a dependable, accurate, and comprehensible way to understand how consumers feel about them. These emotions are detected with a high degree of accuracy (over 96%) and give the insight into emotion advertisers need to make better, more resonant ads. Emaww’s insights can contribute to marketing strategies in several meaningful ways:

Content Optimization:

  • Tailor User Experience (UX): Analyze emotional responses to website content to understand what resonates with the audience. This can guide the creation or modification of content to better align with user preferences and emotions.
  • Deepen A/B Testing: Conduct A/B testing on different versions of web content or ads, comparing the emotional responses to see which variant elicits the most favorable emotional reaction.

Ad Personalization:

  • Segment on Emotional Response: Segment audiences based on their emotional reactions to content or ads. This allows for more personalized messaging that speaks directly to the emotional state or preferences of different groups.
  • Implement Dynamic Creative Optimization (DCO): Use real-time emotional feedback to adjust creative elements of ads on the fly, ensuring that the messaging stays engaging and relevant to the user's current emotional state.

Customer Journey Mapping:

  • Identify Pain Points and High Points: Pinpoint where consumers feel frustration or confusion versus interest or delight in their interaction journey. This can help in streamlining the user interface, optimizing the customer journey, and addressing any areas causing negative emotions.
  • Enhance The Conversion Funnel: Understand the emotional triggers that lead to conversions or drop-offs. Use this insight to refine the steps in the conversion funnel, making it more emotionally engaging and persuasive.

Product Development and Feedback:

  • Incorporate Emotional Insights into Product Design: Use emotional data to understand how consumers feel about certain features or services, guiding product development to cater to user preferences and emotional needs.
  • Improve Consumer Testing and Feedback: Implement this technology in user testing phases to gain deeper insights into user reactions and feelings towards the product, beyond traditional metrics.

Brand Perception and Positioning:

  • Monitor Brand Sentiment: Measure how consumers emotionally interact with brand-related content or campaigns to gauge overall brand sentiment. This can inform brand positioning strategies and public relations efforts.
  • Build Better Brand Stories: Craft narratives and brand messages that resonate on an emotional level, building a deeper connection with the audience.

The incorporation of Emaww's emotion detection technology into digital marketing tactics represents a major breakthrough in how companies engage with their audience. Using the capabilities of machine learning to decode the emotional nuances of consumer engagements, Emaww offers a framework for improved content analysis, personalized advertising, mapping consumer journeys, refining products, and positioning brands effectively.

Emaww’s Results

By integrating Emaww's emotion analytics into these aspects of marketing, advertisers can create more engaging, personalized, and emotionally resonant experiences for their customers, ultimately driving better marketing outcomes. Many have, and the impact is undeniable:

  • With Emaww's pre-launch assessments, companies experience a 2.5X higher ROI on their advertising campaigns.
  • Advertisers that use Emaww to inform their creative messages see a 40% improvement in the effectiveness of their creative campaigns.
  • Businesses that employ Emaww's metrics in their user experience design process see 98.7% quicker task completion rates.
  • Retailers using Emaww cut out 2/3 of their cart abandonment occurrences, reducing cart abandonment rates by 67%.
  • Using Emaww to fine-tune content strategy led to a 2X increase in content engagement and 5.4X longer session durations.

Emaww's AI-powered emotion measurement is not just a step forward; it's a complete change in the way marketers perceive and connect with audiences. By utilizing detailed indicators of user emotions, we unlock the ability to truly connect with consumers at every point of interaction. This goes beyond simply improving content or customizing advertisements; it transforms the very nature of customer experience in the digital era.

How To Pivot To An Emotion Measurement

The evidence is clear: increased ROI, more effective campaigns, faster task completion rates, lower cart abandonment, and significantly improved content engagement. With Chrome's cookie depreciation expanding rapidly, it is crucial to examine, experiment with, and align marketing strategies using emotional measurement techniques.

The opportunity to understand emotions is an opportunity for marketers to earn more love from their customers. The future of marketing is here, and it will be led by those who can tap into consumer emotions effectively. If you want to harness this power to elevate your brand, reach out to Dr. Heraz at today.