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[NEW RESEARCH] Image Intelligence: making Visual Content Predictive

New research from Altimeter's Susan Etlinger on how machines can recognize visual content and turn it into actionable data.

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[NEW RESEARCH] Image Intelligence: making Visual Content Predictive

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  1. PREVIEW COPY IMAGE INTELLIGENCE: MAKING VISUAL CONTENT PREDICTIVE Including 30 use cases for image intelligence in the enterprise By Susan Etlinger, Analyst Altimeter, a Prophet Company July 18, 2016

  2. PREVIEW COPY EXECUTIVE SUMMARY People no longer communicate online simply via written content, such as posts and comments; they upload and share billions of photos every day. This can be both exciting and terrifying from a brand perspective, because approximately 80% of images that include one or more logos do not directly refer to the brand with associated text. As a result, organizations are missing the content and meaning of images and are unable to act on the opportunities or risks they present. Companies ranging from technology startups to industry Goliaths, such as Facebook and Google, are developing technologies that use artificial intelligence to analyze the content of images. Increasingly, they’re applying analytics to images to better understand their impact on the business. But the opportunity for organizations to make sense of images isn’t just about recognition and analysis; it’s about image intelligence: the ability to detect and analyze images, incorporate them with other data sources, and develop predictive models to forecast and act on emerging trends. This report lays out the current market opportunities, challenges and use cases for image intelligence and offers recommendations for organizations that wish to unlock the predictive potential of visual content. TABLE OF CONTENTS Executive Summary The Rise of Visual Media How Do Computers See? From Computer Vision to Image Intelligence The Business Value of Image Intelligence Privacy, Trust and Customer Experience Challenges of Image Intelligence A Look at the Future Recommendations Endnotes Methodology Brands, Researchers, Agencies and Industry Experts (10) Technology Vendors (17) Social & Digital Media Technology Platforms (3) Acknowledgments About Us 2 3 7 12 14 24 26 29 30 33 34 34 34 34 35 36 | @setlinger | 2

  3. PREVIEW COPY THE RISE OF VISUAL MEDIA “I see more and more people sharing images and getting away from text; look at the explosion of memes and emoji. It’s becoming a more and more complex environment, how people are communicating over social media.” — Glen Szczypka, Deputy Director, Health Media Collaboratory, National Opinion Research Center at the University of Chicago | @setlinger | | @setlinger | 3 3

  4. PREVIEW COPY The ubiquity of smartphone cameras, combined with increasing use of social networks, has led to an explosion in picture taking and photo sharing. According to Mary Meeker’s 2016 Internet Trends report, people share and upload over 3 billion images every day on Facebook properties (Facebook, Messenger, WhatsApp and Instagram) and Snapchat alone (see Figure 1). FIGURE 1 IMAGE GROWTH REMAINS STRONG, SAYS MARY MEEKER’S INTERNET TRENDS REPORT Source: Snapchat, Company disclosed information, KPCB estimates Note: Snapchat data includes images and video. Snapchat stories are a compilation of images and video. WhatsApp data estimated based on average of photos shared disclosed in Q1:15 and Q1:16. Instagram data per Instagram press release. Messenger data per Facebook (~9.5B photos per months). Facebokk shares ~2B photos per day across Facebook, Instagram, Messenger, and WhatsApp (2015) In addition to sparking trends and conversations, photo sharing is driving technology innovation. Markets and Markets, a research firm, expects the image-recognition market to reach nearly $30 billion by 2020, driven in large part by sharing via social media.1 Image recognition — what Gartner defines as “technologies [that] strive to identify objects, people, buildings, places, logos, and anything else that has value to consumers and enterprises” — is just the first step in deriving insight from and acting on images, however. The next step is to analyze them to better understand their context and impact. The photo on the following page provides a good example (see Figure 2). | @setlinger | 4

  5. PREVIEW COPY A human can easily interpret this photo as a woman playing tennis at the U.S. Open. If she is a tennis fan, she may even recognize Ana Ivanovic. But a computer simply “sees” a collection of pixels that it must then classify into objects (a woman, a tennis racket, some logos, and so on). It then must interpret those objects to infer meaning: a woman playing in an event in the US Open Series, sponsored by Sony Ericsson and Olympus. FIGURE 2 MEASURING THE VALUE OF IMAGES The value of this photo to a brand such as Sony Ericsson or Olympus is its effectiveness at reaching as broad an audience as possible. When this photo is shared in social or digital channels, however, it is unlikely to include any explicit brand mention such as a hashtag or caption. But for brands that sponsor sporting events, the ability of computers to detect these types of brand mentions can be extremely valuable tools for measuring calculate sharing behavior, reach and, ultimately, sponsorship ROI. | @setlinger | 5


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