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Digital Retailers Drive User Engagement With 3D Imaging

  • Written by  Pantelis Kalogiros, Fyusion

0aaafyusionThere are several significant shifts happening in fashion e-Commerce that are driving the use of 3D imaging into the mainstream online shopping experience. The first trend is the push for a much higher level of engagement with online content. The combination of hyper competition in digital shopping and lower growth in sales means that brands are looking for a highly engaging, differentiated shopping experience. This is especially true for luxury brands. Use of 3D content promises up to 30% higher retention, and over 70% more engagement than static content.

The second trend is toward lifestyle content. Shoppers are far more likely to purchase from online stores that feature visual reviews or shopper-contributed visual content. The rise of third-party fashion influencers using social media outlets, combined with consumers posting their own pictures, whether in reviews or on the sites themselves, has raised the bar for presenting products in an engaging format “on a person” rather than just “on a turntable.”

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When e-Commerce sites put lifestyle content into their stores, consumers are more likely to trust that content and engage with it. Unfortunately, most brands lack the tools to repurpose lifestyle content into a shopping context at scale, as well as the ability to make it engaging. 3D imaging of lifestyle content has the potential to address these issues.

3D images have the potential to enable e-Commerce stores to capitalize on both of these trends. Studies with Fyusion’s e-Commerce customers found that 3D interactive content produced up to a 200% increase in both basket size and order value, and similar gains in click-through rates (CTR) when compared to JPEG content. A 3D image allows the user to move the object and to see it from any angle, to see the object in motion, and to have features of the object visually “tagged” with additional information that is helpful to the buyer.

More importantly, 3D images are not limited to studio environments and can be used to realistically portray lifestyle content. In the lifestyle context, the ability to see the person in motion, and to view them from any angle, becomes even more important to engagement with the consumer.

So why isn’t more content already presented in 3D image format? Until recently, the complexity of 3D content production has been a significant deterrent to its development and use. Let’s take a look at how 3D content was traditionally created. One option was to use a service that creates video of very high quality and uses it to manually generate a 3D image. A second option was to use 3D modeling software to generate a high-quality 3D model of the object that could then be used to generate 3D content. Unfortunately, neither of these options scales to tens of thousands of consumer goods, and neither enables 3D content in a lifestyle, real-world context.

But new software from Fyusion and others combines computer vision technology with machine learning. These solutions change the economics of creating 3D interactive content for digital retailing by enabling any user to capture image content using just a smartphone, and converting it into 3D interactive content either right on the mobile device or by uploading the content and converting it later. Very high quality photo-realistic 3D interactive images can be created using this technology. More importantly, the door is opened to capturing 3D lifestyle content, because any consumer can capture images anytime, anywhere with just a smartphone.

In order for lifestyle, consumer-generated 3D content to be truly useful for e-Commerce, it also must be possible to quickly and easily identify or “tag” the relevant brand items. Tagging seems like it would be easy to do on an image, but on an interactive 3D image good tagging is difficult to achieve. For example, suppose that as a consumer, I want to “tag” my shirt in an image that I have captured. I might view a single image, or a 3D image that is “stopped” in one location, and then I would just add a tag there. But how does that tag get placed on the hundreds or thousands of other images that make up the 3D interactive image? And how does it stay in the same place?

This is a technical problem that requires actually understanding that this is an image of a person and the tag will be located on their upper body. Advanced machine learning software solves this problem by developing an understanding of the “skeleton” of a person, and by then mapping that understanding automatically to each individual image that makes up a 3D interactive image. This last bit of technology is critical to making lifestyle content truly monetizable in e-Commerce.

New software for e-Commerce combines simple capture of images from a smartphone with technology that converts it into a 3D interactive format. This raises engagement while also enabling the tagging features that make that image readily monetizable. The bottom line is that 3D interactive imaging enables lifestyle content that is highly engaging and readily monetizable. As e-Commerce retailing continues to push the boundaries of user engagement with 3D interactive content and lifestyle content, early adopters of technology that enables 3D content stand to gain significant advantage in a highly competitive market.


 

Pantelis Kalogiros is the SVP of Web and Co-Founder at Fyusion. He is responsible for Fyusion's cloud and web services, and is the author of Fyusion’s patented 3D viewer technology, which is available on over 200 web platforms. Kalogiros has 10 years of professional research and engineering experience with web, cloud and mobile technologies, specializing in optimized, fast, scalable architectures and code. He is experienced in application research and development, database design and optimization, 3D Graphics, Computer Vision, Machine Learning and UI development. Kalogiros has authored two books, "Practical Guide to WebAssembly" and "Young Engineer's Guide to Security." He is passionate about research, web standards and open source.

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