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Computer vision brings shoppable videos to life : MARKETING

30 second summary:

  • Computer vision is a long-established segment of AI that deals with a machine’s ability to understand and process visual elements and provide appropriate results.
  • However, as Computer Vision continues to evolve and refine, it is increasingly used in retail marketing to understand consumer needs and streamline the buying process through channels such as shop-for-video.
  • Brands and retailers can upload entire catalogs and use Computer Vision to identify all of the products in them. These products can be automatically recognized in video content without retailers spending hours tagging or manually assigning them.
  • With Computer Vision shoppable videos, consumers can open and browse curated product selections without navigating away from the video or being redirected through multiple pages and links.
  • AI enables thorough and ongoing evaluation of visual content to help brands and retailers refine and optimize their video strategies. It allows them to go beyond general metrics like display rate to fully understand viewer experience.

For the retail sector, green shoots are emerging for the time being, with the latest Barclaycard data shows an increase in expenditure. And with the pandemic driving consumers to increase their digital purchases, online retailers are currently claiming £ 3 for every £ 10 spent – It’s important for retailers to rethink their engagement strategies to deliver rich online experiences that really translate into conversions. Buyable videos that consumers can use to interact with featured products and purchase them are proving to be a popular solution, especially when augmented with artificial intelligence (AI) and computer vision, which significantly increases customer loyalty and conversion rates.

What Exactly Is Computer Vision And How Can It Be Used To Boost Shopable Video Performance?

Computer vision is a long-established segment of AI that deals with a machine’s ability to understand and process visual elements and provide appropriate results.

It is already widely used in a wide variety of uses such as facial recognition software and photo tagging functions on social media platforms.

However, as Computer Vision continues to evolve and refine, it is increasingly used in retail marketing to understand consumer needs and streamline the buying process through channels such as shop-for-video.

Here are three ways computer vision and AI can bring shoppable videos to life:

Product recognition in real time

Computer vision enables machines to view visual media such as images and videos and extract detailed attributes such as style, color and pattern in the same way as the human brain.

These attributes can be used to identify retail products in visual media in real time and to connect the consumer with these products.

Brands and retailers can upload entire catalogs and use Computer Vision to identify all of the products in them. These products can be automatically recognized in video content without retailers spending hours tagging or manually assigning them.

Shopping hotspots can then be created that provide consumers with an instantly satisfying experience where they can go straight from product interest to purchase without leaving the video environment.

AI-based recommendations

Computer Vision can not only identify individual products in real time, but also recommend similar products or those that complement elements in which the viewer is interested.

By recognizing keyframes in video content and automatically identifying the products featured, the technology can instantly generate alternative suggestions and recommendations from the retailer’s product catalog.

With Computer Vision shoppable videos, consumers can open and browse curated product selections without navigating away from the video or being redirected through multiple pages and links.

For example, when a consumer watches a promotional video for a new range of patio furniture from a homeware brand, a shopping hotspot can encourage them to browse and purchase complementary accessories such as pillows and lighting, and chairs and tables from the range of furniture itself.

While suggestions for alternative or complementary products are largely based on products within the video content viewed by the consumer, an additional level of personalization can also be added to the equation.

Recommendations can take into account data related to demographics, preferences, and previous browsing or purchasing habits of the user to ensure that the products displayed are most likely to appeal to their individual tastes.

Analytics and insight

AI enables thorough and ongoing evaluation of visual content to help brands and retailers refine and optimize their video strategies. It allows them to go beyond general metrics like display rate to fully understand viewer experience.

You can determine where in video content viewers are most engaged, which hotspots are most frequently interacted with, and where consumers lose interest and fall away.

You can understand highly specific preferences like preferred music tempo and analyze unique attributes like device type and geographic location.

By feeding these insights into content creation, brands and retailers can continually improve the performance of shop-for-video.

When they understand what works and what doesn’t, they can make real-time adjustments to current content, such as: B. fine-tune video images, audio and duration.

You can also use the insights to inform and refine future video experiences and ultimately achieve a better return on investment (ROI).

As the shift to online shopping accelerates, brands and retailers should turn to shoppable video to deliver a rich and interactive digital experience.

When augmented with AI and computer vision to enable real-time product discovery, personalized product recommendations, and actionable analytics, shoppable video offers exceptional engagement and conversion rates so brands can make the most of the surge in consumer spending.

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