Top 3 reasons why computer vision is changing the asset management game
Marketing professionals, agencies and influencers are using computer vision to drive engagement, increase leads, and boost creativity
Events in 2020 have turned the business of digital advertising and social media on its head. Without the intelligence to navigate this turbulent environment, there’s the risk that even the most visually striking campaigns will miss their audience engagement and revenue goals.
At the same time, public opinion is highly sensitive . One false commercial step can do long term reputational damage to brands and their agencies.
At the heart of the storm, heads of digital marketing (and their budgets) are under greater pressure than ever. Tasked with protecting and promoting, the brand they must squeeze every drop of value from marketing software subscriptions, third-party agencies and media libraries.
The consumer connection
How best to meet these strategic and financial challenges? Many players in this digital marketing ecosystem are looking to computer vision as they refresh their connection with consumers in the post-pandemic world and build deeper relationships that drive engagement, leads, and revenues.
This is where computer vision can help. It uses deep learning techniques to analyse, filter and enhance visual content depending on parameters set by the user. Which images play best with a particular audience, for example? How do you ensure that what you publish is on brand? How do you avoid publishing inappropriate content?
Here are three proven ways that computer vision can help maximize the value of visual content including photography, video and graphics.
For social media and other community platforms, computer vision offers an opportunity to level up the quality of their visual content.
Take an advertising platform where customers use their own content to promote a product or service. The software can be trained to filter out low quality, user-generated images and help the customer select ones that drive higher click-through rates and leads.
There are similar advantages for social media publishing platforms, especially those with a focus on visual content and channels such as Instagram, Snapchat and TikTok.
Imagine being able to offer subscribers the ability to filter their images according to engagement potential, hashtag reach and more. Or offering them a creative brief before shooting starts based on the most popular hashtags connected with their audience.
The same principles can be applied to video. Which clips will have the greatest engagement? Which section of a recording best matches the interests of my audience? How long should it be? Computer vision software can scrutinise thousands of clips far faster than the human eye and provide recommendations to subscribers based on the platform’s historical engagement data.
Computer vision also learns from social trends and audience sentiment, using these criteria to further refine image selection. In addition it will flag and block inappropriate content—always a risk with user generated imagery.
In a fast moving market, marketing agencies can benefit from disruption to grow their client base. Especially in 2020 when Instagram, Snapchat and TikTok have all reported massive spikes in engagement due to exponential growth in user screen time.
How do you stay relevant in the face of so much change ? Computer vision technology can be quickly trained to match influencers and their content with clients and their brands. It is efficient, highly reliable and enables influencer agencies to add greater value to existing client relationships while attracting new ones.
Creative agencies also benefit. This year’s economic crisis has limited production resources, putting them under greater pressure than ever to deliver high-volume, brand consistent content. Computer vision gets the most out of constrained resources by analyzing massive volumes of photography and visual content, and serving up brand-friendly imagery. This dramatically reduces the many hours of manual searching for suitable materials.
Agencies can also train the software to analyze previous advertisements and filter new creative endeavours based on successful campaigns. Such analysis can be segmented by channel, demographic, territory and more.
Think of it as augmented intelligence for creatives. It frees them up to better focus on creative decisions that transform pre-filtered content into eye-catching campaigns that thrill both agency clients and their audiences.
Media organisations with vast commercial content and photo libraries are facing massive disruption from a new wave of ‘free download’ competitors. In addition, global events, such as the Covid-19 pandemic have dramatically reduced the volume of new images taken at hundreds of cancelled sporting, cultural and social events.
This makes it all the more important to squeeze every drop of revenue from existing archives.
Computer vision algorithms trained on the highest-grossing images can search the archives matching content, aesthetics and even the emotions associated with the most commercially successful content.
The software will then tag the images with keywords that match customer’s search intent. As a result, customer searches will return more relevant, higher quality content that is more likely to trigger a purchase.
The good news: As external pressures escalate, marketers, agencies, influencers and media organizations have at their disposal computer vision tools that enable them to analyze the commercial value of their content and act on it.
Computer vision technology can be trained quickly ‘out of the box’ on your historical data, delivering rapid return on investment. Value increases as algorithms learn instantly from current affairs and shifting consumer emotions.
The time to act is now.