Designed to seamlessly integrate with a publisher’s pages, Suggestv is an AI video distribution platform that is ‘slick, fast and optimised to encourage more video plays, more ad revenue and more time on a site’. With publisher clients including Bloomberg, DC Thomson, Stylist and UNILAD, WNIP caught up with Suggestv’s Founder James Pringle to find out more about his company’s AI video tech.
Can you give us some background about your company?
I founded Suggestv in London in June 2015 after seeing the need for publishers to increase their revenue from video advertising and reduce reliance on display. The mission was simple – to accelerate media’s transition to video.
What business problem is your company addressing?
Publishers have had a continual problem around monetisation. With the increase in mobile traffic, yield is under more pressure than ever but video provides hope as the format commands higher CPMs than display and native advertising. Users are craving more video content and advertisers are spending more on digital video – therefore publishers have to provide video in every user engagement to stay relevant and savvy. Adding video to every article is an impossible task for often under-resourced editorial teams, so it makes complete sense to use clever AI technology to automate the distribution of video content into articles.
What is your core product addressing this problem?
Suggestv automatically and intelligently inserts a video player with relevant video content into publishers’ articles. The player sits as a “Watch Next” unit at the bottom of the article. Suggestv increase the average number of articles with monetisable video in them from 30% to over 85% immediately.
How does your product/solution work?
Just as an editorial team would select a video for an article, Suggestv analyses a publisher’s articles and inserts a video player containing the most relevant videos from the publisher’s library, saving time, reducing friction and adding volume to a publisher’s video strategy. In short:
- NLP technology scans the article to analyse semantics of the article.
- Keywords are then cross-referenced with a proprietary knowledge graph.
- The information gathered is then used to search within the video library and returns a playlist of videos to play in the video player.
- The playlist is organised by Relevancy x Recency x Engagement x Brand Safety .
Suggestv works on a SaaS model. We charge a CPM on video views. Suggestv works with the publisher to find a rate or tiered rate that works for them and ensures high margin revenue per video view.
How you view the future?
Suggestv will be adding some exciting things over the next few months. Specifically around mobile. However in the future we see a big opportunity around syndication and also rights management, potentially developed using a blockchain smart contracts system. Video is here to stay and it is easy to scale using our tech.