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South African performance marketing startup Xineoh has raised US$2 million from US and Canadian investors, to complete and roll out its new product – a personal shopping assistant for video streaming.

Founded in 2014 and based in Bloemfontain, Xineoh has built an algorithm capable of tracking user behaviour and predicting potential purchases; with the solution based on artificial intelligence (AI) and machine learning.

While solutions built by Xineoh’s international competitors see prediction success rates between 3 per cent and 8 per cent, the startup’s algorithm has a prediction rate of 14.5 per cent.

Xineoh has now raised US$2 million from a pool of US and Canadian investors in order to build out a new product – a personal shopping assistant for video streaming, VideoLlama.

VideoLlama will be rolled out in the US, looking at the likes of Netflix, Amazon and Hulu to find users the best possible streaming deals.  The solution will use the Xineoh algorithm to offer up calculated recommendations based on future behaviour.

The assistant asks users to swipe left or right based on their preferences, and specify various viewing parameters – such as whether they’re watching with a partner or a child. The algorithm then retrieves relevant titles, and begins to further refine its suggestions based on the user’s expressed preferences.

“The major problem with streaming services today is that they tend to offer up recommendations based on how much you will like a specific movie or show. And while they aren’t inherently wrong, they don’t necessarily offer up real value for viewers,” says Vian Chinner, founder of Xineoh.

“With VideoLlama, we’ve tapped into our algorithm to serve up realistic recommendations based on the behaviour of others, serving the types of movies or shows you’re likely to stream based on the time of day, your company and various other latent variables.”

The startup will also use the funds to expand its engineering and content creation teams; while it also has plans to expand to new sectors, starting with the real estate space.

“Property is a high-investment purchase, and given the economic environment, real estate portals now need to entice users within the first 10 seconds if they have any realistic hope of sustaining interest,” says Chinner.

“Using our algorithm, these sites will be better able to serve users homes based on what they’re actually looking for, tapping into known associations to produce a refined, concise offering far more likely to result in conversion.”

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