SCA invests in SourseAI to power LiSTNR content recommendations

Pictured: SCA’s head of digital and innovation, Chris Johnson 

Southern Cross Austereo (SCA) has become an early-stage investor in Australian-based artificial intelligence (AI) and machine-learning company, SourseAI.

SourseAI will help recommend content to LiSTNR users based on behavioural insights and mood states. It will, according to SCA, help facilitate a hyper-personalised experience across the platform.

Other capabilities from SourseAI such as forecasting and anomaly detection will also be used to schedule seasonal content, analyse genre growth and identify changing tastes among audiences.

The patented augmented intelligence tool will be used alongside SCA’s own in-house analytical capabilities and has already been connected to metadata from SCA’s investment in Sonnant, another AI company, helping to drive data-led marketing decisions within the business.

SCA’s head of digital and innovation, Chris Johnson, said that the investment would help facilitate “a deeply personalised listening experience to consumers, based on consumption habits and context”.

“It also enables us to gain a rich understanding of our audience’s behaviour, enabling us to continue to create market-leading advertising solutions,” Johnson said.

“The AI and machine-learning space is scaling rapidly, and we believe that investing in the Australian entrepreneurial ecosystem to support our digital audio ambitions is the best strategy. SourseAI is the right partner to deliver on our vision and our investment will provide significant long-term value to both parties.”

SourseAI CEO, Matt Jones, said that the company’s sophisticated machine-learning technology would benefit LiSTNR users and advertisers alike.

“Machine learning models make it possible to drive the key media metrics of frequency of engagement and time spent,” Jones said.

“By personalising the experience with Sourse, SCA will progress these metrics, resulting in a more engaged audience, and delivering improvement across both advertising targeting and yield.”