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Classified in: Science and technology, Business
Subjects: VEN, SBS

AI-powered Marketing Personalisation Startup, DataSine, Secures £4 Million Series A Funding


LONDON, Feb. 7, 2019 /PRNewswire/ -- London-based AI startup DataSine, which combines expertise in psychology and machine learning to help businesses personalise their communications at scale, has raised £4 million in Series A funding from leading European and U.S. investors, led by Pentech and Propel Venture Partners. Other investors include C.Entrepreneurs/Cathay Innovation, Twin Ventures and Sistema_VC.

DataSine's personality-driven marketing platform Pomegranate is a SaaS suite offering collaborative AI that helps businesses tailor content to resonate with their audience, from the segment level down to the individual customer. It applies machine learning to behavioural data that companies already collect to build individual profiles and provides an AI-powered content editing platform to guide marketers to tailor a range of content elements, including words and images.

This collaborative product philosophy helps marketers make far more informed decisions and spend less time on tasks like copy tweaking and A/B testing and more time on being creative.

By helping marketers personalise everything from emails and landing pages to call centre scripts, DataSine allows the algorithmic tailoring of customer experience to move beyond recommendation systems.

With over 80% of interactions between businesses and their customers expected to occur online over the next five years, it is crucial for businesses to be able to understand customers contextually, and create more personal and human customer experiences digitally. This funding round recognizes DataSine's unique approach to personalisation, based on psychology and AI.

Along with product development, the funding will support the company's continued geographic expansion across Europe, where DataSine already has clients in France, Belgium, Russia and the UK.  While working with DataSine, enterprise customers across Europe have achieved uplift of up to 80% using DataSine's approach. Now DataSine is entering into the SME market through the launch of Pomegranate in March. Pomegranate already integrates with HubSpot and MailChimp, and is looking to launch further integrations throughout the year.

"I launched DataSine after moving to London to do a PhD in psychology because I believed that personality can help companies understand their customers as a whole, which empowers them to truly connect with customers through every interaction and move beyond traditional focus of click optimisation. By helping businesses build genuine connections, we hope to change the way businesses see their customers," DataSine founder and CEO Igor Volzhanin, said. "With this round of funding, we are now in a great position to deliver on our mission to empower companies to treat their customers like individuals."

According to Boston Consulting Group, personalisation will push a revenue shift of some $800 billion to the 15% of companies that get it right over the next five years in three sectors alone (retail, health care, and financial services).

"We feel DataSine is particularly well-positioned to bring psychology and AI to address contemporary marketing challenges," Marc Moens, a partner at Pentech, said. "The idea that digital communications can be tailored for an individual in the age of Big Data is very appealing and addresses the needs of the market. DataSine has a proven ability to drive business outcomes through their technology and we are happy to support them in their growth."

DataSine launched in 2015 and has worked with leading companies from across Europe, including BNP Paribas, Hello bank!, and Tinkoff bank. It has also received numerous accolades, winning the BNP Paribas International Hackathon and being accepted into the prestigious Techstars accelerator programme.

Volzhanin holds a PhD in Psychology and Computer Science from the University of London. CTO Chris Loy and Chief Scientist James Gin both hold degrees in Physics from Oxford University and Machine Learning from University College London.

 


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