Chattermill raises £600K to use ‘deep learning’ to assistance companies make clarity of patron feedback

Chattermill, a London-based startup that uses ‘deep learning’ to assistance companies make improved clarity of patron feedback, has lifted £600,000 in seed funding. Backing comes from Entrepreneur First — Chattermill is an alumni of a association builder — and Avonmore Developments, along with a series of angel investors, including Jeff Kelisky, CEO of Seedrs.

Founded in 2015 by friends Mikhail Dubov and Dmitry Isupov, Chattermill is one of a series of startups that are rebellious a problem of how to differentiate by and respond to patron feedback and opposite mixed channels. With that information flourishing exponentially, a association is contracting low training to assistance do a pursuit in, arguably, a many some-more scalable and potentially some-more accurate way.

“We assistance companies know and urge their patron experience: we give companies discernment that helps them qualification improved products and services,” Dubov, Chattermill’s CEO, tells me. “Companies with best in category patron knowledge eventually have some-more constant business and find it easier appropriation them in a initial place. Customer feedback is a best information to know patron knowledge and while many companies have a lot of patron feedback, few have a collection to remove discernment from it”.

He says a startup’s resolution is to request a latest low training techniques to analyse patron feedback in a approach that is tailored for any company. “In further to this we yield an analytics dashboard and programmed alerts that make it really easy to take movement on a discernment opposite a business,” he says.

Specifically, Chattermill collates all feedback channels in one place and afterwards “builds a customised low training indication to remove simply actionable insight”. It can afterwards magnitude view to see how business are feeling about any partial of a altogether experience, from pattern of an app down to speed of smoothness and opinion of patron caring agents.

In terms of how it collects patron feedback information in a initial place, Chattermill integrates many customary collection used for soliciting and monitoring patron feedback and sentiment, such as SurveyMonkey, Zendesk, TypeForm or Salesforce, in further to aggregating feedback from Net Promoter Score surveys, reviews, support tickets and amicable media.

The ten-person Chattermill group is now operative with business opposite sectors that include, fintech, e-commerce, transport and gaming. “We work with consumer businesses that have a vast patron bottom opposite industries,” says Dubov. “Notable examples are Transferwise, HelloFresh and Just Eat. Within these companies we work with a product, patron use and patron knowledge teams”.

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Posted by on Dec 7 2017. Filed under NEWS. You can follow any responses to this entry through the RSS 2.0. You can leave a response or trackback to this entry

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