Adobe’s new tool can turn client conversations into actionable insights

Adobe has announced new voice analytics capabilities in its Analytics Cloud to help brands deliver more personalized customer experiences

Adobe voice analytics

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A tech savvy humanBOT, Sharmistha is a professional writer

The rise of voice-enabled devices and digital assistants like Alexa, Siri, Cortana and several others have introduced new interactive forms of communication that have taken people by awe. In the same way mobile devices fundamentally changed the ways consumers engage with each other, voice interfaces have the potential to do the same.

In a move to enhance voice-driven experiences, Adobe has announced new voice analytics capabilities in its Analytics Cloud to help brands deliver more personalized customer experiences and create brand loyalty through voice-based interfaces.

Through deep analysis of voice data complemented by artificial intelligence and machine learning capabilities in Adobe Sensei, the company aims to provide brands with audience insights and recommendations, while automating the traditionally cumbersome, manual analysis. This will in turn allow brands to take action on these insights more quickly, delivering experiences that delight customers while informing other touch points such as email and advertising.

“One of the most important trends in modern technology is how quickly consumers adopt new ways of interacting with content, as we’ve seen with mobile and video,” said Bill Ingram, Vice President, Adobe Analytics Cloud.

“We expect a similar trajectory with voice enabled devices. In the same way Adobe has shaped web, mobile and customer analytics, Adobe Analytics Cloud will enable brands of all sizes to extend voice data insights across the entire customer journey,” Ingram added.

Adobe explains that integration of voice analytics data with the its Marketing Cloud and Advertising Cloud ensures that every digital interaction a consumer has is a continuous and relevant one. For instance, Adobe Target ensures that insights from a voice-enabled device can be automatically leveraged on other channels, while also delivering personalized responses to user queries using machine learning and predictive algorithms.

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