Converseon applies predictive analytics to conversation data

Conversation intelligence platform Converseon has announced the launch of a suite of AI-powered solutions to drive predictive analytics from conversational data. The aim is to predict business outcomes such as sales and evaluate the likely impact of future courses of action based on content in conversational and social streams.

The solutions include:

• Social Brand Reputation Intelligence System: This measures, and predicts the impact of actions on, brand reputation, including environmental, social and governance (ESG) components.
• Social Brand Relevance System: This looks at how well products and services match the needs of consumers in target markets. It monitors not just current needs but predicts future needs and how products can align with them.
• Both solutions incorporate Assess (benchmark against past performance and key competitors), Diagnose and Predict modules.

Why we care. Monitoring website engagement, call center activity and transactional data is not enough in a world where stakeholders can address brands directly — or each other, with reference to brands — in streams of conversational and social engagement that exhibit enormous and increasing volume and velocity.

Using AI to capture positive and negative sentiment at scale, identify developing problems and create opportunities to engage, is in itself nothing new. The intriguing proposition from Converseon is that its tools can build predictive analytics on this analysis.

Read next: Salesforce releases new conversational features for service and marketing clouds

Exponentially growing channels. “Exponentially growing social and related conversational data is a powerful source for predictive insight at a time it’s most needed, but such data all too often drowns brands with too much noise and hindsight insights,” said Rob Key, CEO of Converseon in a release. He considers the ability to connect real time conversation data to business outcomes an important step forward for reputation and brand measurement, social listening and customer intelligence.

About The Author

Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space. He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020. Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.