Re:infer’s Deep Learning platform converts unstructured communications (emails, calls, chats, CRM notes) into structured data in real-time allowing for analytics and automation in the wider IT real estate.
In Operations / Shared Services, re:infer identifies and quantifies manual processes that are mostly transacted in email and tickets. Re:infer’s ability to extract the root-cause drivers of demand enables clients to see which processes cause inefficiency and failure demand across entire operations functions. By mapping every conversation to structured data, re:infer enables automated actions (STP) can take place downstream in RPA, Workflow, Case Management, CRM with no human in the loop.
In the Front Office / Customer Facing Channels, re:infer is used to identify market information, client intents, sentiments, trends and hidden relationships that translate into actionable growth opportunities for front-office teams. The ability to distill the customer journey into a series of episode maps across diverse communications channels (chat, email, calls, reviews, social) closes the feedback loop. Re:infer’s structured data is used in Marketing Automation, Single Customer View Databases, CRM automation and Data Lakes where a join is made with sales data to increase LTV and reduce churn.