How do you improve top-line metrics with conversation data? Gridspace Sift reveals what interactions have in common, so you know how to fix problems and create successful journeys.

Gridspace Sift for Pattern Detection
Whether your goal is to decrease returns, enhance customer satisfaction, reduce service cancellations or increase product upsells, you need to know how interaction characteristics lead to success. Gridspace Sift helps your organization analyze large collections of conversational interactions and understand what interactions need to be reworked or removed. Conversation is not just talk, it's data. Gridspace Sift gives your organization the ability to the see patterns that matter most.
Example Applications
  • A popular subscription service wants to understand and avoid service cancellations following a competitor's recent promotion.

  • A regional credit union company wants to encourage its new customer to adopt new financial services during their account set up calls.

  • A bank's collection department wants to drive its collection metrics while maintaining industry-leading customer service.

  • Technology Benefits
    Gridspace Sift similarity gives organizations the power to identify unusual or idiosyncratic interactions that impact key performance metrics. The capability works as a conversation is unfolding or in a batch configuration to help diagnose operational issues. Because Gridspace Sift similarity relies on unsupervised learning techniques, it can offers excellent results from a single example and work without significant pre-training.

    More Resources
  • Similarity in Sift API

  • Bloomberg DataDriven talk 'How Machines Enter the Conversation'

  • Gridspace Real-time Live Demos

  • Forbes 'Deep Learning to Product Actionable'