Engagement Analytics for Speech and Text

Select the right speech analytics technology    Watch the Video

The Best Speech Technology: Full Transcription (Large Vocabulary Continuous Speech Recognition)

When you consider acquiring a new speech analytics system, you have two technologies from which to choose: Phonetic systems and Large Vocabulary Continuous Speech Recognition (LVCSR), also known as Speech to Text or Full Transcription.

Aspect® Engagement Analytics uses the superior LVCSR technology, which provides higher speech recognition accuracy, faster searches and a full transcript, among other advantages.

Textual Transcript Screenshot

Best-in-Class Speech Analytics

AEA Screenshot

Aspect Engagement Analytics is a powerful speech and text analytics engine designed for quality analysts, business analysts and other advanced users.

Winner of numerous speech analytics implementation awards and technology innovation awards.

Fastest speed-to-intelligence on the market – customers will enjoy valuable benefits on day one.

Perform analytics across all your channels of customer communication - opening a world of new possibilities for deep insights into your operation.

Organic Trend Discovery

With Aspect Engagement Analytics, you can organically identify valuable information without any prior knowledge using 100% of conversations in all voice and text channels.

The frequency and cluster analysis of any set of calls (which can be depicted graphically as a word cloud) helps you spot and address issues you would otherwise not have been aware of.

Topics and Word Cloud

Aspect Engagement Analytics Features

  • Highly intuitive, web-based interface
  • Span all customer channels with a single query
  • Categorise 100% of customer interactions against KPIs
  • Lightning fast search speed
  • Display results as graphs, reports, visualisations
  • Create customer interactions transcripts
  • Perform sentiment analytics using acoustics
  • Identify trending topics for customers
  • Easily customise and configure searches/reports
  • Automatically redact personal information for PCI
  • Understand differences between channels
  • Drill down into interactions behind each metric
  • Drill down into related concepts behind metrics
  • Understand drivers for customer behaviour
  • Enable manual tagging and categorisations
  • Track agent compliance against guidelines
  • Evaluate interactions based on customer data
  • Interface for building automated scorecard
  • Understand the similarities / differences in customer groups
  • Infinitely scalable
  • Open systems architecture with powerful API
  • Numerous speech analytics awards
  • Extract valuable insights through organic trend discovery
  • Count and trend the occurrence of words and phrases
  • Cluster analysis by grouping similar portions of conversations
  • Create word clouds for easy data visualisation


What is Engagement Analytics for Speech and Text?

Until recently, contact centres have been unable to cost-effectively review customer interactions in large enough quantities to ensure better agent quality for all agents, nor could they easily spot operational issues that affected many agents. Most quality monitoring processes currently review only a small percentage of the total interactions that a company handles on a monthly basis. Such a small sample size can’t properly capture and identify the many quality issues faced by hundreds of agents. It would be cost prohibitive to manually review enough customer interactions to get an accurate view of agent performance.

Speech and text analytics uses modern technology to convert unstructured voice and text data into a structured form that can be analysed using statistical and data manipulation tools. The challenge of converting a speech waveform recording of a customer interaction into usable data has been particularly challenging, but new techniques that examine word phonemes in the context of surrounding words have enabled speech recognition accuracy in the neighborhood of 95%.

This new technology enables contact centres to get a 360-degree view of conversations across channels. Each interaction can be automatically monitored for key phrases that are indicative of important conversational characteristics such as whether a proper greeting was used, agent empathy, politeness, customer dissatisfaction and many others. With that data, the contact centre can easily identify individual agent quality issues as well as operational issues that exist across the contact centre.