Transera Releases Customer Engagement Analyzer 2.0



Transera, a cloud-based customer engagement analytics provider, has released Customer Engagement Analyzer 2.0, the latest version its SaaS offering that helps contact centers capture, organize and cross-analyze customer interactions. Customer Engagement Analyzer 2.0 equips contact centers with enhanced interactive and collaborative analytics capabilities to perform business performance analytics on cross-system customer interaction and agent activity data.

Transera’s Analyzer gives organizations a complete view of customer engagement—no matter what operational system or channel through which interactions occurred—so they can optimize for better business outcomes and customer experiences.

Contact center, sales and marketing professionals can use information from Transera Customer Engagement Analyzer for a variety of business purposes, such as identifying the best answering resources, evaluating marketing campaign performance, determining next best actions and offers, prioritizing customers, and comparing sales and customer satisfaction target metrics to actual metrics.

Transera Customer Engagement Analyzer brings data together in the cloud from automated call distributor (ACD) applications, interactive voice response (IVR) systems, customer relationship management (CRM) applications, order entry applications and other customer data sources such as demographic services so that contact centers can analyze, understand, manage and automate customer interactions in new ways.

The Customer Engagement Analyzer 2.0 enhancements fall into four key areas:

Cross-system Analytics

  • Agent Activity Records created from multiple systems are now included for analysis along with customer interaction records.

Interactive Analytics

  • Interactively segmenting and profiling agent activities as well as customer interactions by any variable.
  • Pivoting across different segmentation variables to understand correlations between them.
  • Heat Map visualizations that pinpoint data outliers.
  • Integrated table and chart visualizations for drilling into the data driving graphical trends.

Collaborative Analytics

  • Creating, saving and publishing analytic data sets for analysis by others.
  • Saving segment and profiling filters and visualization defaults for use by peers and team members.
  • Creating standard business-oriented, cross-system Key Performance Indicators (KPIs) and publishing them to a library for use by others.

Productivity

  • Saving results from past analytic steps for retracing and sharing.
  • Feedback on data set size resulting from a proposed query to determine its viability and time requirements before execution.