June 17, 2014
Transera Customer Engagement Analytics Solution Optimizes Contact Center Operations
By Shamila Janakiraman
Transera (News - Alert), a provider of cloud-based customer engagement analytics solutions, has unveiled a statistical performance analytics solution for contact centers. This practical, scalable solution helps unlock business value from contact center agent interactions and customer data collected by enterprises.
This solution will predict future performance of contact center agents and how customer needs and preferences will change with time, based on historical data on agent and customer activity. By matching the data, Transera will be able to link the best available agent to produce the desired business outcome.
The historical data also provides a means to prioritize customers based upon their needs. Depending on an agent’s capabilities, he or she is assigned to a particular customer to achieve results whether it is for customer conversions, retention or for ensuring satisfaction.
An algorithmic scoring model is used to judge the best agent match. This method combines individual agent and the total agent population in addition to past performance data to decipher their future performance. The algorithmic score also considers agent churn, new agent ramp time and the abilities of each agent.
True analytics-driven customer engagement is achieved in four stages. In the first information stage, data collected by different contact centers are placed in a cloud-based customer engagement repository. This includes a contact center system-aware universal data dictionary which leverages Big Data, Hadoop and NoSQL technologies.
In order to derive insights, in the second stage a customer engagement analyzer is used to segment, profile and visualize the data in the customer engagement repository to detect performance affecting correlations, trends and patterns. In the third intelligence stage, predictive models, simulation engines, machine learning, the R analytics language and analytics techniques are used by the Transera data science team to predict customer propensities and agent performance.
To conclude the process in the fourth stage, the Transera data science team will offer its recommendations that can help contact centers to improve agent behaviors which will automatically help enhance business outcomes.
Edited by Rory J. Thompson