Why bring cognitive intelligence to call center analytics?

In essence, call center industry has a rich influx of customer data in accordance with today’s fast-paced world showcasing antsy lot of modern age consumers. With the use of predictive analytics, contact centers can pick up agility to accommodate the changes in customer expectations. Based on number of calls, inquires, and other issues relating to customer experience, different approaches can be taken for dissimilar customers to resolve issues, offer customized plans, or other added incentives.



What makes a top-notch customer support experience?

• Customer sentiment analysis- Identify transcripts of support calls and IVR conversations that reflect customer dissatisfaction.

• Classified issue domains - Know which platforms are driving the most inbound calls.

• Average talktime - Measure how much time agents spend talking to prospects and determine average hold time.
• Ticket status notification - Track issues created, resolved and in-progress per domain. Quick focus on issues escalated or transferred.
  
  
• Hourly /Weekly traffic distribution analysis - Broad overview of call activities to gauge performance.
  
  
• Discover top agents - See which reps are fastest-responding, route calls accordingly.
  
  

No matter how efficient a system is, there is always the possibility of clogging, inevitably adding to the time it takes to resolve a customer query. A smart way would be to understand the entire workflow and realign or plan resources for boosting agent effectiveness and improving overall customer satisfaction. This can be achieved by host of cognitive systems such as statistics, data mining and machine learning.