Hotline Consultant

PEOPLE ENGAGEMENT AI

Predicting behaviors by analyzing operations

 

PREDICT AGENT ATTRITION

Find which agents are thinking of leaving

Working in Contact Centers can be very demanding and unsatisfied and frustrated agents start changing their behavior at work even before they realize they want to move on. Applango’s Artificial Intelligence looks at changes in hundreds of behavioral parameters for each agent and predict who will resign in the near future.

 Young Woman Contemplating
 
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SERIAL CALLERS

Frustrated customers are clogging your contact center

Call Centers activities tend to be repetitive. Thousands, sometimes millions of customers, experience the same kind of disruptions and turn to a call center expecting a positive outcome. Some of those customers will escalate their problems to undesirable levels which could end in repeated calls, churn and lawsuits.

Applango's Artificial Intelligence constantly collects and analyzes hundreds of data points from daily operations and, on the base of previous outcomes, predicts which customers are most likely to become frustrated in the next days.  

To avoid critical situations agents need to take care of customers before their issues are escalated to critical level. Applango predictions prioritize call center activities in their effort to avoid such critical escalations.

 

ARE YOUR DEVELOPERS HAPPY?

Advance warning on developer attrition

Tech companies specific AI learns the meaning of dramatic changes in developers' engagement levels.


Supporting managers’ decision process by predicting behaviors weeks in advance.


High precision predictions enable preemptive actions to avoid crises.

Based on existing logs from:

  • Code repositories - Jira, Github,...

  • HR: Successfactors, attendance logs

  • ...other available logs

At Work
 

HOW IT WORKS

The solution is in your data

Applango AI analyzes hundreds of KPIs and activities to find hidden patterns in everyday operations.
Our research-grade AI finds correlations even where signals are very weak and provides valuable insight into what is going to happen next.
Our data source is in the activity logs of professional software applications, from which we reconstruct the history of events that characterize mainstream activities in detail.