DataInfoCom worked closely with the Global Consumer Support division of one of the largest high-tech hardware manufacturers in the world, a "Fortune 50" corporation, for several years. The company has tens of thousands of agents - captive and outsourced - operating out of centers in several countries, handling hundreds of thousands of calls every week. Providing phone-based technical support for different varieties of high-tech hardware products is an enormous cost center. Not recognizing any trends or inefficiencies can cost the company millions in wasted effort and result in a wave of customer dissatisfaction.
 
Most call centers try to measure dozens of metrics in an attempt to identify problems before they get out of hand. For the company in discussion, with thousands of calls arriving every hour, each call with a variety of subtle differences, it is almost impossible for the managers to understand all the pertinent data, let alone take decisions on the data. The calls are coming in so quick, with so many different facets - by the time the managers spot a trend, it has already become a big problem.
 
The company had multiple initiatives in place - all well-intentioned, primarily with the mandates to improve customer satisfaction and/or reduce cost - but, these programs operated in their own bubbles, without the holistic understanding of how the outcome of each initiative affects everything else.
 
All these led to unhappy customers, unhappy agents, and cost overrun.
 
DataInfoCom looked at all the metrics the company was tracking and grouped them into 4 major categories: Satisfaction & Loyalty, Resolution, Productivity, and Cost & Revenue. DataInfoCom worked closely with the customer to identify the key metrics for each category. Using its patent-pending technologies and all the datasets in customer's possession (plus, other useful datasets such as environmental data, benchmark data, etc.), DataInfoCom conducted the following steps.

During the initial stages of the relationship, the company wanted to be cautious and stopped the process after step 1 and waited for a month to verify the accuracy of DataInfoCom's predictions. After a month, almost all the monthly predictions came in within a few percent of the actual values.
 
The company, upon gaining confidence on the capabilities of DataInfoCom's technologies, followed through the next steps and also implemented the optimized recommendations from step 3.
 
In collaboration with DataInfoCom, the company started driving its key metrics, looking forward. This was a 180 degree turnaround from how the company used to do things, i.e., analysis/paralysis of past events, a reactive approach that just wasn't working. Over 2-3 months, the metrics in each of the 4 categories started improving, especially the metrics in the areas of customer satisfaction and cost.