Let's get right to the point: contact centers operate on lean budgets and profit margins depend largely on operational efficiency. This universally-accepted reality is what drives the focus on measuring and improving work output.
In turn, optimizing work output typically means sacrificing employee satisfaction for profits - at least superficially.
This leads to 2 potential outcomes, as a direct result:
- Low employee engagement (a.k.a.: motivation, presenteeism)
- High employee turnover (a.k.a.: attrition)
Both of which lead to lower profits, as a direct result.
The solution - behavioral & predictive analysis
Behavioral analysis: Applied Behavior Analysis is the process of systematically applying interventions based upon the principles of learning theory to improve socially significant behaviors to a meaningful degree, and to demonstrate that the interventions employed are responsible for the improvement in behavior
Predictive analysis: Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.
I am pretty sure I know what you are thinking: the above sounds somewhat Orwellian. However, what those of us adhering to the principles of data analysis are really striving for is simple: information that will assist management in increasing employee satisfaction.
Use Case: Based on reports from the nGUVU contact center solution - or for that matter IBM/Watson - customer service - a contact center manager finds out when specific members of his team are performing at their best.
With that information at hand, the manager adjusts the schedule to optimize peak performance times, using downtime to initiate ongoing training, competitions, or other activities to augment agent knowledge, build team collaboration and offer opportunities for career development.
Decreasing employee turnover
Use Case: Using information gathered from the nGAGEMENT application, the contact center manager has determined that an agent is showing signs of disengagement and detachment from work; increased absenteeism, lower performance scores, and several other contributing factors being combined and analyzed with machine learning-based algorithms.
Instead of losing that employee and being blindsided, a perfect opportunity is presented to take remedial action - not on the employee, but with the work environment; several studies have repeatedly demonstrated the high cost of attrition vs. retaining talent.
What's next for Data Science?
As with virtually every other Workforce Optimization application dedicated to resolving the issues above, nGUVU is increasing its investment in Data Science talent, and will soon be announcing a collaboration with one of North America's leading data science research organizations (stay tuned).
Do we still care about Gamification? Absolutely, as it is the most effective way to engage end-users - contact center agents, sales, support, managers and supervisors. But without the science to back up the interface, there is much less of an impact on hard data such as operational efficiency, profitability, and sustained growth in revenues.