As a technology company nGUVU is known to be in the space of contact center gamification and employee engagement, but to our customers, we are also known for our machine learning capabilities.
"Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed." Or, in plain English, automated algorithms that work in tandem to optimize or solve a particular type of challenge.
One might ask how machine learning applies to the reality of a call center, customer care team or sales gamification? The answer covers multiple dimensions: risk estimation and insights, performance/KPI analytics, relationship networks and correlation impact between various performance indicators in your company and more. Machine learning and gamification begin to shine together for large teams and enterprises. Once you're past the threshold of a couple of hundred call center agents, sales executives, or customer care reps., this is where manual tracking, analytics, and risk estimation becomes troublesome. At this point, machine learning can take over and help your management team by providing real-time monitoring and analysis of everything relevant to your company.
A few examples of merging machine learning and gamification technology for contact centers:
1. Workforce Attrition. Consider a case with a loss of an employee - to hire and train a call center agent costs could vary from $6000 to $10000. A call center with 500 employees could potentially lose 5% of a workforce on a monthly basis - this would translate into an expense of $150000 to $250000. What if a portion of this cost can be predicted or even better averted with the help of the machine learning?
In case of nGUVU, our machine learning algorithms connected to nGUVU gamification platform can track signal across all of your call center agents. Looking at absenteeism, engagement levels, company-specific KPIs, team engagement, performance variable correlations and many other factors. Our machine learning can gauge what the risks of losing each employee are and what's overall employee attrition risks. This way managers can attempt to engage with the portion of the workforce that's at risk or at least be aware of a potential loss of the team.
2. Cross-correlating performance indicators, KPI funneling and dynamic goals. How many KPIs does your contact center track, do you have any additional performance variables? It's now uncommon to have a dozen or so KPI's and multiple company-specific performance tracking variables. Now raise that rough number of indicators and variables and spread it across few hundred or a couple thousand of call center agents and it becomes a challenge, but in a world of machine learning it's an opportunity.
Algorithms can look at a chain or a funnel of KPIs and, or, other performance variables. Analysing how agents are performing in real-time and telling who's underperforming, who's at risk, or for some reason doesn't care anymore. Whether it's a chain of events - if A followed by B then C or, a group prediction X KPIs are failing and Y number of performance variables at a minimum - machine learning can run day and night tracking and analyzing based on a company-specific model.
Not every example has to be negative, consider performance-related opportunities. When machine learning can advise on what are optimal challenge levels that you can give to your agents on an individual level. In this case, it's all about seizing the moment and using gamification environment to challenge and motivate selectively. The goal is to be flexible and provide effective challenge advice to managers that would be dynamic on a per agent level.
3. Learning and education challenges. When gamification is closely tied in with learning and survey capabilities, it provides an interesting ground for education-related analytics and advice. It becomes easy to conduct knowledge test for the contact center or sales workforce, but survey or test completion presents hidden opportunities. Algorithms can tell you what the areas where your team is making mistakes or requires additional training are. What are complementary courses or materials that agent(s) need to look into or better? How and if multiple test completions are highlighting any educational gaps?
The idea is simple - once the machine learning algorithms are set to track and optimise they will do so relentlessly, and while doing this they will get better at problem identification and advice.
Above three areas/examples are just a few cases of application of machine learning within call center environment while using a gamification platform. In the case with nGUVU and our platform, we were able to tie together gamification, employee engagement, and machine learning together.