Enhancing the Agent Experience through AI and ML: A Model for Business Transformation

Share This
Silhouettes of several people in a three dimensional digital matrix

In the contact center industry, organizations have started to view the customer service workforce as a consumable asset. This becomes a unique challenge when millions of contact center agents go to work every day with the intent to do well in the work that they engage in. Organizations have built large mechanisms to recruit, onboard, and engage a workforce with a massive objective of bringing the brand to life for their customers. The dichotomy of agents with best of intentions in their daily work with the necessarily rapid speed of the enterprise contact center environment results in a disengaged workforce that is required to onboard quickly with very little training. Ultimately, this creates disparate outcomes for customers and the business and a disengaged, disempowered workforce.

So how do we begin to solve for the needs of these large organizations, who require speed, efficiency and cutting-edge performance from every agent to achieve their business objectives and outperform the competition?  

For nearly two decades I’ve been working in the customer service field, and I feel blessed to have worked directly with some of the world’s most respected and admired brands like Disney, Apple, Carnival, AT&T, GE and Walgreens. I’ve seen workforces that show up every day with the true intent to deliver and excel. Conversely, I have also seen these same workforces very quickly find themselves in a cycle where they feel unempowered and ill-equipped to succeed right out of the gate. Many agents in large enterprise environments complete hurried training and immediately begin performing well below the standards for success. In many instances, these agents will quit within the first three months. According to research, “The average turnover rate for a call center is 30-40%, but some centers see numbers as high as 100% in a single year” (Reynolds, 2015).

As you might imagine, quick voluntary turnover is not ideal for the psyche and self-esteem of the person who has engaged in all the work but does not experience a degree of success in their role. The brand now has a combination of all the time, energy, and investment in that individual who then in turn engages with many of the company’s customers and underdelivers – despite their desire to do well.

And this challenge continually manifests itself. Organizations, rather than focusing on the root cause of staff disenfranchisement, instead have built mechanisms of recruiting, hiring, and training efficiency to place a band aid solution on their turnover and attrition challenges. Studies have shown as much as $6,400 is spent to attract, train and retain one new associate, only for them to attrit in a very short window of time (Reynolds, 2015). When they have the courage to view this on an annualized basis, organizations will thus find find themselves, turning over 100% of their workforce.

So what can we do as professional service providers to help agents become more successful in those early days and truly solve the root cause problem of agent engagement and attrition? We know historically that this can be accomplished through mentorship and direct one to one coaching. In the customer service and sales industries an agent’s success takes shape through the ability to spend ample time with associates who are extremely familiar with the levers it takes to succeed.

At the scale that many sizeable organizations are operating at, this level of direct mentorship could not be pragmatic human to human. Thus, one of the real promises of machine learning is that we can use the combination of human and machines to augment people, supporting them to learn and grow to almost total proficiency, right from the beginning of their career – providing every agent with the required mentorship and guidance to quickly succeed in a very complicated environment.

Empowering new agents to not only meet expectations – but exceed them – as they gain proficiency and build their individual style and rapport with customers, expands their horizons of what they are capable of achieving in their role. Performing at a high level almost immediately, they can assist customers with confidence while being their authentic selves. This delivers an exceptional agent experience that solves the challenge of agent retention while exceeding business goals and outcomes.

This is this really exciting frontier. We can start to build a more durable experience for agents in which feel like they are part of the solution, growing in their skillset, and not moving from one company to another on 90 day- to 6-month intervals. They are gaining true expertise and industry understanding while becoming great practitioners in the business.

This idea of durable solutions that can endure over time is the duty of the innovators and leaders of our industry to create that context. The responsibility of all of us as leaders is not to figure out how to move more people through a broken process, but to rethink the process. To truly reimagine a world in which we innovate to improve the human experience for every agent, giving them the early guidance and assistance through machine learning augmentation to succeed from day one.

Works Cited

Reynolds, Penny. “Exploring Call Center Turnover Numbers.” Exploring Call Center Turnover Numbers – Quality Assurance and Training Connection, QATC Publications, 23 Apr. 2015, http://qatc.org/winter-2015-connection/exploring-call-center-turnover-numbers/.

Matt Coughlin
Matt Coughlin
Founder and CEO, XSELL Technologies

Matt is a leader with contagious passion for great customer engagement and the economic and business impacts created when customers are engaged in exactly the right way. Prior to founding XSELL, Matt had the opportunity to work with many quintessential brands helping them to bring their customer experience to life.

His experience in these environments led to his observation of a power law in sales, regardless of the brand. Top performing agents were not just marginally better than their peers – they were multiples better and they delivered disproportionately successful results. This observation drove Matt’s curiosity around the ability to scale the actions, tactics, and strategies of top performers to an entire enterprise. Matt founded XSELL Technologies on the premise that this was not only possible – but that when done exceedingly well, it would radically transform the customer experience.

“80% of your customers are speaking with agents who simply do not know or do not understand how to deliver a great experience. But what if all your customers could speak to your very best agents?”

Connect with Matt on social media: LinkedIn

Be the first to acthearknow

Subscribe to receive the latest call center and machine learning trends right in your inbox.