The 2023 NBA season has come to a spectacular finish with the Denver Nuggets claiming their well-deserved championship after just 47 SEASONS. While the Finals were entertaining it was the entire post season saga that got me excited as I’ve had the pleasure of watching, analyzing, discussing, and debating basketball with one of my sons Kaushal Bobba. Aside from the post-game banter on bad officiating and the constant generational debate of who’s the GOAT, MJ or Bron, we (mostly Kaushal) really dug into the remarkable transformation that the NBA has undergone in the past decade—because of advanced analytics and AI. It was a watershed moment for me as a proud father who happens to be an engineer.
Over the course of several discussions, we began unraveling how the NBA has changed and we were able to boil it down to three key areas: scoring, talent selection, and player matchups. Adam Silver, the NBA Commissioner said, “analytics are a part and parcel of virtually everything we do now.” Surprisingly there are strong parallels to what’s happening today in contact centers.
Scoring methods have changed to maximize returns
During the 2015 NBA finals, the ABC network compared the amount of three-pointers made during the entirety of the 1980 NBA playoffs (the first year of the three-point line) to Steph Curry’s cumulative three-pointers made during the 2015 playoffs. Curry, alone, hit 91 three pointers, in that single playoff run. This was one more than the total number of three-pointers made by all the teams during the entire 1980 playoffs.
Over a decade ago NBA teams started digging into the data and applying advanced modeling techniques to reveal that shooting long two pointers were inefficient compared to moving back a couple of feet and shooting a three pointer. The consensus was that there was a 50% return gained from the additional point at the three-point line makes the long-range attempt worth. It’s even common to see power forwards and centers shoot from beyond the arc in today’s game.
In the contact center…
According to Forrester Research, customer service is consistently a top driver for customer experience in their annual CX Index benchmarks. Like how scoring methods changed in the NBA, contact center leaders are increasingly emphasizing experiential metrics like first contact resolution (FCR) and customer effort over that of operational measures like average handle time (AHT) and call deflection. Because these serve as a better measure on the quality of the service interaction which invariably impacts customer retention and loyalty.
At the same time a growing number of agents that tag team with AI (using agent-assist technologies like Minerva CQ) are seeing fundamental gains in both experiential and operational performance without sacrificing gains over one metric vs the other. As such contact center leaders have a new lens to view correlations and derive greater insights to improving interactions. This becomes vital as more contact centers try to shift from a cost to a profit center as service and sales moments increasingly blend together.
Talent selection is modeled around how to win as a team
The process of player selection by NBA teams is undergoing a transformation, blending the art of human intuition with the power of analytics peppered with AI. From meticulous scouting to identifying undervalued talents, analytics is emerging as a crucial tool in enhancing teams' chances of winning. It’s the equivalent of Moneyball but for basketball, and 10 years ago it was just not there.
Nikola Jokic is one of the best players in the NBA and this year's Finals MVP. He makes everyone around him better. In 2014, Jokic was drafted in the 2nd round with the 41st pick. A scouting report labeled him as “an average athlete lacking great speed and leaping ability.” Jokic had a 52.9 combined average of points, rebounds and assists per game in the playoffs, second only to Wilt Chamberlain in 1967. Jokic shot over 58% from the field in the finals. That is insane!
Today’s advanced analytics is enabling teams to flatten, calculate, and analyze a diverse range of inputs. For instance, being able to assess whether a defensive player effectively denies passing lanes and maintains optimal defensive positions. Furthermore, they generate a comprehensive score that quantifies the level of ball pressure exerted by each defensive player on their offensive counterparts throughout the game.
These seemingly small yet critical details serve as invaluable insights in the process of selecting the right players, whether through free agency or drafts. By integrating advanced analytics and AI into their decision-making, teams gain a deeper understanding of player capabilities and contributions. This comprehensive approach enhances their ability to make informed choices and assemble a roster that maximizes their success.
In the contact center…
It’s important that companies hire the right agents with right skills to create the right team. Soft skills such as empathy should be assessed alongside traditional skills. As agent assist becomes more prominent in contact centers there will be less need for the traditional agent skillsets like navigating apps, looking up information, etc and more emphasis on empathy, passion, and instilling confidence to underpin brand moments. Programs and tools that focus on agent satisfaction (ASAT) are becoming more popular as agent attrition continues to be a major problem and with the rise of WFH agents it’s becoming harder for contact centers to establish a cohesive team culture. At Minerva CQ we’ve already started applying advanced analytics and AI to training and agent wellness management and it’s only a matter of time before it is used in recruitment.
Match ups are optimized at the individual level
In the last decade, the NBA has installed multiple cameras around every court that tracks data on player movements, patterns, and shots through data visualization tools and video tracking using machine learning and AI to analyze players’ on-court movements in the cloud. This data is then compiled by the teams’ data analysts and then used by the coaching staff to pick up on player tendencies, making it easier for the coaching staff to create accurate scouting reports on the opposing teams. Teams have even begun collecting sophisticated data points about their players through wearables, sleep monitors, and even saliva samples to assess their fatigue level and predict their performance going forward.
Forcing players to their weak hand, shading off a shooter who isn't as efficient in the right corner as he is on the wing, and knowing to short roll on a big that prefers to play in drop coverage in a pick and roll are all examples of tendencies that analytics pick up on. Knowing these tendencies assist the coaches in placing their players on the court with the best matchup to maximize their chances of success. Pinpointing these advantages in matchups before the game gives teams that small edge that can make all the difference in winning.
In the contact center…
In the 2000s we started seeing skills-based routing in customer service. Now in the age of AI we’re seeing ML-based routing that best matches the customer’s persona with a similar agent persona and skills for better compatibility which would then drive better interactions and outcomes. We’re still in early adoption but these algorithms will most likely be embedded on top of cloud-based ACDs, aka CCaaS, as a standard feature in coming years. Where it will get more exciting is when this AI-based matching is applied to more channels when companies actually pull off omnichannel successfully with omnichannel agents. The call transcripts that we use in the Minerva CQ platform provide a unique perspective into the actual mechanics of the conversation as we measure task completion along with sentiment throughout the different phases of the interaction. These insights can be used to bring further individual agent level data to improve ML-based routing.
There's a tremendous amount of innovation happening due to advanced analytics and AI in all walks of life and the contact center is no different. I have heard industry pundits prognosticate massive agent displacement from generative AI like ChatGPT. I tend to see the latest enhancements in AI as an opportunity for collaborative intelligence where agents actually collaborate with AI to make their jobs easier and customers happier.
Thank you to my co-author and partner in crime, Kaushal Bobba, for your tremendous insights, passion and courage.