Big Data Analytics – What Contact Centers Can Learn from the Tampa Bay Rays
Originally published on Customer Magazine
Big Data has been with us for years, and across its many forms, is used more widely than most of us realize. As the analytics derived from Big Data play a growing role in decision-making, expectations become higher for the accuracy and completeness of the underlying data. Not only must organizations capture data from an ever-expanding range of sources, but they must also manage it in ways to glean insights to support business decisions.
When utilized effectively, Big Data provides substantive business value, but analytics is a complex discipline, and some businesses use it better than others. The contact center is a prime example where Big Data analytics has become very strategic, not just to improve operations, but to drive CX – customer experience. While interactions around both of these generate massive streams of data, contact centers have had mixed results – mainly underwhelming – in harnessing that data to achieve the desired outcomes and to justify the ROI for Big Data technologies.
Focusing on CX Means Thinking Differently
There are many reasons why results have been mixed, but the technology itself is not really the problem. A key factor pertains to how contact center leaders think about their challenges and their understanding of the ways that Big Data analytics can address them. To some extent, this is a product of applying legacy thinking to the types of data being collected and how the outputs are applied.
The conventional approach is to compile data on operational metrics, with the focus on measuring the efficiency of the contact center and the performance of agents when interacting with customers. To support that, the vendors provide a mature set of reporting templates with extensive descriptive metrics tailored for contact center leaders.
This approach works perfectly well if these agent-centric operational metrics define the universe for what’s needed to assess contact center performance. It also works well if, in fact, this was the only thing management was interested in regarding the contact center. There was a time when this was all true, but it’s certainly not the case in 2020.
In today’s digital, global economy, competition is relentless with far more options to buy, as well as consume your offerings. For today’s consumer, value is defined as much by the experience around the offering as the offering itself, and it’s never been harder to understand their needs and wants. Most contact centers remain tethered to legacy technology, and it’s not hard to see why CSAT scores, in general, continue to decline.
The solution, however, is right in front of us, and the keyword here is digital. Consumers across all verticals live in an increasingly digital world, and that’s actually good news for contact centers. As the customer journey becomes more digital, more touchpoints become enabled and connected, from which rich streams of data arise to provide clues about their needs and wants.
Thinking Beyond the Contact Center
Contact centers that remain agent-centric will miss all this, and their reporting tools will only help fine-tune internal operations. Their field of vision must become wider, not just in terms of becoming CX-centric, but also for utilizing data sets from across the entire organization to effectively support CX.
The starting point is shifting to a CX-centric model, and to recognize the value of data both inside and outside the contact center. There are plenty of technologies in place today to capture all these forms of data, and this is where contact centers need to think differently.
Not only must contact center leaders think about tapping a broader set of data, but they must also know how to use it. This is where the difference between reporting and analytics is so important, and why Big Data is central to improved decision-making. Conventional contact center reporting is well-entrenched, but it only provides internal visibility and focuses more on effect than cause. Analytics goes further by identifying the root cause, but contact center reporting is largely internal, so root cause analysis here is rather limited in scope.
Big Data analytics represents a more holistic approach that draws data from across the organization as well as from customer touchpoints. Adopting this broader vision puts contact centers on the right road to improving CX, but most of them still have a long way to go. As noted earlier, some companies deploy analytics better than others, and to illustrate why contact centers need to get better here, consider an unlikely analogy from the world of baseball.
How the Tampa Bay Rays Win with Analytics
While it’s no surprise that the LA Dodgers won the World Series a few weeks ago, few expected the Tampa Bay Rays to be there. Aside from a few poor managerial decisions, the Rays were worthy opponents, but in the end, the more talented team prevailed. In the early 2000s, Billy Beane famously pioneered an analytics-driven approach to baseball – Moneyball – and while radically different from conventional approaches to the game, small-market teams like Tampa Bay have embraced analytics with at times, wild success.
Baseball purists are quick to dismiss Moneyball, but in recent seasons, the Rays have been out-performing big-market rivals like the New York Yankees and Boston Red Sox with vastly higher payrolls, but little to show for it. Tampa Bay may have fallen short in 2020, but their ROI to owners and fans is far greater than these perennial big spenders.
So, what does analytics have to do with this, and what can contact centers learn from the Rays? As noted earlier, there’s a big difference between reporting and analytics, and it applies equally well in baseball. The KPIs we all grew up with are still with us, and no sport is more attached to both tradition and metrics than baseball. Position players are evaluated on simple metrics such as batting average, hits, walks, home runs, RBIs, etc. Pitchers as well continue to be rated on metrics that have been in place since the game was invented – wins, strikeouts, innings pitched, ERA, etc.
With today’s technology, a vastly wider range of metrics can be captured, and computer-driven analytics has given rise to an entirely new class of KPIs that we didn’t have or even think of in analog times. Given the massive amount of data generated from a sport with such a long season and so many touchpoints, these new metrics (also called sabermetrics) are more sophisticated, yielding subtle insights about performance that traditional reporting-oriented metrics cannot reveal.
Analytics-driven metrics such as on-base percentage (OBP), slugging percentage (SLP), or wins above replacement (WAR) may sound obtuse to casual fans, but in a game where the smallest difference can make the difference between hits and outs or wins and losses, these are the KPIs that teams like the Rays use to get an edge that money can’t buy.
If this sounds similar to what’s happening in the contact center space, you’re right. When a team relying on conventional metrics and gut feel goes up against a team steeped in analytics, the latter is going to win more than they lose, and over time, that little difference adds up. Conventional contact centers that rely on reporting to drive CX will lose more than they win today for the same reason. Think of KPIs like AHT, FCR, on-hold time, cost per call, etc. as you would RBIs, HRs, BA, etc.
These are good, reliable metrics, but will be no match for OBP, SLG, WAR, etc. The key point here is that customers steeped in digital technologies are using different tools – and better tools when it comes to shaping CX. In this environment, analytics will win out over reporting more often than not, and with that comes declining CSAT scores and everything else along with it.
This brings us to the next question – how can contact centers perform more like the Rays and less like the Yankees? Thinking in terms of analytics instead of reporting is the first step. The next step is to identify the data sources to support these analytics. This is where going beyond the contact center is so critical. All contact center vendors have robust reporting capabilities as well as for analytics modules, but that doesn’t tell the full story.
Taking a Holistic View with Analytics
CRM integrations provide essential customer-facing data sets, but there’s also a need for organization-wide data sets. A great CX means that all customer needs can be addressed from one interface and one point of contact from the organization. Delivering that requires integrating across these sources and data sets, and when it’s transparent to the customer, CX can be magical.
Getting to that point, however, requires more than just thinking differently about CX and the value of analytics. Not only do contact centers need to draw from data outside their domain to improve CX, but with CX having such strategic importance, internal business leaders from outside the contact center have other needs that these data sets can also address. Standard contact center reporting simply cannot support all this.
Clearly, the stakes are higher now with Big Data analytics, and there’s a missing piece in my view that contact centers need to recognize. Data is often kept in silos, and organizations face many challenges to break them down and make data more accessible. The task of doing this falls largely into the domain of BI – business intelligence – where complex platforms have evolved to make organizational data useful for decision-making.
The sphere of BI is very broad, and while only some elements are pertinent to the contact center, they are critical pieces for a holistic approach to CX. Key examples would be CRM, HR, QA, Sales, Marketing, etc. As with reporting from contact center vendors, analytics platforms from BI vendors are well-established, but prior to becoming CX-centric, organizations have not had much need to tap BI for contact center needs. Even if they did, the platforms are very different with no easy way to bridge them.
Comparing Contact Center and Business Intelligence Platforms
In a perfect world, analytics for the contact center would blend seamlessly with relevant BI data sets, but the current state appears to be rather imperfect. In preparation for this article, I have reviewed analytics platforms from 10 leading contact center vendors and 12 leading BI vendors. While not exhaustive research, I see a consistent pattern in both cases.
Among contact center vendors, the platforms are narrow in focus but deep. The analytics capabilities are quite rich, but largely contact center-centric. The familiar KPIs are all there, providing the core insights needed for operational efficiency and tracking agent performance. A few vendors specifically address BI integrations, but overall, these worlds seem pretty separate.
BI vendors, by nature, have the opposite orientation – wide in focus and not as deep. These platforms cover a broad swath, but with extensive segmentation to optimize the value of their analytics. Vertical markets would be one major vector, typically including healthcare, finance, public sector, education, retail, media, tech, etc. Given the complexities of each vertical, this focus is to be expected.
Another important vector is by function within the enterprise; IT, security, DevOps, infrastructure monitoring, supply chain, operations, finance, sale, marketing, HR, etc. Again, each has specific BI needs, so this vector also makes sense. All of this segmentation has utility, but there was limited mention anywhere of a contact center or CX as a specific application for BI. Just as contact center vendors generally have limited integration with BI, the same appears to hold here going the other way.
Eventus – Bringing These Worlds Together
This isn’t to say there isn’t a seamless platform that brings both of these together, but I haven’t come across one yet that’s firmly based in either space. In my view, that signals a gap in the market, and a prime opportunity to help contact centers follow the example set by the Tampa Bay Rays. There is clearly a need for contact centers to take reporting to another level, and this is where Big Data analytics comes into play, especially when all the relevant data sources can be integrated with a focus on CX.
During the course of my research, I have come across one company with this focus, and not surprisingly, they are not a contact center vendor, and nor are they a BI vendor. The company is Eventus, and as a consultancy with deep roots in the contact center space, they recognized this gap, and out of what they viewed as a necessity, have developed their own platform. Over the course of several interviews and briefings, I have come to understand the distinct challenges in addressing this gap.
There certainly are complexities here that are beyond the scope of this article, and for that alone, I can see why BI or contact center vendors have not done this already. That said, the business value of doing this is clear to me, and for progressive contact centers aspiring to be like the Rays, giving Eventus a tryout could be a winning move. There may well be others with a similar platform, but I haven’t come across them yet. If you’re out there, I’d love to hear from you, in which case I’ll be back with another analysis soon.
Do you know how to improve your call center customer service – or if you even need to? Customer expectations have changed dramatically in the last decade. Your first step to improving customer service is understanding what your customers actually expect.
In today’s fast-paced world of on-demand services, entertainment, and products, customers’ expectations have risen in regard to almost every aspect of a company, including the customer experience. All too often, help desk agents, whether in contact centers or manning online chat sessions, become the target of customers who are frustrated when their products don’t work, when their billing is incorrect, when they regret a purchase and demand a return or a refund, and more.