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On July 29, alumni from VCU School of Business Master of Decision Analytics Professional Track (DAPT) kicked off their four-part virtual seminar series on “COVID-19 and Analytics” and welcomed Greg Bucko, vice president of Data Analytics for Marriott Vacations Worldwide.  

Matt Leary, a 2018 DAPT graduate and data scientist with Markel Corporation, moderated the Zoom event. Steve Custer, director, VCU DAPT, introduced Bucko and fondly recalled that he was the member of the inaugural Decision Analytics master’s degree class (2014-2016) selected by his peers to speak at their graduation.  

“We were one of the first programs in the country,” Custer said. “It was more of a partnership than a traditional faculty-student relationship.”  

Bucko described how he built a successful data and analytics team and how his group transitioned to provide valuable insights in the midst of a global health crisis. These highlights have been condensed and edited for clarity.

Five key ingredients to a high-functioning analytics team.
There are five key ingredients to building a high-functioning analytics team in any industry:

  1. Establish your brand
  2. Develop relationships built on trust.
  3. Have a clear strategy
  4. Develop relationships built on trust.
  5. Assemble a dynamic, multi-dimensional team

Establish your brand: To build a high-performance analytics team, you first must establish your brand. This can be an abstract concept to a lot of quantitative leaders who find the “touchy feely stuff” hard. But if all we do is focus on the science, data, analysis and algorithms, but don’t clearly communicate our purpose and value, we can’t complain later than that our findings are falling on deaf ears or that no one is taking our work seriously.

We realized early on that we were not the only data and analytics team in the company. Almost every department had a group with those words in their title, but they were mostly shadow IT teams created by vice presidents to build reports to serve their departments.

We differentiated ourselves from those teams and defined ourselves as the group that does not build reports, but instead uses data to find insights to support better business decisions.

Develop relationships built on trust: Next, I prioritized developing relationships built on trust. Leaders in the organization didn’t know us enough to establish trust in us. Having a concise brand identity that I could articulate was critical. We were a brand-new team and they had no evidence that we were any good at analysis or building models. Having a clearly defined vision in those early executive meetings bought us just enough trust to give us a shot.

Have a clear strategy: Once we had a clear identity and some brand equity to spend, it was time to start getting down to business. But before we could really get serious about building models, calculating lifetime value or creating segmentation solutions, we had to develop a strategy and then hire, budget and plan for it. We had a wide variety of skills on the team – traditional analysts, data engineers and data visualization skills. I was able to hire the first internal data scientist in the company’s history. So we had the pieces.

As for resources, we had a substantial budget, but much of it covered third-party contracts and legacy technologies. I told senior executives, “I don’t need all of this money.” I explained that we could replace the legacy technology with lower-cost, and even open-source, software and described how our multi-disciplinary analytics team was going to make at least some of those third-party contracts obsolete. Giving back some budget gave our team another boost of trust and bought us time to start showing meaningful results.

Put a process (like Agile) in place: It was clear we would need to adopt Agile practices into our analytics workflow. Most people apply Agile to project management, but I’m convinced that it’s just as powerful for analytics teams.

We told internal clients that we were not just going to take questions and requests and come back in weeks with an “a-ha” PowerPoint presentation. Instead, we intended to be fully transparent and highly collaborative during the entire analysis process. We started sharing bite-sized results every few days. By doing that, we stayed focused on understanding what was important to the business, not just what we thought was important.

It was in the Agile analytics process that we really began to gain our reputation. Our work began to speak for us, and our team began receiving positive feedback that kept us motivated.

Build a diverse multi-dimensional analytics team: The final critical ingredient was becoming a diverse, multi-dimensional analytics team.

We hired individuals who had strong quantitative backgrounds but also brought in those who did not have traditional computer science and statistics backgrounds. We hired creative thinkers with a penchant for numbers, as well as communicators and collaborators. These non-traditional thinkers understood our complex industry and helped us engage with internal clients in new ways.

The hospitality industry responds to COVID-19
On March 12, 2020, Disney decided to close their parks indefinitely. That day, the hospitality industry realized that this threat was very real, here to stay, and likely to impact our industry in immeasurable ways.

We realized our data and analytics team could serve a unique role. In the face of profound uncertainty, we could deliver answers. It wasn’t just our job to provide insights. It was our duty to provide clear and concise information to our colleagues.

Fortunately, people knew who we were and what we had to offer. We had cultivated relationships that could help us secure data – the most precious raw material for our team. We had also demonstrated how our team could complement other data teams, so we were seen as less threatening and more valuable.

Although we had to throw out some of our strategy, our Agile process meant that we were able to be flexible, adaptable and fast. But what most positioned us for success was our creative, multi-disciplined team. We relied on all of our various skills and backgrounds to squeeze the most insights out of the data that we had available.

COVID-19 Data and Analytics Strategy: We quickly formulated a new strategy to create new first-party data sources in the form of primary market research and combine that with secondary sources. Our agile mindset allowed us to chase down a variety of perspectives and data sets – everything from digital behavior and social media comments to macroeconomic measures and reservation and cancellation patterns. First-party data, third party data, internal data, external data – all of it was fair game in this pursuit of insights.

We built a dashboard that showed new COVID-19 cases, death rates and testing. But we layered on top of that data from Kaiser Family Foundation that tracks each states’ response and how strict their quarantines and travel advisories are. We layered on top of that a view of where our customers live, their preferred properties and how many already canceled.

We also saw how many were still searching for availability and layered that on top of that a tracking study that followed our customers and their confidence in being able to travel in the near future. This gave us some leading indicators. Then we added text analytics to social media comments and explored digital patterns, including what words people were searching for on our website.

Our insights helped shape some critical decisions around things like our refund policy and the enforcement of safety protocols at our properties. We’ve been able to demonstrate our customer pent-up demand, given how people were engaging differently with our digital tools in our websites. We determined how to most effectively communicate to them and understand how this virus is impacting our customers’ propensity to travel.

As a result of some unfortunate, COVID-related workforce reductions within my own team, I’ve had the pleasure of getting to jump headfirst into data again. I get to visualize and analyze and model and do all the things that made me fall in love with this discipline in the first place. It feels a bit like being back at VCU. I’m very proud of that fact that I was a member of the inaugural cohort of VCU’s Decision Analytics master’s program and a lot of what I learned in those two intense years continue to serve me as a leader.

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