Bonnie Spalding, consulting director for The Walt Disney Company, shares insights into how the data analytics approach pioneered at Disney’s hotels and resorts proved effective and grew into companywide practices.
HOUSTON—The Walt Disney Company is a massive organization spanning the globe with everything from animation to amusement parks to hotels and resorts under its umbrella.
But Bonnie Spalding, consulting director for Disney, said much of the company’s approach to data analytics grew out of the practices innovated at the company’s hotels.
Speaking during “The magic of mastering analytics” session at HSMAI’s 2018 Revenue Optimization Conference, Spalding shared some insights into how her company approaches analytics.
She noted the company has 140 people—or “cast members” in Disney parlance—on its data team, roughly a third of which have doctorates.
“We have this group of really specialized, science-driven team members,” she said. “It creates a center of excellence within the organization.”
There is no end point
Spalding said it’s important for organizations recognize there’s no point where they’ve solved data analytics, and instead it’s a journey of continual improvement. She said Disney’s journey in data—which started with proving success in the hotels and parks before growing more widespread—mirrors that of revenue management in the hotel industry, which began with incremental successes in room inventory management before growing in other areas.
She added that mentality is key in winning over stakeholders like owners or general managers.
“If you prove success in the rooms segment, that enables credibility and talent to move into the next steps or other areas or opportunities in an organization,” she said.
Steps in the journey
Spalding said it’s important to recognize that different organizations are at different points in that journey to mastery in data analytics, and there are several stepping stones along the way. She referenced the book “Competing on Analytics: The New Science of Winning” to note that organizations start as “analytically impaired.” She mentioned the other stages—localized analytics, analytical aspirations, analytical companies and analytical competitors—while noting many organizations naturally stop at the localized stage.
Reaching that stage “could end the journey, but what makes or breaks it is the buy-in,” she said.
Spalding said as companies grow in their use of analytics, they become more integrated and data is used as the “drivers and measures of success within an organization,” which she said is key.
“A lot of us get stuck a bit on transactional analytics,” she said.
Analysts should trumpet successes
The Broadway musical version of “The Lion King” is often pointed to as a massive success in the company’s deployment of data analytics to optimizing theater ticket pricing. Spalding said that it’s important to find successes like that to “evangelize and educate” people on the importance and power of analytics.
“We essentially evangelize the idea of data analytics within and outside the organization,” she said, noting the company hosts an annual conference on analytics for that very reason.
‘Start with an end in mind’
Spalding said data analytics efforts are often more successful when they’re more focused. She said this can help get the right information in front of the right people right away, which helps identify key stakeholders and identify things like what the right key performance indicators are for a project.
She said it’s necessary to know “what success looks like from a tangible, concrete perspective.”
In terms of deciding what metrics to look at and present to others, she said it behooves data analysts to determine “what is most important to you and make sure it’s front and center.”
Incorporating alternate realities
Spalding said her team at Disney spends a considerable amount of time figuring out what “counterfactuals” they need to look at to measure success against.
“That essentially means you’re making up an alternative reality,” she said.
She said these alternate outcomes act as sort of a control group versus real performance in a “nonsterile environment.”
That can mean considering what would’ve happened if by taking a different action than what actually happened or even “the cost of doing nothing.” She said it’s important to identify counterfactuals that are accurate representations to benchmark against.
New data tools
There are considerably more tools to deal with data than ever before, including machine learning and artificial intelligence, Spalding noted. She also said there are more sources of data today than in the past that analysts need to consider.
Before embarking on incorporating those new tools, though, she said it’s important to identify the person on the team who has the expertise to deal with them along with other factors, such as whether tools will be outsourced or built internally, how quickly the team can get them to market and if they’re scalable in a way that’s applicable to the organization.