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What is a digital transformation? That seems like a simple question. But as organizations embark on massive changes, understanding what a digital transformation is—and isn’t—is emerging as a crucial success factor. In this interview with McKinsey’s Barr Seitz, Rob Roy, chief digital officer at Sprint, discusses the nature of a digital transformation and what it takes to develop a data-first culture to support the change.

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Barr Seitz: How was your digital transformation different from what you expected when you began?

Rob Roy: We had a great start to our digital-transformation journey—budget, enthusiasm, energy. But we fell into the trap of not really understanding what a digital transformation was. We started using the phrase “digital transformation,” migrated processes and tools to be more digital, and created a dedicated business unit, and thought we’d automatically see that transformation happen.

For example, we decided to do more sales online. When we set it up, we then tried to force customers down the digital path. But many of them weren’t ready. The spirit of what we were doing was correct, but a complete understanding about what we were trying to do wasn’t there.

After six months, we learned that just because you say it, it doesn’t make it so. A digital transformation isn’t about digitizing a channel or simply doing more things digitally. It’s a much broader scope than that. We’re really looking to improve and simplify customer “moments of truth”—and all the supporting processes that build a true omnichannel, world-class experience. We’re now working with each area in the business to help everyone think and act digitally for the things they control. And we’re starting to see real gains in productivity, simplification, cost reduction, and building on earlier gains focused on sales.

Those gains and everything we’ve learned have given us real facts to help make better decisions. When we ask for more investment, we come with a business plan that’s more thoughtful and based on data and actual use cases. For example, we were able to show that we created algorithms that improved the rate of churn in a high-risk segment. To increase the benefits, we showed we needed three more algorithm people with PhDs. It’s a much easier case to make.

Barr Seitz: How—and why—did you inculcate a data-first mind-set?

Rob Roy: There’s not just one metric you need to pay attention to, but it’s not hundreds either. Organizations can get overly excited about data, then all of a sudden, you’re overwhelmed. So we decided to focus on data that helped us understand customer behavior and eliminate the unknowns. Look-alikes (an algorithmically assembled group of people who resemble, in some way, an existing group) based on existing segments of customers were most valuable, and over time we layered additional elements, such as demographics, behavior, age, current carrier, and location.

We then overlay those insights with data from digital properties: website, mobile app, stores, and call centers. And we started to understand better our customers’ journeys across the web, as they called us, tweeted about us, etc. We’re now starting to teach our “bots” to learn more about contextually relevant interactions with the customer. For example, if a customer visits one of our stores, then comes online and looks at various sets of pages or has a pending order, the bot learns how to respond to that specific customer profile. We can then start to paint a picture around users that we know we want and who are most important to our business.

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Having a data-first mentality is a crucial first step, but then you need to put in place the processes and capabilities to be able to use the data. We had to first collapse data into one or two locales so we could easily extract it. We created a large Hadoop environment and fed in all the data we had: network, store, customer, site, third party, and DMP (data management platform). There was a lot of laying of pipes and foundations to allow us to start using the data.

Then we did a road show. We went to all the biggest business owners, showed them the data and what it could do, and asked them what problem we could help them with. One of them said they were starting to see pressure on churn, so we analyzed why, based on correlative events, and gave those insights back to the business. Right away, those insights started producing actionable tactics and results, which showed how much value there was in the data. Soon enough we saw so many wins that other pockets of the organization started to come to us. We learned that people really value the facts that allow them to drive results, so we focused just on delivering facts, not opinions. What we’ve been able to do is lay intelligence and facts over people’s personal experience.

Barr Seitz: What were your most important hires, and how did you make them succeed?

Rob Roy: We used a combination of intense networking and tracking down people at companies we aspired to be like. Our top hires were:

1. Head of BI (business intelligence) and AI (artificial intelligence) have been the most important hires. They have the ability to understand large blocks of data and to put into English how we can use that data in a meaningful way.

2. Business lead for digital adoption. This is a role that champions the idea of being more digital-first, and helping the parts of the business see the improvements from digital—for example, working with the in-store team to update or refresh the store system to be more digitized so they can do more personalization or A/B testing. This role is also a cheerleader for digital successes and, more importantly, gives people across the business the spotlight so they share in those successes.

3. Digital DMP owner. That person has been integral in the operation of ingesting traditional data and tying it to digital opportunities. Think data warehouse on steroids. This role enables almost real-time querying of a very broad data set. And they work with teams to translate those learnings into offers and distribute them through the digital-media landscape to capture net new customers.

It’s hard enough to find these people, so it’s really important to make sure they succeed once they’re here, and that’s where I focus my time. My main job is to block and tackle for them, get through red tape, and help them build relationships with my peers. For example, there had been lots of people standing up many data environments separately, and pipes between them were very thin, i.e., it was hard to exchange and access the data. Now, we have one source of truth, one place to go for meaningful insights across the entire organization.

To facilitate standing up this capability and support my BI/AI lead, for example, I went to the various decision makers to argue the case for consolidating the data and convinced them to provide funding for a universal data hub. In doing so, by the way, I was also able to identify smart resources in the company and bring them into the process, which helped move things along.