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How to build a data-first culture for a digital transformation

Digital transformation - data culture - Cubility

Getting better data is key to eliminating the unknowns of a digital transformation. At Sprint, as CDO Rob Roy explains, leaders called for a new company culture that put data first.

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.

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.

Article originally published by McKinsey

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