How to build a
data-first culture for a digital transformation
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.
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.
https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/how-to-build-a-data-first-culture?cid=other-eml-alt-mip-mck-oth-1803&hlkid=0ffce3b35b8643a1ba1672ba70262edd&hctky=1627601&hdpid=80e7db92-1a3c-4dca-90e5-2cc354bd9577
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