Valeria LedezmaHow Data-Driven Marketing Fuels SMB Growth – Insights from GoDaddy’s Tom Lehman
In this episode of the Data-Driven Marketing podcast, Gideon Rubin, CEO of Local Data Exchange (LDE), sits down with Tom Lehman, Vice President of Analytics at GoDaddy, for a conversation about the realities of implementing data-driven strategies at enterprise scale. With decades of experience consulting for Fortune 100 companies, Lehman offers a grounded, practical perspective on what it really takes to build a culture around data and drive measurable growth—particularly for small and medium-sized businesses (SMBs).
Business First, Not IT-Led
Lehman begins by outlining the misalignment of analytics functions. Too often, he explains, companies house analytics within IT departments rather than within the business units that benefit most from data insights. While this setup might seem logical—given that data is technical in nature—it often leads to underperformance and lackluster ROI.
“Analytics needs to be aligned to the business side of the company,” Lehman asserts. “Not to the IT side.” His argument is based on both experience and outcomes: when analytics sits under IT, it becomes more about systems and less about strategy. But when it’s business-led, analytics teams are positioned to directly address organizational goals, unlock new opportunities, and ultimately justify further investment.
Gideon agrees, most successful data-driven transformations he’s seen are the ones where analytics is not just a support function, but an integrated partner to the business. The conversation emphasizes that this alignment isn’t a luxury—it’s a requirement for sustainable success.
The C-Suite Mandate and Specialization Challenges
The discussion then turns to an increasingly visible trend: AI and machine learning mandates being issued by the C-suite. As more executive teams push for automation and digital transformation, organizations are rushing to embed AI into business processes. But with that excitement comes complexity.
Lehman explains that while these mandates signal positive momentum, they also create tension within teams—particularly around roles and expectations. One of the biggest pitfalls he sees is the unrealistic scope of responsibilities being placed on data scientists. “We’re trying to get too much out of data scientists right now,” he warns. “They’re being asked to do everything—be a data engineer, a program manager, a documentation expert… it’s just not scalable.”
To address this, Lehman advocates for a strategic “decoupling” of roles. Rather than forcing one person to wear five hats, organizations need to return to clear specialization. In practice, that means understanding what each role does best and building cross-functional teams that operate collaboratively instead of redundantly. This structural clarity, Lehman believes, will become even more critical as AI adoption accelerates.
Automation and the Customer Experience
A major thread throughout the episode is the power of automation to improve customer experience—particularly in SMB contexts. Lehman shares how GoDaddy is actively using analytics to identify friction points in their customer journey and streamline operations accordingly. Whether it’s improving onboarding flows or tailoring messaging, the goal is to meet customers where they are and serve them in smarter, faster ways.
The discussion highlights a fundamental truth: good data isn’t about dashboards—it’s about impact. For GoDaddy, that impact is measured in how well they help small businesses thrive in a crowded digital landscape. By automating the right experiences, Lehman says, they’re able to scale personalization and reduce time to value for users.
Rubin and Lehman agree that this is where data science can make its most meaningful contribution. It’s not just about predicting behavior—it’s about shaping it in real time to create better outcomes for businesses and their customers alike.
The Future of Data Teams
Looking ahead, Lehman sees a shift coming in how data teams are structured and supported. He predicts that organizations will move away from monolithic, do-everything data science roles and toward more distributed models where analysts, engineers, and product managers each bring specific strengths to the table.
This shift also means rethinking how companies invest in talent. Instead of hiring unicorns who can supposedly “do it all,” smart companies will double down on team composition, training, and tools that allow each contributor to focus on their core competencies.
At the same time, Lehman notes that leadership must evolve as well. He urges senior decision-makers to be realistic about what AI can and can’t do, and to remain focused on business value. As he puts it: “There’s always going to be a shiny object. But if it’s not tied to a business problem, it’s not worth solving.”
Key Takeaways
By the end of the episode, several clear insights emerge:
- Analytics belongs in the business – Embedding data teams within business units increases alignment, clarity, and return on investment.
- AI mandates need structure – Executive directives are important, but must be paired with well-defined roles and realistic expectations.
- Specialization is strength – Asking data scientists to do everything leads to burnout and inefficiency; the future lies in focused, collaborative teams.
- Customer experience is the frontier – Automation efforts should prioritize real impact on customer journeys, especially for SMBs.
- Leadership matters – Driving a data culture isn’t about tools—it’s about aligning people, processes, and incentives toward business outcomes.
This episode of Data-Driven Marketer is a must-listen for anyone working at the intersection of analytics, marketing, and SMB growth. Tom Lehman’s perspective is refreshingly honest and operationally savvy, offering listeners not just ideas, but actionable frameworks for driving transformation in their own organizations.
Podcast: Challenges to becoming Data Driven with Tom Lehman, Senior Director of Marketing Analytics at GoDaddy.
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Podcast Transcription:
I’d love to hear from your perspective.
What are some of the biggest challenges that enterprises wrestle with to
become data-driven, to really, you know, move their business based on the
information they’re getting, the feedback loops?
One of the bigger challenges that I faced, and I’ve done a tremendous amount of consulting for basically the Fortune 100, I recommend if you really want to be successful in analytics, that analytics needs to be aligned to the business side of the company. Not to the IT side. A lot of times people will make the investments and put it more in line with IT, thinking you’re moving a lot of data etc.
That works to a certain degree, but a lot of times what you run into is you run into investment commitment issues.
And I find that, for more reasons than one, you’re always going to be best served by having
analytics branch up into the business.
You’re going to be able to really solve around the
business problems, which, when you start getting the traction around there, you start getting
additional investment. And I have always claimed, for anybody that I have interviewed, for anybody that I have talked to at the university level, you really want to be integrated with an
analytics company that is business-focused. Yeah, that’s interesting. I mean, taking that
same concept from a little bit different position, we’re starting to see a lot more C-Suites
sort of making AI or ML mandates across their organizations. You know, certain goal posts
they’re putting in front of the business.
How do you think that’s going to impact
business processes over the next five years? You know, I think what it’s going to do is
start creating more of a decoupling back into the areas of specialization that we’ve seen
before.
I’m sure you’re seeing this as well, you know. Where people are trying to automate
right now seems to be around customer experiences, trying to really get into, by automating, trying to figure out what the needs and goals are of the customers and to be able to really speak about that, to speak to them in that fashion. And we’re doing a lot of this across GoDaddy, we’re trying to use analytics to help us understand where we can automate different functions. So, I think you’ll continue to see this, but what’s going to happen is you’ll see the decoupling and I think
you’ll also see that, as part of that decoupling, I think some of the areas that we’ve gotten
ourselves maybe in a little bit too deep, is trying to get too many functions out of data
scientists.
We were talking about data movements earlier. I really believe that, you know, we need to be thinking about the investments that we make, and getting people aligned to the things
that they do well.
We’re trying to get too much out of data scientists right now, I think.
We’re trying to get them to wear every hat from a data engineer to a program manager, to a
documentation specialist, you name it. And we’re just trying to get too much out of that
specific function. And so, you know, I’ve gone back to this concept of decoupling. I think you’re going to start seeing this as a response, really, from the different mandates that are coming from executive levels trying to automate analytics.
