Turn Research Into Actionable Insights with Nathan Livingston

Inside Turn Research Into Actionable Insights

1. —

   Meet Nathan Livingston

   The episode opens with Nathan's background: head of customer research at Bally's Interactive, an iGaming and sports betting business across Europe and the US. After years in sport and entertainment research, from Inter Milan and Real Madrid to the EFL, he sets up a conversation about working fast with a lean team, telling stories from data, and protecting human critical thinking as AI takes over more of the work.

2. 01

   From a History Degree to Research

   • How a history degree built the exact skill research needs: weighing the same evidence and forming your own view.
   • A graduate scheme at Millward Brown (now Kantar) that turned reading and surveys into client narratives.
   • Why no specific degree is required, as long as you can apply the thinking to research.
3. 02

   How Sports Research Grew Up

   • The old days: "is it the highest number? tick," and research treated as rough.
   • The shift from media value and eyeballs to brand impact and sponsorship uplift.
   • Why smaller sponsors, women's football, and charity deals are all pushing for proof of value.
4. 03

   Reading the Signal in Social Noise

   • Why social matters for year-round engagement, even when a partnership gets little TV airtime.
   • Cutting through the noise: which creators, which shots, which content actually engages.
   • Balancing the data against commercial and contractual realities, like who you can and can't feature.
5. 04

   The Fight for Research Budget

   • Nathan's blunt take: sport doesn't value data the way other industries do.
   • The "race to the bottom" to get as much data as cheaply as possible.
   • The researcher's real job: saying no to data that won't answer the question, as he did at the EFL.
6. 05

   Building a Lean, Fast Research Stack

   • A two-person team built for speed, turning some projects around within a week.
   • AI-moderated qualitative interviews that run off a discussion guide without booking a moderator.
   • Quick tools like OnePulse and a daily CSAT tracker in Medallia, and why 50% of an answer is sometimes enough.
7. 06

   Turning Data Into a Story That Lands

   • Start with the objective and a human conversation about what people actually want to learn.
   • His commercial director's lesson: if you don't know your product, you can't research it.
   • Pre-briefing stakeholders before the big reveal, so their context strengthens the final story.
8. 07

   The Researcher's Job Is to Agitate

   • Why good research challenges people, framed as a confirm-or-deny of a clear hypothesis.
   • Planting deliberately provocative questions to make a room think, even if they're "wrong."
   • Real examples: questioning a clunky deposit, withdrawal, or ticket-collection process from the customer's side.
9. 08

   The Hardest Part: AI Without Critical Thinking

   • How AI lets anyone generate a ten-page document from five sources, minus the judgment.
   • Why he still checks every AI-assisted CSAT pull before it reaches stakeholders.
   • The rule: spend the time AI saves on talking to people and adding the context it can't.
10. 09

    Don't Lose the Craft to AI

    • Why you should do it by hand once: scripting, questionnaire design, building decks from scratch.
    • The real risk of never learning routing or randomization because AI did it for you.
    • The UK fixture that AI invented, and why knowing your domain means you can challenge a wrong answer.
11. 10

    Advice for Starting (and Hiring) in Research

    • His magic-wand wish: get people to see, and prove, the value research delivers.
    • What he'd tell his younger self: slow down, and compete on attitude and quality, not hours.
    • What he hires for beyond AI skills: communication, interpretation, and the willingness to say when you don't know.