Most companies treat leadership development as a soft skill, something nice to have, but secondary to strategy and execution. Emilia D'Anzica, growth coach, go-to-market strategies advisor, and founder of Emilia Speaks CX, argues that's backwards: leadership isn't separate from growth, it's the foundation it stands on.

In this episode, Emilia and Luke talk about why founders hit a ceiling when they try to do everything alone, and why asking for help so often comes too late, only after revenue is already declining or key people are already leaving. Emilia shares her coaching methodology: an anonymous survey process, CC'ing the CEO on everything, and eight hours spent observing a team in training before she ever gives leadership feedback on what needs to change.

They also dig into what's actually broken in most go-to-market playbooks, the handoff between sales, customer success, and support, and why Emilia believes revenue problems are almost always wellbeing and alignment problems wearing a strategy costume.

Key topics:

• Why founders wait too long to ask for help, and what "healthy hustle" actually looks like
• Emilia's coaching process: anonymous surveys, CEO transparency, and reading a room in real time
• Why leadership has to be visibly involved in training, or change reverts within 30 days
• What's broken in most go-to-market playbooks, and how to fix the handoff between teams
• Emilia's quarterly goal-setting ritual, and her advice for staying grounded while building a company

Connect with Emilia on LinkedIn or Instagram, or reach her directly at emilia@emiliaspeakscx.com.

For the first time, Luke goes solo — no guest, just a direct update on where Fixing the Game has been and where it's going.

This podcast started out of frustration: watching Google and Meta go from leveling the playing field for small businesses to quietly working against their growth. For 49 episodes, the mission has been helping business owners fight that David-vs-Goliath battle. In this episode, Luke explains why that mission hasn't changed, but the tools to win it have. With AI, the rules of the game are shifting for real this time, and Luke is shifting the podcast's focus to match.

He talks openly about the quiet year behind the scenes, taking over leadership of his agency, and welcoming two newborns at home and why he's never felt more determined to make this show bigger. He shares his vision for where marketing is actually headed: a shift from fully manual, human-run campaigns to semi-automated and eventually fully autonomous marketing, using the self-driving car analogy (eyes on the road, hands on the wheel, until one day you let go) to explain how this transition will actually unfold, skill by skill, channel by channel.

Luke also previews what's next for the show: guests who are actively building this future rather than just commenting on it, bilingual episodes in German and English, and a sharper focus on agentic marketing, AI-driven systems, and the products his own company is building to prove the thesis out.

Key topics:

• Why the David-vs-Goliath mission of the podcast hasn't changed, even as the tools have
• The honest story behind a quiet year: new leadership, new kids, and a lot of reflection
• The self-driving car analogy for how marketing automation will actually roll out
• Why "the space for pure human advisory is getting narrower" and what that really means
• What's coming next: bilingual episodes, new guest criteria, and a renewed focus on agentic marketing

This episode kicks off a new chapter for the show — if you've been listening for a while, Luke would love your thoughts on what to call this next era of marketing (agentic CMO? AI-driven marketing? something else?), plus any guest suggestions for who should be on the show next.

Most people think ABM just means "more personalized emails." Declan Mulkeen, CMO at Strategic ABM and host of the Let's Talk About ABM podcast, has spent decades proving that's a misunderstanding of what account-based marketing actually is — and why it still works when done right.

In this episode, Luke and Declan trace ABM's origins back to the early 2000s — the idea of putting a CMO inside your most important customer, treating each account as a "market of one" — and walk through why that mindset matters more than ever. Declan breaks down the five stages of ABM maturity, what actually separates a good pilot from a wasted one, and why most companies get their ICP wrong the first time (and why that's completely normal).

They also get into where AI genuinely helps — deep research, synthetic audiences, hyper-personalization — and where it falls apart, plus Declan's "3 Rs" framework (Reputation, Relationships, Revenue) for knowing whether your ABM program is actually working.

Key topics:

• The real history of ABM, and why it's not just "good marketing" or a new name for key account marketing
• The five stages of ABM maturity, and how to tell which one your company is actually in
• What makes a pilot succeed — ICP selection, sales alignment, and picking the right account count
• Where AI actually saves time in ABM, and where "personalization at scale" just becomes noise
• The 3 Rs framework for measuring whether ABM is working: Reputation, Relationships, Revenue

Learn more about Declan's work and find Let's Talk About ABM on Spotify and Apple Podcasts.

Every time you use ChatGPT, that query gets tied back to you, even if you're running it "locally," even on an enterprise account. In this episode, Luke talks with Andrew Sispoidis, founder and CEO of ESMOS AI, about why today's AI companies can't avoid retaining your data, and what it would actually take to build an AI gateway that's private by design, not by policy.

Andrew explains why centralization is the root problem, large language models are built to take, analyze, and retain data and how ESMOS strips the "who" from the "what," so a query can be answered without ever being tied back to the person who asked it. He gets specific about the risks most people never think about: how anonymized data gets re-correlated back to individuals, how insurers can use query patterns to adjust rates, and why running a model "locally" still leaves an audit trail on someone else's servers.

We also get into the product itself — how ESMOS routes queries through multiple encrypted gateways, strips metadata down to a single timestamp, and lets users chain a question across multiple AI models without ever surfacing their identity — plus why professionals in regulated industries (legal, healthcare, finance) are becoming the platform's earliest and most natural users.

Key topics:

• Why centralized AI companies structurally can't avoid retaining your data
• How "anonymized" data gets re-identified, and real examples of the fallout (including insurance rates)
• Why running an LLM locally doesn't actually solve the privacy problem
• How ESMOS separates identity from content across every query
• Why regulated professionals are the first real market for private-by-design AI

Learn more about Andrew's work and ESMOS AI, and how they're building toward a fully decentralized, private gateway to large language models.

Most companies have the data to make better marketing decisions, they just don't use it. In this episode, Luke talks with Brian Massey, managing partner at Conversion Sciences and one of the original voices behind the rise of conversion rate optimization, about why so many businesses still run on assumptions instead of evidence.

Brian explains why redesigns so often backfire (a beautiful, modern site with a conversion rate that just dropped 40%), and why treating your website traffic as a live, ongoing focus group, not a one-time design project, changes everything. He breaks down the small, overlooked details that quietly kill conversions: dropdown menus that alienate the wrong customers, navigation that fails to communicate what you actually do, and messaging that never gets tested because it's "just words."

Brian also shares how AI is starting to change his work, not by replacing strategy, but by making it possible to rewrite copy in different voices for different types of decision-makers, and what happens when half of a website's visitors are no longer human, but AI agents themselves.

Key topics:

• Why UX designers and marketers still avoid the data they already have
• The real cost of a bad form field (a single dropdown menu can tank conversions)
• Why messaging matters more than most redesigns give it credit for
• The four types of decision-makers on your website, and how to speak to each
• How AI is changing copywriting, testing, and what "visitors" even means going forward

Connect with Brian and Conversion Sciences to learn more about how they help companies turn traffic into evidence-based growth.

Amazon's review system can make or break a launch overnight and it's under attack more often than most sellers realize.

Shane Barker is the founder of TraceFuse, an Amazon review-integrity platform that has removed over 13,000 fraudulent or guideline-violating reviews for more than 500 clients. After 25 years in e-commerce, SEO, and influencer marketing — including building UCLA's influencer marketing program — Shane spent two years cracking the code on how to get fake reviews legitimately removed under Amazon's own rules.

In this episode, we get into how review-bombing attacks actually work: competitors buying products, shipping them to strangers around the world, and using those "verified purchases" to leave coordinated one-star reviews, often timed right before Prime Day or the holidays. Shane breaks down the real revenue impact of a star-rating drop, the difference between white-hat and black-hat review tactics, and how his team uses AI to monitor thousands of reviews a day while keeping every filing compliant and human-reviewed.

We also cover the free tool TraceFuse built so any seller, client or not, can check which of their reviews are in violation and could be removed.

Key topics:

• How fake review attacks are actually executed, and why "verified purchase" doesn't mean what you think
• The real sales impact of dropping from a 4.3 to a 4.2 star rating
• White-hat vs. black-hat tactics for managing Amazon reviews
• How AI is used to monitor reviews at scale without automating the filings themselves
• Where to get a free report on which of your Amazon reviews violate guidelines

Ranking well on Google used to mean something. These days, it's only half the story.

Thomas Peham is the former VP of Marketing at Storyblok, co-founder of Otterly AI, and one of the leading voices in Generative Engine Optimization (GEO). His team has analysed over 100,000 websites to understand what actually drives brand visibility in AI search—and the results will change how you think about content, PR, and measurement.

In this episode, we get into why organic traffic is declining even when your Google impressions stay flat, and what the hidden impact of AI search looks like when it's not showing up in your click data. One of Thomas's clients had 1% of web conversions attributed to ChatGPT—but when they asked leads directly, 10% said ChatGPT was how they found them.

We cover the three pillars of GEO strategy (on-page, earned media, and community), why Reddit and user-generated content punch above their weight in AI results, and how the SEO/PR silo is finally starting to break down.

Key topics:

• Why click data is massively underreporting AI search impact
• The three content pillars that drive brand mentions in ChatGPT
• How one B2B brand went from zero citations to showing up in 3 weeks
• Why community marketing is having a quiet renaissance
• What skillsets will define the best marketers in the next 2 years

What happens to marketing when clicks disappear?

In this episode of Fixing the Game, Luke sits down with Rand Fishkin (Co-founder of SparkToro, former Moz founder) to break down one of the biggest shifts in digital marketing: the move toward zero-click content, AI-driven search, and platform lock-in.

Rand challenges many of today’s assumptions about SEO, content, and AI and explains why most marketers are focusing on the wrong things.

🔥 Key takeaways from this episode:

• Zero-click is not new (and not caused by AI)
  Platforms like Google, Instagram, and LinkedIn have been reducing outbound traffic for years. AI is just the latest layer.
• Traffic is a vanity metric
  Less traffic doesn’t mean less revenue. Many companies see traffic down, revenue up—because users convert later, not earlier.
• Stop chasing clicks. Start building demand.
  The goal isn’t to drive traffic—it’s to ensure people search for you when they’re ready to buy.
• “Great content” is no longer a strategy
  Generic, high-quality content is now commoditized. AI can create it instantly.
  What works: original, opinionated, “spiky” content that stands out.
• Brand is the ultimate growth lever
  Recognition and trust make every channel more effective—especially sales and outbound.
• Focus on your audience, not the hype
  It doesn’t matter what ChatGPT or Google does globally.
  What matters: where your audience actually spends time.
• Distribution has changed
  Your website is no longer the main traffic driver—it’s your home base.
  Attention is won on platforms like LinkedIn, YouTube, Reddit, etc.
• Viral loops matter more than ever
  With traffic harder to get, your product and content must be shareable by design.
• Audience research is the foundation
  Understanding behaviors, preferences, and channels is more valuable than ever—for both product and marketing.
• Execution still beats theory
  Learn how platform algorithms work and adapt. That edge is just as powerful today as it was 20 years ago.

💡 One core idea to remember:

Marketing isn’t dying.
It’s going back to what it always was:

👉 Build a brand
👉 Earn attention
👉 Create demand
👉 Show up when it matters