The Yield Doctor with James Deaker

Is your Retail Media Network actually generating new revenue, or just shifting trade spend around? In this episode, James Deaker (The Yield Doctor) sits down with retail media expert Kiri Masters to diagnose the "Retail Media Doom Loop" and uncover the blueprint for building a high-margin commerce media business. Whether you're a mid-sized retailer or a major network, we dive into the structural shifts required to move beyond basic ad units and start capturing real, incremental yield. In this video, you’ll learn: The critical difference between Retail Media and Commerce Media. Why reclassifying trade spend is a "short-term win, long-term fail." How smaller networks can compete (and win) against the giants. The specific "Yield Doctor" diagnosis for maximizing digital asset value.

What is dCPM—and why does it confuse so many people in digital advertising? In this video, I break down what dCPM (dynamic CPM) actually means, how it evolved from the ad network era to modern programmatic platforms, and why it’s still widely misunderstood today. We’ll cover: The difference between fixed CPM and dynamic CPM How auction-based pricing works in practice Why platforms like The Trade Desk, Google, and Meta use different terminology The key tradeoff: efficiency vs predictability And most importantly… who controls pricing and who takes the risk If you work in digital media, ad tech, or yield management, understanding dCPM is critical—not just as a pricing concept, but as a framework for how decisions are made in modern advertising systems. Key takeaway: dCPM isn’t a standardized industry term—it’s a concept that shifts control, risk, and pricing decisions in ways that aren’t always obvious. I’m James Deaker, The Yield Doctor. I work with clients on issues like these to help them make more money from their digital assets.

Most sales compensation plans are designed to drive revenue. But what if they’re quietly destroying profit? In this video, I break down a common but overlooked problem: the misalignment between sales incentives and business economics. When comp plans reward the wrong behavior, you don’t just get suboptimal outcomes—you get systematic leakage. We’ll walk through: Why revenue ≠ profit (and where the gap comes from) How common compensation structures create unintended consequences Real examples of behaviors that look good on paper but hurt the business What better alignment actually looks like in practice This isn’t about blaming Sales—it’s about designing systems that drive the right outcomes. If you’re responsible for pricing, deal structure, or revenue strategy, this is a problem you need to understand. We discuss: Sales incentives vs. business objectives Revenue vs. margin tradeoffs Deal structuring and discount behavior Compensation design pitfalls Aligning incentives with long-term value I’m James Deaker. I’m The Yield Doctor. I work with my clients on issues like these to help them make more money from their digital assets. ---- Related Video on aligning Sales Comp with Profit: https://youtu.be/WOAkrqT24qQ

If your rate card only changes once a year, you don’t have a pricing strategy — you have a pricing ritual. In this video, I introduce the concept of the Pricing Heartbeat — a structured, ongoing approach to keeping your pricing aligned with the market. Most companies treat rate cards as static documents. They get set once, debated heavily, and then left untouched. But the market doesn’t stand still. Demand shifts. Performance changes. Product mix evolves. And if your pricing doesn’t keep up, you create gaps — either leaving money on the table or slowing down revenue. In this video, I walk through a more disciplined approach to pricing: What a Pricing Heartbeat is and why it matters Why infrequent rate card updates create risk How to think about cadence vs. constant change What signals should trigger a pricing update How to maintain consistency while still adapting to the market This is a practical framework for anyone responsible for pricing, monetization, or revenue strategy in digital media. We discuss: Why annual rate card updates fall short The concept of a pricing “heartbeat” How often pricing should actually be revisited The risks of stale pricing in a dynamic market Signals that should trigger pricing changes How to balance stability with responsiveness Other Rate Card Videos: https://www.youtube.com/watch?v=KA9M9SAZsb4&t=8s https://www.youtube.com/watch?v=disIapXfB5Y

Most publishers think custom rate cards are a necessary part of doing business. A large client asks for special pricing… Sales pushes for flexibility… and before long, you have dozens of “exceptions” across your business. But what seems like a short-term win can create long-term damage. In this video, I break down the hidden cost of custom rate cards—and why they often lead to: Loss of pricing discipline Increased operational complexity Constraints on product innovation Poor visibility into true market demand More importantly, I walk through what to do instead, including: How to maintain a single source of truth for pricing Why adding value is more effective than discounting price How to create structured flexibility without creating chaos If you’re managing pricing, yield, or monetization at a publisher, ad tech platform, or retail media network—this is a problem you’ve likely seen firsthand. We discuss: Why custom rate cards erode pricing integrity The operational burden of managing exceptions How custom deals distort price-demand signals Better alternatives to discounting How to structure pricing that actually scales These are exactly the types of pricing and yield challenges I work on with clients. If you’re dealing with similar issues, feel free to reach out or follow along for more content.

Most pricing teams aren’t failing because they’re bad at pricing. They’re failing because they’re set up to fail. In this video, I break down one of the most common (and misunderstood) problems in pricing organizations: why strong analysis, good data, and capable teams still struggle to have real impact. If you’ve ever felt like: “We’re doing great work, but no one listens” “We’re constantly being overridden” “Pricing gets blamed when deals go wrong” …then this isn’t a capability problem. It’s a structural one. What you’ll learn in this video: Why pricing teams often lack influence (even when they’re right) The structural issues that limit impact (not talent or tools) Why pricing is often under-leveled relative to Sales and leadership The concept of pricing as a power and authority problem, not a data problem How leading companies structure Pricing, Deal Desk, and Yield/Revenue Management Why expanding scope is key to elevating the function The risks of overcorrecting with overly senior org structures Key idea: If your pricing team doesn’t have the authority to influence decisions, it won’t matter how good the analysis is. Who this is for: Pricing leaders and analysts Ad tech and digital media professionals Revenue, yield, and monetization teams Sales and commercial leaders working with pricing Executives thinking about org design and decision-making Work with me This is exactly the type of challenge I help companies solve — not just improving pricing, but structuring teams so that pricing can actually drive results. 👉 Learn more: https://www.korukeamedia.com 👉 Or reach out directly if you’re dealing with this in your organization

In digital media, one of the strangest meetings you can be called into as a revenue or yield leader is this: Every product team reports that price realization improved. And yet… total revenue is down. It sounds impossible, but it happens more often than you might think. In this video, I walk through one of the most important — and frequently misunderstood — dynamics in digital advertising economics: the Mix Problem. Using a simple example, we explore how a company can successfully increase pricing across multiple products and still see overall revenue decline. The reason lies in changes in demand mix — when buyers shift from higher-value inventory to lower-value alternatives. I also extend the example to a very common situation in digital media: launching an Audience Extension product. While new products can increase scale and opportunity, they can also unintentionally cannibalize higher-value inventory, reducing overall yield if the mix shifts in the wrong direction. This is exactly why yield management exists. Someone needs to monitor the system-level economics of the business, not just the performance of individual products. In this video we cover: • What the Mix Problem is in yield management • Why higher prices don’t always mean higher revenue • How product mix shifts affect digital media yield • Why Audience Extension can sometimes cannibalize premium demand • The role of Yield Management and Revenue Operations in protecting overall economics If you work in digital advertising, ad tech, retail media, or publisher monetization, understanding this dynamic is essential for managing pricing, packaging, and inventory strategy.

I recently attended RampUp 2026, LiveRamp’s annual conference, where AI dominated almost every session and conversation. But what struck me most wasn’t the technology itself — it was how early the industry still is in figuring out how AI will actually be implemented in digital advertising. In this video I share three takeaways from the conference: AI in advertising still lacks standards. Many of the protocols that will enable AI agents to operate in advertising markets are still being developed or competing with each other. As with past infrastructure waves, standards may ultimately determine how quickly AI can scale. AI currently faces little organized resistance in AdTech. Unlike earlier innovation waves such as programmatic buying, there is not yet a strong group pushing back against AI adoption. The conversation about AI is ahead of the operational reality. Despite the intense focus on AI, relatively few companies have actually implemented agentic workflows in production environments. Finally, I discuss the tension emerging between AI acceleration and increasing privacy regulation, and why governance, control, and structured data will likely become critical capabilities for companies navigating the next phase of digital advertising. If you work in ad tech, programmatic advertising, or digital media, I’d be curious to hear your perspective on how quickly AI adoption will actually happen. — James Deaker The Yield Doctor

AI is everywhere in digital advertising right now. But as Brian Silver (EVP, Global Marketing Solutions at TransUnion) makes clear in this conversation — AI will not fix bad data. If your identity resolution is weak… If your data is fragmented across 16 systems… If your enrichment strategy lacks depth and scale… Then AI simply accelerates confusion. In this episode of The Yield Doctor podcast, Brian and I go deep on: Why the majority of AI initiatives fail due to poor data quality Identity resolution as the foundation of revenue strategy The growing power of publisher data in the ecosystem Data enrichment and connectivity as competitive advantages Why fragmentation in CTV and global markets still matters How leading companies are using AI for propensity modeling and real business outcomes The emerging risk of synthetic traffic and AI agents We also revisit lessons from our Yahoo days and explore how the supply-side is beginning to rethink its leverage in a world dominated by demand-side scale. If you are a publisher, ad tech executive, or marketing leader trying to prepare your organization for an AI-driven future, this conversation will challenge you to focus on fundamentals — not features. Because in the end: AI won’t fix bad data. But high-fidelity identity, enrichment, and measurement can drive real revenue. Learn more about TransUnion Marketing Solutions: https://transunion.com/clarity https://www.youtube.com/@transunion

Most executives assume stronger data controls, privacy processes, and governance frameworks slow a business down. In practice, the opposite is often true. In this conversation, I’m joined by Devan Brua, Founder & CEO of PrivacyWise, to explore why weak data practices quietly create friction across product, sales, and leadership teams — and how stronger data controls actually give organizations the confidence to move faster, not slower. We discuss: Why many companies lack true visibility into their data flows, data usage, and data ownership How data privacy and data governance issues often surface late, after key product, sales, or partnership decisions are already made The gap between believing you’re “fine on data” and the operational reality inside ad tech and digital media organizations Why stronger data controls, privacy processes, and governance frameworks reduce hesitation and speed up business decision-making How AI, scale, and automation amplify existing data governance weaknesses rather than creating entirely new risks This isn’t a conversation about box-checking compliance or slowing innovation. It’s about building shared visibility, clearer decision-making, and operational confidence — so teams can move quickly without constantly second-guessing data risk. If your business relies on data for growth, this conversation will likely challenge some assumptions. I’m James Deaker — The Yield Doctor. I help digital media and ad tech companies diagnose the structural issues that quietly cap growth, and design systems that let teams operate faster with confidence. Edge Tool: https://privacywise.tech/edge/ Devan Brua: https://www.linkedin.com/in/devan-brua/

Bad ads are rarely the real problem. They’re symptoms. Symptoms of weak incentives, poor decision-making, or ad operations teams stuck firefighting instead of optimizing. In this video, I walk through a set of external signals — things anyone can observe using nothing more than a standard browser — that often point to deeper problems in a publisher’s ad operations and yield management. When I look at a website or app for the first time, I’m not guessing. I’m looking for signals in the ad experience itself: what’s broken, what’s showing up, and what that reveals about how the business is really being run. This isn’t about naming and shaming. It’s about learning what to look for — whether you’re a publisher, an investor, an analyst, or someone evaluating an acquisition. What this video covers I break down a set of observable ad-level signals and explain what each one might indicate internally, including: Ads that fail to load or render correctly High-value placements filled with house ads or PSAs The same creative appearing in multiple ad slots Lack of advertiser diversity Aggressive ad refresh behavior Sticky ads vs. pop-ups Overall ad and advertiser quality For each signal, I evaluate: Ease to Detect — how easy it is to spot just by using the site Strength of Signal — how strongly it suggests deeper yield or operational issues Some signals are easy to spot but weak on their own. Others are harder to catch, but very concerning when you do see them. What matters isn’t any single issue — it’s the pattern across multiple signals.

Most publishers don’t struggle with programmatic because of bad technology — they struggle because they never decided what programmatic is for. In this video, I break down three distinct “programmatic operating models” (not tactics), so you can recognize which one you’re actually running today — and whether it’s the one you intend to run. You’ll learn: Model 1: Programmatic as Insurance — direct sales protects premium value; programmatic just catches what’s left. Model 2: Programmatic as the Business — inventory value is maximized through scale, competition, and continuous market feedback. Model 3: The Hybrid — where most publishers live, balancing relationship-led selling with market-driven price discovery (and the complexity that comes with it). We’ll cover what each model implies for: The role of sales vs. ad ops Market exposure (and what demand you’ll never see) The quality of your pricing signal Clearance logic (waterfalls, auctions, PMPs) The risks that show up first (often before revenue moves) The big takeaway: every model can work, and every model can fail — the problems start when the model you’re running doesn’t match the model you think you’re running. If you want a second opinion on your current setup — especially if you’re in the hybrid middle-ground — reach out. I’m James Deaker. I’m The Yield Doctor.

Retail Media Networks (RMNs) don’t grow in a straight line. In this video, I break down the predictable stages of growth that every Retail Media Network goes through — from early launch momentum to scaled, complex operations — and, more importantly, the challenges and traps that tend to appear at each stage. My focus here is on Retail Media Networks because they’re the fastest-growing part of our industry right now. But if you’re working at any publisher or platform experiencing rapid growth, you’ll recognize many of these same patterns. I’ve seen these stages play out repeatedly across publishers, platforms, and monetization teams. The specifics change, but the underlying dynamics don’t. In this video, we cover: • The core stages of RMN growth • Why early success often creates future bottlenecks • The operational and pricing challenges that emerge as scale increases • Where teams most often get stuck — and why • How to think ahead so today’s wins don’t become tomorrow’s problems This is a framework video — not a set of tactics. The goal is to help you recognize where you are now, and what problems you should expect next, before they catch you by surprise.

Every publisher wants a bigger slice of ad revenue. In retail, that only happens if you can prove ads drive incremental sales, not just clicks. In this conversation, I sit down with Danilo Tauro (ex–Global Director of Media, Tech & Data at Procter & Gamble; former Head of Product for Amazon Ads; ex–GM of Uber Eats Advertising; now Managing Partner at Cardigraphy AI) to unpack how retail media networks (RMNs) and commerce media networks (CMNs) really grow. We cover: The difference between retail media and commerce media – and why RMNs are a subset of CMNs Where each model has the “right to win” and how to break through growth plateaus The next wave: off-site, CTV, sponsorships, and data products Why ROAS alone is a terrible North Star and how to think in terms of incrementality, margin, and LTV How to build smarter data partnerships and clean room strategies Build vs buy: where to differentiate, where to partner, and how to keep your stack modular Where AI is actually being used today in retail and commerce media (creative, optimization, and beyond) If you work on a retail media network, commerce media network, or you buy media from them, this is a deep dive into the strategies and trade-offs that will matter over the next few years. I’m James Deaker, The Yield Doctor. I advise publishers, app developers, and ad tech companies on yield, pricing, and operations. If you’d like help applying these concepts to your business, feel free to reach out.

Dynamic pricing is one of the most overrated tactics in programmatic yield management—not because it doesn’t work, but because most teams try it far too early. In this video, I break down why dynamic pricing often backfires, the risks you need to understand, and the foundational yield management practices you must have in place before you even think about changing floors dynamically. We cover: What “dynamic pricing” actually means in programmatic Why it creates revenue leakage when done too early The guardrails, rate cards, deal desks, and segmentation you need first How algorithms and marketplaces can react in unexpected ways When dynamic pricing does make sense The risks of outsourcing dynamic flooring to SSPs or intermediaries Dynamic pricing can work—but only when your strategy is mature, your data is strong, and your product hierarchy is crystal clear. Otherwise, it becomes a random walk that can damage your brand, your PMPs, and your long-term revenue. If you’re facing these challenges, this is exactly the kind of work I do with clients. —James Deaker, The Yield Doctor

AI isn’t sweeping through digital media in one big wave. It’s reshaping ad operations in three very different ways — evolutionary changes, true revolutions, and a whole category of “too early to tell.” In this session (recorded at AdMonsters Austin), I break down what each category actually means for people working inside publishers, platforms, and ad-tech companies. We look at: 👉 Evolution: Incremental changes in areas like ad serving mechanics, forecasting, segmentation, and dashboarding — where years of machine-learning investment mean AI is an accelerator, not a shock to the system. 👉 Revolution: Where the real disruption begins — from creative generation at massive scale to workflow compression, execution agents, and client-facing automation. I also explain why protocols like MCP and AdCP matter for the next layer of advertising infrastructure. 👉 Unknowns: The risks no one is fully prepared for yet: arms races with fraud, black-box behavior, agent-to-agent collusion, and the digital-advertising equivalent of “50 Waymos blocking a cul-de-sac.” Whether you run ad operations, revenue operations, or sales operations, this talk will help you understand: – Where AI will quietly streamline your world – Where it will fundamentally change roles and workflows – And where you’ll need guardrails, oversight, and real human judgment If you’ve got thoughts, pushback, or examples from your own org, drop them in the comments — I always reply. — James (“The Yield Doctor”)

Rarely do you get to hear from someone who built an entire advertising business from the ground up — inside a company that wasn’t even known for ads. In this episode, James Deaker (The Yield Doctor) talks with Shane Dwyer, Head of Sales for Lyft Ads, about how Lyft created a thriving advertising ecosystem from scratch — from its first in-app placements to national brand campaigns. Shane shares what it takes to launch and scale a media business inside a non-media company — the challenges, lessons, and innovations that made it work. In this conversation: The origin story of Lyft Ads. - Turning ride intent and destination data into a unique first-party signal How Lyft balanced rider experience with monetization The unconventional cost-per-ride pricing model What’s next — from programmatic expansion to international growth Building culture in a startup-within-a-company environment 🎧 Whether you’re in ad ops, retail media, or digital strategy, this is a masterclass in how to build an advertising business from zero to one. 📍 Guest: Shane Dwyer, Head of Sales, Lyft Ads 📍 Host: James Deaker – The Yield Doctor 📍 Topics: ad tech | commerce media | programmatic | pricing | yield management

What can yield analysts really take away from Ari Paparo’s new book Yield? This isn’t a book review — it’s about the gold inside the book that every pricing or yield analyst should highlight and study. In this video, I break down 4 key lessons that will make you sharper and faster in digital advertising yield management. If you’re new to the industry, or even if you’ve been around a while, these insights will help you connect the dots between theory, practice, and results. Related: The book review of this book: https://youtu.be/C_Yym-Xf_6w

Why is yield management so much harder in digital advertising than in airlines or hotels? In this video, I break down why pricing and yield management in digital media is uniquely complex — from the explosion of ad formats, currencies, and targeting options, to the lack of market transparency and the fragmented supply chain. You’ll see: A real-world example using the Yahoo homepage How auctions, banners, video, native, and affiliate deals fit together Why price response in advertising differs so much from airlines What publishers and ad tech companies can learn from the broader pricing industry I first shared these ideas at Pricing Week, a conference for pricing professionals — but I’ve reworked them here specifically for publishers, ad tech teams, and anyone working in digital media. 👉 Even if you’re an expert in digital advertising, you’ll want to stay until the end, where I highlight what our industry can learn from the discipline and rigor of traditional pricing teams. About me I’m James Deaker — The Yield Doctor. On this channel, we explore frameworks, stories, and strategies that help publishers and ad tech companies unlock more value from their inventory. If you found this useful, don’t forget to like, subscribe, and share it with your team.

In this video, I review Ari Paparo’s new book Yield: How Google Bought, Built, and Bullied Its Way to Advertising Dominance. As “The Yield Doctor,” I’ll share my perspective on: Who should read this book The key themes and stories inside What it reveals about Google’s rise to power in ad tech Lessons that publishers, ad ops teams, and ad tech professionals can take away If you work in digital advertising, ad tech, or publishing, this book (and my take on it) will give you valuable context on how we got here—and what it means for the future. 👉 Subscribe for more content on yield management, ad tech strategy, and revenue growth for publishers.

Some of the biggest names in digital media still run blind ad networks—and advertisers keep buying. But why? In this video, I break down the differences between blind vs. transparent ad networks, showing how each model works, what advertisers see, and why publishers and network owners choose one over the other. You’ll learn: The key attributes of blind ad networks (and why definitions are shifting) How margins, transparency, and business models differ Examples of $100 advertiser spend flows in blind vs. transparent setups Why some tech, retail, and social platforms succeed with blind networks How publishers unintentionally create blind networks when extending audiences What to consider when deciding which model fits your strategy Whether you’re a publisher, ad network operator, or ad ops professional, this video will help you understand how these models impact trust, revenue, and long-term success. I’m James Deaker, The Yield Doctor. I’ve spent 20+ years helping publishers and platforms maximize yield through pricing, ad operations, and marketplace strategy. If you’re grappling with blind vs. transparent networks—or want tailored advice for your business—this is exactly what I do. 👉 Drop your thoughts in the comments, and subscribe for more insights on pricing, yield management, and ad tech strategy. 📺 Related video: https://youtu.be/qaSuicw9DIs

Ad tech vendors often warn publishers: “Don’t lower your price floors.” But what if that advice is holding you back from seeing your true demand curve? In this video, I explain why starting lower isn’t about giving away value—it’s about uncovering hidden demand, attracting new buyers, and making data-backed decisions that maximize long-term yield. You’ll learn: Why “optimal” floors from vendors may not align with your definition of success How first-price auctions have changed the dynamics of bidding and floors The overlooked benefit of discovery—for both publishers and advertisers How to run temporary floor tests without devaluing your inventory Common traps like buyer anchoring and perceived value (and how to avoid them) Lowering floors isn’t a race to the bottom. It’s a strategic experiment to reveal demand you can’t see when floors are set too high. Start low, learn quickly, and then raise floors with purpose. If you’re leaving money on the table because of high floors—or relying solely on “auto-optimized” exchange settings—this video will show you why it’s worth grabbing the wheel yourself. 👉 Watch to learn how to test, optimize, and drive higher long-term yield.

When publishers move to programmatic, one of the biggest fears is losing control. Will buyers cherry pick your best inventory and leave you with the scraps? Will premium deals dry up? In this video, I break down: What “cherry picking” means in ad tech and programmatic advertising Why it’s a real risk — and when it’s not How smart channel management can protect your premium sales while unlocking new demand Using a simple orchard-and-cherries analogy, I show how publishers can set guardrails, balance legacy sales with new revenue streams, and avoid the trap of over-protecting inventory at the cost of growth. Chapters: 0:00 – The fear of cherry picking in programmatic 0:49 – Cherries as an analogy for ad slots 1:45 – Premium sales vs. self-pick model 2:27 – Guardrails to protect premium deals 3:16 – What channel management looks like in ad tech 3:46 – The risk of over-protection 4:48 – When inventory shouldn’t go programmatic 5:20 – Balancing direct sales with programmatic

What makes a deal desk truly successful? Faster approvals? Higher margins? Fewer escalations? Be careful—optimizing for the wrong metric can backfire. In this (5-minute) Yield Doctor episode, James Deaker explains why many common deal desk KPIs are Goldilocks metrics—they shouldn't be too high or too low. Learn how to monitor approval rates, turnaround time, and margin without turning them into dangerous performance goals. Discover why dashboards are better than dogma when it comes to deal desk success. 👉 If you're a publisher, platform, or pricing leader trying to improve your yield, subscribe for more insights or reach out for consulting support. 🔗 More from The Yield Doctor: youtube.com/@jamesdeaker

The Perils of Predictable Discounting | Yield Story with The Yield Doctor In this Yield Story, James Deaker (The Yield Doctor) shares a cautionary tale about the dangers of predictable discounting and pulling revenue forward — a trap that many pricing and yield leaders fall into. Based on real (but anonymized) consulting experiences, this story dives into how one company’s quarter-end discounting habits trained customers to delay purchases, eroded pricing power, and created operational chaos. You’ll learn: ✅ What "pulling demand forward" really means ✅ How predictable discounts damage long-term revenue ✅ Practical strategies to break the cycle and retrain your customers ✅ Why sales incentives must be designed with caution If you're a pricing, sales, or yield management leader, this is a must-watch. 🔔 Subscribe for more insights on digital pricing, yield strategy, and monetization: [Your Channel URL] 📧 For consulting inquiries, reach out at [Your Email or Website] #PricingStrategy #YieldManagement #RevenueGrowth #B2BSales #EndOfQuarter #Discounting #TheYieldDoctor

What Do CMOs Really Care About? | A Candid Conversation with Lauren Weinberg What does a CMO actually look for in a media partner? And what publisher metrics matter... or don’t? In this exclusive interview, James Deaker (The Yield Doctor) sits down with Lauren Weinberg—former CMO at Square and Peloton, and co-author of Self-Made Boss—to explore how top marketing executives make media investment decisions. Lauren shares tactical insights on: ✅ How CMOs evaluate publisher performance ✅ What drives budget shifts mid- and post-campaign ✅ The role of MMM, MTA, and incremental ROI ✅ Why impressions and CPMs may not mean what you think ✅ What actually sets a media pitch apart Whether you're a publisher, ad tech pro, or just trying to understand how to win a marketer’s trust, this is a must-watch conversation. 🔗 Connect with Lauren: https://www.linkedin.com/in/laurenweinberg13/ 📘 Her book: Self-Made Boss Chapters 0:00 – Introduction: What CMOs value in publishers 0:56 – Audience fit and planning considerations 2:22 – Metrics that matter: Performance vs. brand campaigns 4:48 – How brand media impacts lower-funnel performance 5:30 – Mid-flight vs. post-campaign optimization 7:49 – Decoding MMM and MTA 8:30 – Publisher metrics: What’s meaningful vs. table stakes 10:14 – Disconnects between CMOs and publishers 12:03 – Do CMOs care about “premium” publishers? 13:06 – Custom ad formats: Barrier or opportunity? 15:10 – A winning pitch: Peloton × Twitch case study 17:30 – CPMs vs. ROI: What really matters 19:05 – The reality of measuring incremental ROI 20:25 – Wrap-up and how to follow Lauren

Why don’t publishers have good pricing software anymore? Back in 2007, Yahoo, AOL, and Microsoft used a tool called Rapt to optimize rate cards in real time. Today? That software is gone—and no one has replaced it. In this video, I explain why Rapt disappeared, why nothing has filled the gap, and what it would take for a new solution to emerge. Drawing on my own experience working at Rapt, I break down the key challenges—including programmatic dominance, product complexity, and the lack of transparent market signals. 📈 If you're a publisher or ad tech leader grappling with pricing, yield, or programmatic strategy, this one’s for you. ⏱️ Chapters 00:00 – The 2007 Era: When Rapt Ruled 00:20 – Why I Know This: My Time at Rapt 00:47 – What Pricing Software Should Do 01:32 – How Rapt's Price Director Worked 02:16 – Microsoft Acquisition and Decline 02:23 – Why No One Replaced It: My Take 02:34 – Reason #1: Rise of Programmatic 03:25 – Reason #2: No Public Pricing Signals 04:11 – Reason #3: Ad Products Are Too Complex 05:38 – The Need for Customization (Compliance) 05:58 – Why Ad Tech Hasn’t Solved This Yet 06:16 – What the Future Might Look Like 06:35 – Closing Thoughts & Call to Comment 🔔 Subscribe to stay ahead of yield management and ad tech trends: youtube.com/@jamesdeaker 📬 Need help with pricing, inventory, or programmatic strategy? Reach out via LinkedIn or at theyielddoctor.com #DigitalAdvertising #YieldManagement #AdTech #RateCards #JamesDeaker #TheYieldDoctor #Programmatic #PricingSoftware

There’s a lot of hype around AI in ad tech — but where is it really having an impact today? In this episode, I speak with Nagarajan Chakravarthy, Chief Digital Officer and founding team member at iOPEX Technologies. We go deep into practical examples of AI transforming advertising revenue operations — from media planning to campaign optimization, and even creative generation. You’ll hear: What “Agentic AI” really means in Ad Tech Which parts of the ad ops workflow are being automated The biggest cultural and organizational blockers to adoption Why AI may actually level the playing field for smaller advertisers 📧 Contact Naga: [email protected] 🌐 Learn more: https://www.iopex.com — Timestamps: 0:00 - Introduction: Why AI in ad tech needs more real examples 1:01 - Meet Nagarajan Chakravarthy, CDO at iOPEX 2:07 - What iOPEX does and their approach to transformation 3:35 - What are “command agents” and how do they work? 3:52 - AI use cases in advertising revenue operations 6:41 - How AI accelerates creative and campaign execution 7:35 - From media plan to standardized IOs 8:02 - Speed vs. oversight: the new trade-off 10:08 - Skill shifts: From execution to prompting 11:08 - Quality control and changing error patterns 13:02 - Real-world example: AI-generated media plans 16:07 - Where AI still falls short: the EQ of creative 17:22 - Where human judgment remains indispensable 19:02 - Common misconceptions about AI in ad tech 21:31 - 4 areas where AI will be most disruptive 24:35 - Internal blockers: Skills, data, and expectation-setting 27:39 - Will AI level the playing field or boost the giants? 29:41 - Why AI is a net positive for publishers 31:38 - Final thoughts and how to connect with Naga — If you enjoyed this video, please like and subscribe. More content coming soon from the front lines of ad tech and monetization.

Meta Goes All-In on AI Ads: What It Means for Advertisers & Agencies Meta just announced plans to fully integrate AI into its advertising platform by the end of next year — enabling brands to create and target ads using artificial intelligence with little to no manual input. In this video, I break down what this shift means for: Facebook/Meta advertisers Agencies whose value lies in creative and targeting The broader digital advertising ecosystem I’ll also share my perspective on how this move fits into the long-term trend of AI in ad tech, and why it might give Meta — and other walled gardens — an even greater advantage. Timestamps: 0:00 – Meta’s AI Ad Announcement 0:42 – Implications for Facebook Advertisers 2:10 – How Agencies Should Respond 3:45 – Industry-Level Disruption 5:20 – My Speculation: What Meta Might Do Next 6:30 – Final Thoughts + Your Input If you’re in ad ops, media buying, or agency strategy — this one’s for you. Let me know in the comments: Do you think this makes Meta more effective — or more dangerous?

In this episode of The Yield Doctor, James Deaker sits down with Alanna Laforet—CEO of Laforet Productions and former head of the IAB Tech Lab—to unpack where AI is actually transforming digital advertising today, and where it's headed next. With experience spanning DoubleClick, Google, Omnicom, and Conde Nast, Alanna brings deep insight into how AI is reshaping media planning, creative optimization, and the ad tech stack itself. We explore: What’s real vs. hype in current AI implementations How startups and holding companies are approaching AI differently Whether AI will democratize the industry or entrench the big players The potential for AI tools like ChatGPT to act as publishers The risks of fraud, fake content, and over-optimization in a post-AI world Whether you're a publisher, advertiser, or just curious about the future of ad operations, this conversation is packed with insight and candid perspective. 📌 Chapters included in the description below 🔗 Connect with Alanna Laforet: LinkedIn: [Insert Link] Email: [email protected] Alanna discusses the areas where GenAI will have the most impact on the AdTech ecosystem 00:00 Introduction & Guest Background 01:07 Current AI Impact in Digital Advertising 03:01 Real AI Adoption Inside Companies 04:45 Startup Frenzy & Market Fragmentation 06:00 The “Layer Cake” Problem in Ad Tech 08:13 Toward End-to-End Systems with AI Plugins 09:45 Where AI Will Have the Most (and Least) Impact 11:08 Could AI Platforms Become the Next Big Publishers? 13:36 Democratization vs. Walled Gardens 15:33 AI and the Return of Imagination 16:54 Publishers: Winners or Losers in the AI Shift? 17:38 The Ethics & Dangers of AI-Generated Content 19:53 What Excites Alanna About AI's Future 21:31 Where to Find Alanna + Closing Remarks

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