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.