AI & Marketing Research with Dr. Eva Wolf
Not another AI news podcast. This is a research radar — a twice-weekly briefing that surfaces peer-reviewed studies on AI and marketing, tells you what the evidence actually says, and helps you decide what's worth a deeper read.
AI & Marketing Research with Dr. Eva Wolf
AI Pricing Algorithms, LLM Bias & Ad Retrieval: 3 Research Signals
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
Thanks for listening to AI & Marketing Research Radar by Big Plans Media.
I’m Dr. Eva Wolf, and I help marketers, educators, consultants, and business owners turn AI marketing research into practical strategy, smarter workflows, and real business opportunities.
More episodes: https://bigplans.media/ai-marketing-research-radar/
Consulting: https://bigplans.media/ai-marketing-consulting/
Big Plans Media — Where Big Ideas Meet Smart Marketing.
You're listening to Evita, an AI-generated research briefing avatar trained on the research framework and methodology of Dr. Eva Wolfe, marketing professor, AI researcher, and founder of Big Plans Media. Every day, Evita scans emerging research in AI, marketing, consumer behavior, psychographics, and business strategy to identify the most relevant developments, opportunities, and risks worth watching. These daily radar reports are designed to help busy professionals stay informed without having to read hundreds of research papers themselves. And every Friday join Dr. Eva Wolfe Live for her personally recorded weekly AI Marketing Radar Roundup, where she breaks down the biggest stories, explains what actually matters, and shares practical insights and strategic implications for marketers, educators, entrepreneurs, and business leaders. Now, here's today's radar report. Here's the signal I can't ignore today. AI is setting your prices. AI is writing your ad copy. AI is deciding who sees what. And most marketers have no idea what those systems are actually doing with their information or who they were built to serve. Today's papers point to the same pattern. The AI tools running your marketing are making decisions based on inputs and training assumptions you probably haven't audited. We screened 374 papers. Three cleared the full text bar and made the radar. Quick caveat: this is a first pass research briefing, not a final academic review. Every paper today has full text access. I'll tell you what the papers suggest, what they don't prove, and which ones deserve a deeper read. Okay, let's get into it. Paper one. Here's the business question. If your AI pricing tool is connected to a market data feed, is more information always better? Or could you be accidentally training your algorithm to collude? Wong and Ye built a theoretical model, two firms competing on price, both using Q-learning algorithms to set those prices automatically. Then they ran simulations under three information regimes: no market data shared, full market data shared, and a middle option they call upper censorship, where the algorithm knows when demand is low, but the exact height of a demand spike is hidden. Here's what they found. I want you to really sit with this. When the algorithms are patient, optimizing for long-run profit, cutting off their information access actually causes them to collude more, not less. More information makes them compete harder. Less information makes them huddle up. The opposite of what you'd expect. The opposite of what standard economics predicts. And that upper censorship structure, hiding the peaks, sharing the troughs, lets companies earn more profit than full transparency does. That's the finding that should make your legal team uncomfortable. Here's why it matters. Regulators trying to prevent AI-driven price collusion by restricting information sharing could make the problem worse, not better. And if you're running automated pricing, the data feed shaping your algorithm isn't just a technical detail. It's a competitive and legal exposure. That is not a configuration setting. That is an antitrust question. But here's the catch. This is a two-firm simulation using Q-Learning. Real markets have more players, differentiated products, and algorithms way more complex than Q-learning. The findings are provocative. They're not proven in the wild. Plain English payoff. The data your AI pricing algorithm receives shapes whether it competes or colludes. And more data doesn't always mean more competition. Okay, here's where this becomes commercially interesting. Money move. Build a pricing algorithm transparency audit. A consulting or SaaS product that maps exactly what market signals feed into an AI pricing engine and flags configurations that could attract antitrust scrutiny. Sell it to legal and ops teams, not just marketing. That's a premium service right now. Action step. If your company uses any automated pricing tool connected to a third-party data feed, pull up the vendor documentation and ask one question. What information does the algorithm receive and in what form? If your vendor can't answer that clearly, that's your first red flag. Evidence check. This is a preprint, not peer-reviewed, two-firm model only. Q-learning specifically, other algorithm types may behave completely differently. Do not restructure your pricing stack based on this. Use it to ask better questions. Radar verdict. Not because the evidence is rock solid, it isn't. Because the regulatory environment is moving fast, and an internal audit of your pricing data inputs costs almost nothing. This next one looks like an AI research paper. And technically it is, but the marketing implication is one of the most practically useful things I've come across in a while. Stay with me. Paper 2. Here's the business question. When you use AI to talk to your customers, email, chat bot, recommendations, is it actually built for your customers or is it built for someone else's? Garbasia wrote what's called a position paper. No new experiment. She synthesizes a large body of prior research and makes an argument. The argument. AI systems trained on averaged human feedback are systematically broken for anyone who isn't in the majority. Here's the mechanism. When AI developers train models like ChatGPT, they gather thousands of human ratings. Which response is better? Which feels more helpful? Then they average those ratings. But averaging hides disagreement. If 60% of raters prefer blunt, direct answers and 40% prefer nuanced ones, the model learns blunt, full stop. Those 40% are stuck. And it gets more pointed than that. Research cited in the paper shows that LLM opinions match liberal, educated Western populations about 0.3 points more than other demographic groups. So the aligned AI, aligned to what exactly? Aligned to one demographic. Not yours if your customers are global, older, non-English speaking, or just not in that sweet spot. So let me translate that. If you're using a mainstream AI tool to write for customers in Southeast Asia or for working class buyers or for technical specialists, the model is working against your brand voice every single time. That is not a diversity issue. That is a conversion rate issue. But here's the catch. This is a position paper. Garbasia is making an argument, not running an experiment. No head-to-head comparison of personalized versus averaged AI performance. The proposed solution, bounded personalization, is conceptual, not tested. And honestly, that bothers me a little. The argument is compelling, but I want empirical tests before I redesign anything. Plain English payoff. The AI writing your customer communications was trained to please the average user. And if your customer isn't average, it's quietly underperforming for them every single day. Okay, here's where this becomes commercially interesting. Money move. Build a preference profile prompt layer, a lightweight system that captures each user's tone preference, expertise level, and formality setting, and injects that into every AI interaction. Sell this as personalization middleware for enterprise chatbots or SaaS customer service tools. The demand is already there. The infrastructure hasn't caught up. Action step. Pick one AI-generated communication your team sends at scale. A welcome email, a chatbot greeting, a product description. Run it through a native speaker or cultural consultant for your top non-English speaking market. Count the mismatches. That's your audit baseline. Evidence check. Preprint. No peer review, no original empirical data. The 0.3 point demographic finding comes from a third-party study, not this paper. The theoretical framework is solid. The proof of concept is not here yet. Radar verdict. The argument is important and the marketing implications are real, but this needs empirical follow-up before you restructure your AI content workflow around it. This last one is more technical, but it explains something every performance marketer needs to understand about why paid and organic keep pulling in opposite directions. I'm telling you, the payoff is practical. Paper three. Here's the business question. Why doesn't optimizing your paid ads automatically improve your organic reach? And what does AI have to do with it? This is a survey paper, peer-reviewed, published in an engineering journal. The researchers reviewed the retrieval algorithms powering ad recommendation systems and organic content feeds. One thing I need to flag up front. I'm working from abstract level information here. So what do they find? The algorithm deciding who sees your ad uses fundamentally different math than the algorithm deciding who sees your organic post. Paid systems optimize for conversion. Organic systems optimize for engagement. Different objectives, different logic, different results. The dominant architecture here is something called a two-tower neural network. One tower learns everything about the user. One tower learns everything about the content. They're compared to find a match. And the paper flags that this same retrieval architecture is now being embedded into large language models, meaning the technology behind your ad targeting and the technology behind Chat GPT are quietly converging. That's the long run story, and it's one worth watching. But let me be clear about what this paper isn't. It doesn't tell you which platform uses what. It doesn't test performance, it maps the landscape. Here's where I'd be careful. Mid-tier engineering journal, abstract only access. So I'm not going to oversell it. What I can tell you is the underlying point is correct. Paid and organic retrieval logic diverge, and most marketers treat them as the same thing. Not the same. Different systems, different goals, different inputs. Plain English payoff. Your paid ad algorithm and your organic content algorithm have different goals at the machine level. And running the same strategy for both is leaving performance on the table. Okay, here's the business hiding inside the research. Money move. Offer a cold start accelerator service for brands launching new products on major ad platforms. The survey flags cold start as a major unsolved problem. New products have no data, so the algorithm underperforms early. A structured data seating and look-alike audience setup package sold as a launch sprint service fills that gap. Charge a premium for weeks one through four. Action step. Pull your last campaign. Look at your paid and organic performance data separately. If you ran the same targeting logic for both, that's your test. Run one campaign next cycle where paid and organic have explicitly different audience and format strategies. Measure the delta. Evidence check. Survey paper. No original data. Abstract only access for this briefing. Mid-tier venue. The LLM recommendation convergence point is flagged as emerging, not proven. Use this for orientation, not for redesigning your stack. Radar Verdict Watch list. The technical landscape it describes is real and relevant. But without full text access and with no original empirical findings, there's nothing to act on directly today. At first glance, these papers look separate, but together they show one thing. The AI systems running your marketing are making decisions based on inputs and assumptions that were set by someone else. And most of us have never audited them. Paper one says your pricing algorithm's behavior depends on what data it's fed, and the relationship is counterintuitive enough that even regulators can get it backwards. Paper two says your content AI was trained to please a demographic that probably isn't your customer. Paper three says your ad targeting and organic reach run on fundamentally different logic, and most teams treat them as one thing. The pattern? Not more AI. Better oversight of what the AI is actually doing. Not more automation. More visibility into the inputs. Not a rebuild, an audit. But here's the tension I keep coming back to. All three of these papers are early. Two are preprints, one is abstract only. The counterintuitive findings in paper one genuinely need peer-reviewed replication. The personalization argument in paper two needs empirical testing. The convergence claim in paper three needs a full text study to land properly. So the pattern is real. The evidence isn't solid enough yet to restructure anything major around it. What you can do right now is audit what you already have and start asking better questions of your vendors. That's the frame. Not panic, not overhaul, informed skepticism about the systems you're already running. Here's the playbook from today. One, if you use automated pricing with any third-party data feed, ask your vendor this week exactly what information the algorithm receives and in what form. If they can't tell you clearly, escalate it. Two, pick one AI-generated communication you send at scale and run it through a cultural or demographic reality check for your actual audience, not the training data's assumed audience. Three, look at your last campaign's paid versus organic data side by side. If you ran the same strategy for both, plan a split test next cycle with explicitly different logic for each. Evidence check on all of that. Two of today's three papers are preprints, and the third was abstract-only access. None of these findings are battle tested yet. Use them to decide what to test, not what to blindly believe. Links to all three papers are in the show notes. Read the originals before making major decisions. Want the human expert take? Join Dr. Eva Wolfe every Friday for the AI Marketing Radar Roundup, where she extracts no-nonsense money-making tips, practical strategy, and real business opportunities from the week's research. Subscribe on Apple Podcasts, Spotify, YouTube, and wherever you listen to podcasts. This is Evita for Big Plans Media, and I'll be back in the next radar brief.