『AI & Marketing Research with Dr. Eva Wolf』のカバーアート

AI & Marketing Research with Dr. Eva Wolf

AI & Marketing Research with Dr. Eva Wolf

著者： Dr. Eva Wolf
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Evidence-led briefings that translate peer-reviewed studies and important preprints on artificial intelligence, generative AI, marketing, advertising, consumer behavior, and business strategy into practical insight. Dr. Eva Wolf explains what the evidence actually says, what deserves a deeper read, and what marketers, consultants, educators, and business leaders can do next.

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  • AI Ads Inside AI Answers, Self-Evolving Ad Systems & Health AI

    AI Ads Inside AI Answers, Self-Evolving Ad Systems & Health AI
    2026/08/28
    What if the ad slot you're bidding on today becomes irrelevant — not because clicks drop, but because AI generates the answer before users ever see a search result? And what if your customers are already using ChatGPT to research your product before they talk to anyone on your team — and not telling a soul? In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering token-level advertising inside AI-generated answers, autonomous AI-driven ad system optimization, and hidden consumer AI behavior in the healthcare journey. What you'll learn: - How a proposed auction system would let brands bid — word by word — to appear naturally inside AI-generated answers, not beside them - Why a purpose-trained AI model roughly doubled the success rate of senior human experts at improving an ad recommendation system - Why most survey respondents said they used ChatGPT to research health questions before a doctor visit — but didn't mention it to their physician - What the hidden AI research stage in the consumer journey means for health marketers right now - Why off-the-shelf tools like GPT-5.5 underperformed badly at specialized ad optimization, and what that suggests for how you build internal AI tools Papers covered: 1. Token-Level Advertising Authors: Hanbing Liu, Bowei Zhang, Changyuan Yu, Yinyu Ye, Qi Qi Source type: Preprint (not yet peer-reviewed) Access: Full text reviewed Source: https://arxiv.org/abs/2608.27382v1 2. Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems Authors: Jinxin Hu et al. Source type: Preprint (not yet peer-reviewed) Access: Full text reviewed Source: https://arxiv.org/abs/2608.27287v1 3. Generative AI use before medical visits: disclosure-item responses, trust, and care-seeking behaviors in a cross-sectional social-media survey in Poland Authors: Simona Wójcik, Anna Rulkiewicz, Justyna Domienik-Karłowicz Source type: Peer-reviewed journal article (Frontiers in Digital Health) Access: Full text reviewed DOI: 10.3389/fdgth.2026.1933451 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-token-advertising-self-evolving-ad-systems-health-ai-2026-08-28 Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a final academic review. Preprints have not been peer-reviewed and findings may change. All claims are attributed to the cited papers; listeners should consult the original sources before acting on any findings. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

    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.
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    20 分
  • AI Marketing Research: Gen Z Trust, Ad Forecasting & LLM Ads

    AI Marketing Research: Gen Z Trust, Ad Forecasting & LLM Ads
    2026/08/27
    Are brands that openly explain how their AI works actually winning more trust from Gen Z consumers? Can your forecasting tools simulate what happens if you change your ad budget — or just tell you what already happened? And what might it look like to buy ads inside ChatGPT or Perplexity based on the meaning of a conversation rather than a keyword? In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering Gen Z consumer trust, AI-driven demand forecasting, and advertising auction design for large language model interfaces. We screened 388 papers to get here. What you'll learn: - Why Gen Z consumers in one study responded more positively to brands that explained their AI — and what the study's limitations mean for how much you should act on it - Why your current forecasting tools can tell you what happened but likely cannot tell you what would happen if you changed your ad spend - What AI-native advertising auctions could look like inside conversational AI tools — and why they would behave differently from keyword auctions - The difference between treating AI transparency as an ethics checkbox versus a conversion lever - Why separating the effect of your ad budget from the effect of an external event matters for accurate campaign attribution Papers covered: 1. Building Gen Z Consumer Trust Through Transparency in AI-Driven Marketing Source type: Peer-reviewed journal article (Journal of Advance and Future Research, JAAFR) Peer review status: Likely peer-reviewed Access: Full text reviewed Radar verdict: Use cautiously DOI: 10.56975/jaafr.v4i8.513942 2. CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition Source type: Preprint (accepted at KDD 2026 — not yet fully peer-reviewed at time of recording) Access: Full text reviewed Radar verdict: Test this week Preprint: https://arxiv.org/abs/2608.25871v1 DOI: 10.1145/3770855.3818338 3. The Power Diagram Auction: A Formally Verified VCG Mechanism for LLM Advertising Source type: Preprint (Zenodo — not peer-reviewed) Access: Full text reviewed Radar verdict: Watchlist DOI: 10.5281/zenodo.21723923 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-gen-z-trust-demand-forecasting-llm-advertising-2026-08-27 Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on Dr. Eva Wolf's research framework and methodology. It is not a substitute for full academic review. Findings are summarized for informational purposes. Always read the original papers before making business decisions. Preprints have not completed peer review and should be treated with additional caution. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

    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.
    続きを読む 一部表示
    19 分
  • AI Workflow Gaps, Brand Equity & Content AI: 3 Research Signals

    AI Workflow Gaps, Brand Equity & Content AI: 3 Research Signals
    2026/08/26
    If your AI tool can quote a key finding from a document word-for-word, does that mean it actually used that fact when making a recommendation? This week's radar brief surfaces three recent papers that all point to the same uncomfortable pattern: the gap between what AI can do and what it actually delivers in practice is almost always a workflow problem — not a model problem. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI content production for small teams, AI tools as brand-building assets in higher education, and how AI document analysis workflows may silently discard information your team assumes is being used. What you'll learn: - How one startup combined ChatGPT and Copy.ai with a two-week sprint structure to post more consistently on social media — without hiring more people - Why pairing AI content tools with free analytics like Meta Business Suite creates faster feedback loops for small marketing teams - How a university's custom-branded AI assistant may function as a brand touchpoint — shaping student perceptions of quality and loyalty — not just an IT feature - Why an AI that can accurately retrieve a fact from a long document may still completely ignore that fact in its final recommendation - How chunk-and-summarize pipelines may be silently discarding information your team assumes the AI is using Papers covered: 1. Implementation of Generative AI for Digital Marketing and Social Media Content - Source type: Peer-reviewed journal article (Journal of Applied Engineering and Social Science) - Access: Full text reviewed - DOI: 10.25124/jaess.v4i1.11157 - Radar verdict: Test this week 2. Reconceptualizing Higher Education Marketing in the Algorithmic Era: Institutional Generative AI and Multidimensional University Brand Equity - Source type: Peer-reviewed journal article (low-profile venue — treat findings with caution) - Access: Full text reviewed - DOI: 10.5281/zenodo.21169440 - Radar verdict: Use cautiously 3. Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows - Source type: Preprint — not yet peer-reviewed - Access: Full text reviewed - Source: https://arxiv.org/abs/2608.24842v1 - Radar verdict: Test this week Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-workflow-gaps-content-ai-brand-equity-document-retrieval-2026-08-26 Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a substitute for a full academic review. Findings represent what the papers suggest, not what is proven. Preprints have not been peer-reviewed and should be treated with additional caution. Always read the original papers before making strategic decisions. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

    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.
    続きを読む 一部表示
    21 分
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