Flo's Notes

Marketing in the Age of AI

3 min read

Hand-crafted by Flo, auto-checked by AI.

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According to the AI index report, AI capability is accelerating and reaching more people than ever. The jagged frontier of AI: they may be extremely accurate or intelligent in complex tasks, but lack the ability to complete basic ones (eg multimodal LLMs reading analog clocks).

AI in Marketing

Marketing is a highly relevant field for AI applications, as it’s heavily data-driven and personalized. In essence, marketing is a transformer of company and consumer values.

  • Adoption: Highest company-weighted AI adoption (18% of firms).
  • Market Growth: AI in marketing predicted to reach $110B by 2028.
  • Investment: Top priorities: GenAI (50%), MROI measurement (42%), and consumer insights (38%).

Predictive vs. Generative AI

  • Predictive AI: Image classification (e.g., Chihuahua vs. muffin) and forecasting.
  • Generative AI: Content creation (Text, Image, Video, Audio, Code).
    • Text is most adopted (63%), followed by Images (36%).
    • Impact: AI-generated SEO content can outperform human experts. Enables personalization at scale.

Creative Industry Examples

AI boosts productivity by automating creative tasks:

  • Kraft Heinz: Uses TasteMaker, a “promptless” GenAI platform (Vertex AI).
  • Coca-Cola: “Real Magic” platform for unique AI artwork.

Performance & Perception

  • Real-world Evidence: AI images match or beat human performance if they don’t look like AI.
  • The Face Effect: Images with more facial area appear more “human”.
  • Cost Efficiency: Massive gains. AI visuals (10) or freelancers ($100).

Guest Speaker

Fabian Uhrich, CPO at Quicklizard. Quicklizard provides dynamic pricing software for e-commerce, using AI to optimize prices in real time.

Customer Perspective: Online Shopping Development

  • Wave 1: Category Browsing (1990s): Navigation via “digital shelves”. Static pricing relying on MSRP and simple discounts.
  • Wave 2: Search and Filter (2010s): High price transparency. Emergence of dynamic pricing algorithms. B2C focus on price matching; B2B on e-procurement portals.
  • Wave 3: Discovery Commerce (2020s): Shift to peers, creators, and reviews. Pricing less important than perceived fairness or values. 34% of customers switch brands to “feel special” despite higher prices.
  • Wave 4: AI-Agent Shopping (On the Rise): AI becomes the buyer. Pricing data must be structured and defendable. 75% of consumers are open to using shopping agents.

The AI Triple Play at Quicklizard

  1. Build: Rapid prototyping via “vibe coding” with clients.
  2. Interact: Chatbots (Pricing Guru) for sparring, natural language filtering, and Python co-piloting.
  3. Impact: Self-learning algorithms that constantly retrain for better pricing and ROI.

Setting Better Prices

Models estimate price/cross-elasticity to maximize revenue and competitiveness.

The pricing engine is a fly-wheel:

  1. Elasticity: How demand changes when price changes.
  2. Cross-Elasticity: How changing the price of one product affects sales of others (cannibalization/complements).
  3. Competitor Sensitivity: How much customers actually care about competitors’ prices for a specific item.
  4. Segmentation: Grouping products/customers based on behavior to apply different strategies.
  5. Seasonality: Adjusting models for time of year, holidays, or events.
  6. Forecasting: Predicting future demand and the outcome of pricing decisions.

This feeds back into the start, automatically adapting to markets and human corrections.

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