Reality-check your ad or marketing idea with a GPT trained on 1,050+ behavioral science studies and ad effectiveness books grounded in research from IPA, Ehrenberg-Bass, and more.
Add as much context as you like. More detail means sharper evaluation.
Below are the core sources this GPT was trained on - spanning 1,050+ behavioral science studies and the most respected ad effectiveness research from IPA, Ehrenberg-Bass, and more.
Summary: Challenges traditional marketing assumptions and presents laws of brand growth based on empirical data.
Data/Methodology: Draws on 50+ years of quantitative research and data from thousands of brands and categories (Ehrenberg-Bass Institute for Marketing Science). Based on large-scale market data across global FMCG and service sectors.
Summary: Extends the original thesis to categories like luxury, services, and emerging markets, affirming core growth principles apply broadly.
Data/Methodology: Uses international datasets from hundreds of brands across five continents to confirm the universality of brand growth laws.
Summary: Shows the complementary roles of brand-building and activation, emphasizing that long-term emotional campaigns drive sustainable growth.
Data/Methodology: Based on over 1,000 case studies from the IPA Databank (Institute of Practitioners in Advertising). Uses effectiveness scoring and business metrics to reveal patterns in campaign outcomes.
Summary: Identifies which metrics and campaign strategies most predict profitability and business success.
Data/Methodology: Meta-analysis of 880 IPA case studies using econometric modeling and effectiveness benchmarks.
Summary: Argues that mental availability is built through consistently used brand assets like logos, colors, and characters.
Data/Methodology: Based on 25+ empirical recognition studies across 15+ categories. Introduces structured testing of brand asset distinctiveness.
Summary: Explores 25 behavioral biases that affect buying decisions and how marketers can apply them.
Data/Methodology: Synthesizes findings from 100+ behavioral science experiments and academic studies, contextualized with real-world marketing scenarios.
Summary: Connects neuroscience, behavioral economics, and marketing to explain how people really make decisions.
Data/Methodology: Uses fMRI research, implicit association testing, and controlled experiments with 1,000+ participants to link decision-making to brand cues.
Summary: Shows that effectiveness depends on context, sector, brand size, objectives—offering guidance on tailoring strategy.
Data/Methodology: Segments 1,400+ case studies from the IPA Databank by context variables to show when and how general principles should be adapted.
Summary: Examines which media channels drive the strongest long-term business effects.
Data/Methodology: Draws from 700+ IPA case studies, comparing the long-term business impact of various media channels through econometric analysis.
Summary: Argues that creative attention has shifted to left-brain styles (narrow, repetitive), and urges a return to right-brain elements (humor, narrative, humanity).
Data/Methodology: Combines IPA case data with over 30 facial coding and eye-tracking studies to assess attention patterns and effectiveness outcomes.
Summary: Proposes a behavioral model for changing behavior through action rather than attitude change.
Data/Methodology: Synthesizes 50+ behavioral science and psychological studies along with campaign case examples to demonstrate behavior-led strategy.
Summary: Demonstrates that the most creatively awarded campaigns are also the most effective.
Data/Methodology: Correlates 20 years of Cannes Lions and IPA Effectiveness Award winners to analyze the relationship between creativity and business impact.