MuratcanKoylan/MarketingStructuralPrompts

README.md

Enhancing Large Language Model Performance in Digital Marketing Strategies with a Specialized Prompt Dataset

Creator: Muratcan Koylan

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About the Dataset

This dataset, comprising 4,643 specialized prompts across various categories of digital marketing, aims to enhance the performance of Large Language Models (LLMs) like GPT-3 in generating accurate, relevant, and industry-specific marketing strategies. 30 Paid Search Prompts 15 ROAS Prompts 45 Facebook Ads Prompts 13 Google Remarketing Prompts 15 Ad Network Prompts 14 Linkedin Ads Promtps 14 Advertising Budget Prompts 16 Quality Score Prompts 14 BING Ads Prompts 15 Classified Advertising Prompts 20 CPM Prompts 15 X (Twitter) Prompts 15 CPC Prompts 15 PPC Prompts 15 Instagram Ads Prompts 15 Youtube Ads Prompts 15 Google Ads Prompts 15 Programmatic Advertising Prompts 15 Remarketing Promtps 15 CPV Prompts 15 Reach Promtps 15 CPL Prompts 15 Ad Rank Prompts 15 Interstitial Prompts 15 Ad Sense Prompts 15 SEM Prompts 20 Affiliates Prompts 15 Dsiplay Advertisement Promtps 20 Video Ads Promtps 20 Mobile Ads Prompts 20 TikTok Ads Promtps 20 Pinterest Ads Prompts 20 Shopping Ads Promtps

Dataset Composition:

• StrategyDomain: Main category representing the broader strategic area of digital marketing.
• TacticScope: Sub-category focusing on specific tactics within the StrategyDomain.
• StrategicPrompt: The actual marketing prompt text designed to simulate real-world marketing scenarios.

Methodology:

The dataset represents a synergistic fusion of human expertise and advanced AI technology, blending 30% human-generated content with 70% synthetic data crafted using cutting-edge generative AI models like GPT-4, Claude2, and LLama2. This approach strategically leverages the nuanced creativity and contextual understanding of human input, while exponentially expanding the dataset's breadth and depth through AI's vast generative capabilities. This methodology ensures the dataset embodies both the rich, detailed insights of human marketing experts and the diverse, innovative perspectives that AI models can offer.

Applications:

• Fine-Tuning LLMs: This dataset is pivotal for refining LLMs to produce more targeted, effective marketing strategies. By exposing LLMs to a diverse array of real-world marketing scenarios, they become adept at crafting nuanced and strategically sound solutions.
• Marketing Campaign Development: A valuable tool for marketers, this dataset aids in the ideation and development of comprehensive marketing campaigns, offering inspiration and strategic guidance.
• Training AI Agents: Ideal for training AI agents to autonomously handle various digital marketing tasks, this dataset can drive efficiency and innovation in marketing automation.
• Cross-Domain Potential: Beyond marketing, this dataset's structure and approach hold potential for adaptation and application in sectors like finance, healthcare, and education, where specialized language models can offer significant value.

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Experimental Results

Upon rigorous testing against standard LLM benchmarks, the dataset has demonstrated remarkable improvements in producing strategically relevant, creatively rich, and platform-specific accurate marketing content. These results underscore the dataset's efficacy in enhancing the contextual and strategic understanding of LLMs within the realm of digital marketing. Results will be shared in the near future with a proper paper.

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Future Directions

Looking ahead, the goal is to continuously evolve and enrich this dataset, incorporating emerging marketing trends and novel concepts. This ongoing development aims to broaden the dataset's utility, making it an indispensable tool for future LLM applications in digital marketing and beyond, including potential cross-disciplinary applications that push the boundaries of AI's role in various professional fields.

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Contact and Collaboration

As a fervent advocate for AI-driven innovation in marketing, I welcome collaboration and dialogue with fellow AI enthusiasts, marketers, and builders. My aim is to foster a community of like-minded professionals who are passionate about exploring the frontiers of AI in marketing. Reach out to me on X (@youraimarketer) for any collaboration ideas, discussions, or queries regarding this dataset.

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Acknowledgments

This dataset stands as a testament to the power of collaborative innovation, combining the best of human creativity and AI's transformative capabilities. A heartfelt thank you to all the contributors, including AI developers, data scientists, and marketing experts, whose collective efforts have brought this project to fruition.

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