iab-营销中的人工智能报告（英文）-2019.12-19页_595kb

报告摘要

AI in Marketing: A Summary of the IAB Guide

Core Content

This IAB guide explores the transformative role of artificial intelligence (AI) and machine learning in the advertising and marketing industry. It outlines the opportunities, applications, and best practices for leveraging AI in marketing, emphasizing the importance of data, responsible use, and industry collaboration.

Main Points

What is AI and Machine Learning?

AI is a broad field that enables machines to mimic human cognitive functions such as learning and problem-solving.
Machine Learning is a subset of AI that focuses on developing systems that can learn from data and improve over time.

AI in Advertising and Marketing

AI is now integral to the digital advertising landscape, with at least 80% of the digital media market using AI in advertising.
AI helps in performance optimization, personalization, automatic ad creation, audience targeting, and media mix modeling.
AI enables real-time feedback, predictive analytics, and automated decision-making, leading to more effective and efficient marketing strategies.

Benefits of AI

Ad precision and effectiveness: AI helps in profiling users and creating targeted campaigns.
Audience optimization: AI allows for hyper-targeting based on consumer behavior and long-term engagement.
Creative relevance: AI enhances ad content through natural language processing, sentiment analysis, and real-time customization.
Media efficiency: AI improves media mix modeling, bid optimization, and brand safety through anomaly detection and fraud prevention.
Fraud and brand safety: AI helps in detecting bots, identifying unsafe content, and ensuring brand safety.

Key Uses of AI in Advertising and Marketing

Performance optimization: AI analyzes ad performance and provides real-time recommendations for improvement.
Personalization: AI delivers highly personalized ads based on real-time behavioral data.
Automatic ad creation: AI generates ad copy and creative assets quickly and at scale.
Audience targeting: AI identifies and targets the most relevant audiences with precision.
Media mix modeling: AI helps in optimizing the media mix and improving ROI through continuous analysis of consumer responses.

Real-World Examples

Norwegian Airlines: Used AI to drive flight bookings with a 170% lower cost-per-booking.
The Humane Society: Achieved an 86% lift in conversion rates using relationship targeting.
Toyota Prius Prime: Increased engagement by 37% using cognitive ads.
Campbell's: Saw a 1.9x increase in desktop ingredient submissions and a 27% increase in mobile video completion.
Behr: Achieved a 17% increase in purchase consideration and an 8.5% lift in foot traffic.
Best Western: Recorded a 48.6% increase in visits using AI-driven audience targeting.
Vodafone: Increased share of voice by 67% through AI-powered in-image ad overlays.
IKEA: Achieved a 7.68% engagement rate and a 58.3% reach metric using voice-enabled interactive ads.

Best Practices & Key Takeaways

Evaluate

Clearly define the business problem and desired outcome.
Ensure the AI solution actually addresses the problem.
Understand the data inputs and how they impact the results.
Assess the quality and reliability of data used.
Explore new data sets to improve modeling accuracy.
Monitor model scoring over time to understand adaptation and effectiveness.

Experiment

Be flexible and open to new discoveries.
Conduct small pilots to test AI solutions with minimal risk.
Test in stages to refine before full-scale implementation.
Be aware of and address potential biases in data and models.
Learn from both successes and failures.

Conclusion

AI is reshaping the advertising ecosystem, enabling brands to engage with consumers more effectively and efficiently. As AI continues to evolve, the industry must establish standards and ethical guidelines to ensure responsible use and protect consumer data. The future of marketing will be driven by AI, with the potential for even greater innovation as technologies like 5G and IoT mature.

Additional Resources

IAB Report on How to Build a 21st Century Brand
eMarketer AI Best Practices Guide
Adobe Digital Insights Reports
IAB AI Working Group Publications

About the Report

Authored by: IAB AI Working Group, with 115 members contributing insights.
Purpose: To provide clarity on AI in marketing, outline best practices, and address ethical and privacy concerns.
Collaboration: Led by Matthew Groner (AdTheoret) and Antonio Tomarchio (Cuebiq), with contributions from various industry experts and partners.

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