Use of Generative AI by Ad Agencies in the Whole Marketing Funnel

1. Technology | Research & Analytics Services WHITEPAPER Use of Generative AI by Ad Agencies in the Whole Marketing Funnel How Generative AI is Transforming the World of Marketing and Advertising

2. Use of Generative AI by Ad Agencies in the Whole Marketing Funnel Introduction Popular Generative AI (Gen AI) interfaces, such as Bard, ChatGPT, Dall-E, and Stable Diffusion, are algorithms that allow users to create a wide variety of content in the form of videos, text, codes, audio, images, and simulations. Generative AI systems fall under machine learning (ML) and are not a burgeoning concept. The term was introduced in the 1960s and became significant when generative adversarial networks (GANs), an ML algorithm, were introduced in 2014 to create authentic audio, videos, and images of humans. Gen AI picks up patterns from the existing data and uses this information to produce new and unique outputs. Thus, AI can generate real and sophisticated content, which typically mimics human creativity, making it valuable for several industries. Recent breakthroughs in the field, such as Midjourney and Generative Pre-trained Transformer (GPT) have opened new possibilities for Gen AI and suggest drastic changes in the approaches to content creation. Marketing Touches New Heights with Gen AI Gen AI is an essential tool to thrive in today’s digital- first world. Most marketing and advertising agencies are now looking to embrace and adapt the tool to identify audience segments, create landing pages and social media posts, and develop outreach templates to reach prospects. While the new levels of sophistication of the technology have intrigued marketers, it has also raised valid concerns. Specifically, if Generative AI is trained on inaccurate or biased information, it may produce biased, offensive, or incorrect results. Therefore, it’s crucial to carefully curate the training data, fine tune GPT models, and monitor the AI’s outputs to prevent such issues. Marketers believe it holds great potential for rapidly creating personalized and text and images while reducing the time spent on mundane and repetitive tasks. It acts like an assistant to amplify creators’ work by supporting them with ideas, content creation, and assessment. For instance, PRophet, the first AI-focused software platform that sits atop OpenAI’s GPT-3, an autoregressive language model, by the marketing firm Stagwell, enables the PR community to analyze press outlets and predict media interests, sentiments, and reach before pitching a story. Likewise, BrandMuscle, a marketing firm, collaborates with organizations like Allstate and Bobcat to develop AI-driven content without human involvement while complying with a brand’s voice and message. According to a BCG April 2023 survey involving over 200 CMOs from eight countries in Asia, Europe, and North America, CMOs worldwide are positive and enthusiastic about Gen AI’s capability to create an edge and enhance productivity. Thus, marketers are extensively exploring Gen AI’s transformative power, benefits, and challenges, while helping companies execute new business models and introduce new products. The survey also highlights that “inaction is not an option,” and that marketers need to focus on Gen AI tools to be a “have” in an industry rather than a “have-not.” Therefore, marketers will have to balance between experimenting with Gen AI and mitigating the risks that come with it. Four key areas for marketers include deepening the understanding of Gen AI to redefine blueprints and operating models, recognizing and prioritizing use cases, instituting an enterprise-wide model, and creating an AI framework around ethical and legal considerations associated with Gen AI. AI tools focused on the advertising sector allow users to create captivating content, such as marketing copy, captions, web content, blog posts, social media ads, images, TikTok and YouTube video scripts, emails, and SEO content. These tools—designed to be highly collaborative—can be integrated with many data-driven tools and social platforms like Facebook to accelerate the content creation process and assess creative assets’ performance. contextually accurate 2

3. Use of Generative AI by Ad Agencies in the Whole Marketing Funnel Gen AI Use Cases for Marketing AI tools are uniquely flexible and scalable, alongside constantly learning and leveraging their enormous and dense neural networks in ways that humans cannot. Gen AI uses cases for marketing, including analytics, content creation, personalization, and improved briefing process. Content Creation Customer Service Text Gen AI employs large language models and NLP algorithms to automate content creation. These models derive patterns of gripping content by working with existing content styles and examples. In addition to producing unique ideas, AI-generated text can be useful for marketing in the form of emails, blogs, social media posts, product descriptions, videos, ad storytelling, and scriptwriting. It is a known fact that conversational AI has been successful in understanding and responding to customer queries faster than humans. Gen AI automates customer service models via readily available chatbots and messaging apps, email automation, multilingual support, and self-service portals that offer personalized solutions and recommendations to customers. Personalization Optimization Gen AI can personalize customer experiences at scale by fetching cues from real-time data and insights. For example, Gen AI can recommend products to consumers based on their past purchases and browsing history. Likewise, the technology can assist marketers in driving sales and customer satisfaction with offers based on their unique interests. Gen AI can help organizations detect SEO-friendly, powerful keywords and expressions for boosting their digital marketing campaigns. Regarding optimization, marketers can use Gen AI to discover new topics, find appropriate titles, direct keyword research, classify search intent, and create content frameworks. Automation Post-cookie World Generative AI can automate rote tasks for marketers, thereby helping them focus more on strategic activities like developing marketing campaigns. For instance, Gen AI can create leads from websites, social media, and other sources. The technology can also create diverse content like blogs, social media posts, and articles, while helping marketers enhance their click-through and open rates via personalized email campaigns. Gen AI can also promote brand awareness and reach wider audiences with the help of attractive social media posts. A post-cookie future is imminent as platforms like Apple, Firefox, Google Chrome, and Safari begin to phase out third-party cookies. This is a huge concern for marketers who may feel that such restrictions may reduce targeted ads’ performance. However, Gen AI assists in code generation, thus freeing developers and programmers from strenuous tasks like bug discovery, code optimization, and code completion, and helps them focus more on activities requiring human intervention. Image and Video Creation Gen AI can automate image creation with both generative adversarial networks (GANs) and deep learning (DL) algorithms. In marketing, Gen AI can create close-to-real images of products for social media channels and other marketing materials. It can also develop logos for visual branding, while crafting engaging advertising. Gen AI video tools also assist in developing superior product demos and marketing videos, contributing to increased brand awareness and conversions. 3

4. Use of Generative AI by Ad Agencies in the Whole Marketing Funnel The Rush for Gen AI – AI Chatbots to Create Advertising Jingles Betting big on Gen AI, WPP looks at offering a competitive edge to marketers looking at AI to scale advertising practices to meet the breakneck pace of digital marketing. One of the world’s most prominent advertising agencies recently collaborated with chipmaker Nvidia to use Gen AI to create advertising campaigns. Claiming to be an early adopter of the technology, WPP will leverage software, such as Getty Images and Adobe’s Substance 3D and Firefly products, to build more lifelike and brand- focused videos and imagery. Omnicom Group, a marketing communications company, also partnered with Adobe via a joint initiative to bring enterprise Gen AI expertise to its clients. Omnicom is looking at leveraging Adobe’s Firefly creative generative AI models alongside Omni data, the company’s open operating system, to develop on-brand content for assisting marketers in devising better results. The association also allows both companies to embed Firefly into their clients’ ecosystems, thereby facilitating content generation that complies with a brand’s style and language using APIs for increased automation. • The rise of voice assistants and AI chatbots also opens up new channels for advertising and customer engagement. Programmatic advertising, powered by AI, allows for real-time bidding and ad placement, maximizing efficiency and reach. Future of Artificial Intelligence in Advertising • The future of AI in advertising looks promising, with several emerging trends on the horizon. As AI algorithms become more sophisticated, we can expect to see even greater personalization in advertising. • AI will also play a critical role in measuring ad performance, with predictive detailed insights into campaign success and areas for improvement. Moreover, as AI continues to learn and adapt, it will get better at understanding human emotions and nuances, resulting in more effective and emotionally resonant advertising. and corporate analytics offering Predictions and Future Trends • As we look toward the future, AI is expected to become a more integral part of advertising. We can anticipate more advanced AI algorithms capable of creating highly engaging and creative ad content. Furthermore, integrating AI with augmented reality (AR) and virtual reality (VR) could create immersive advertising experiences that are both engaging and interactive. Challenges and Opportunities • The use of generative AI in advertising presents a wealth of opportunities for marketers, yet it also comes with its own challenges. Understanding both the potential and the pitfalls is key to successfully leveraging this technology. Preparing for the Future • To prepare for these advancements, marketers should start by familiarizing themselves with AI and its applications in advertising. Investing in data collection and analysis capabilities will be crucial, as these will form the backbone of any AI strategy. • Continued learning and adaptation will be key. With AI evolving continuously, marketers will need to stay on top of the latest developments and adjust their strategies accordingly. • In conclusion, while AI in advertising presents some challenges, the opportunities far outweigh them. With careful planning and strategic investment, marketers can leverage AI to create highly effective and personalized advertising campaigns that drive engagement and conversion. Understanding the Limitations of AI in Advertising • While AI can help automate tasks, analyze vast amounts of data, and generate new content, it is not without limitations. The effectiveness of AI depends greatly on the quality of the data it’s trained on. Poor quality or biased data can lead to incorrect predictions or results. • Furthermore, AI algorithms can be complex and lack transparency, which leads to the “black box” problem, where users don’t understand how the AI is making decisions. This lack of transparency can create trust issues among consumers. • Finally, while AI can automate many tasks, it cannot replicate human creativity and emotional intelligence. A successful advertising campaign often relies on a nuanced understanding of human emotions, cultural context, and creative storytelling - areas where AI currently falls short. Emerging Trends and Opportunities • Despite these challenges, the opportunities offered by AI in advertising are significant. Advancements in machine learning and data processing enable more sophisticated and personalized advertising campaigns. • AI-driven predictive analytics can identify consumer trends and preferences with unprecedented accuracy. Personalized marketing, facilitated by AI, allows for campaigns that speak directly to individual consumers’ needs and preferences, engagement rates. significantly improving 4

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