The generative AI revolution: What marketers should expect in 2024

Published Nov 24 2023 Last updated Nov 28 2023 9 minute read
2024 marketing trends
Contributors
  • Sean Dougherty
    Written by Sean Dougherty

    A copywriter at Funnel, Sean has more than 15 years of experience working in branding and advertising (both agency and client side). He's also a professional voice actor.

The marketing industry has recently experienced a significant transformation mainly driven by the development of generative artificial intelligence (Gen AI). This technology changes how marketers make content, interact with customers, and analyze data.

According to our Marketing Data State of Play 2024 report, 85% of marketers are already updating their skills because of AI. At the heart of this revolution is the shift from static, one-size-fits-all campaigns to dynamic, nuanced, and hyper-personalized customer experiences. 

Marketers need to prepare for a landscape where AI is not just an optional tool but a core component of their strategy. Drawing on industry insights gathered from our Marketing Data State of Play 2024, we have identified several key areas where generative AI is likely to profoundly impact the marketing landscape.

Key trends to watch in 2024

While AI has already made significant strides in data analysis and customer segmentation, the coming year will see even more advancements that will greatly impact the marketing industry. Here are some key trends to watch out for in 2024:

AI-generated content

One of the most well-covered and exciting developments in the AI space is the ability for machines to generate human-like content. Companies have already used this technology to create news articles, blog posts, and product descriptions. According to a survey by Funnel, 42% of marketers primarily use AI for creating content. This underscores its growing importance as part of the modern marketing toolkit.

Throughout 2024, AI-generated content will become more common in marketing campaigns. Computers will improve at mimicking human language and tone, producing more engaging content. The following are the three main types of AI-generated content to look out for in 2024.

AI-generated text

The sophistication of AI in text generation has reached new heights, with tools like GPT-4 by OpenAI leading the way. Marketers use this technology to create everything from compelling blog posts and creative ad copies to informative product descriptions and interactive chatbot scripts.

Imagine a marketing bot trained on a huge database of material from your favorite marketers or a content-generating algorithm that can create engaging informational blog posts in mere minutes. These previously unimaginable scenarios are quickly becoming a reality thanks to new versions of GPT with vastly expanded context windows and OpenAI's GPT Builder, which allows users to create custom models for their specific needs.

AI-generated images

Image and video generation using AI algorithms have also made significant strides. For example, DALL-E by OpenAI and Midjourney can both create images from text descriptions with remarkable accuracy and detail.

Marketers have been using these AI-generated images as attention-grabbing visuals for social media posts, website banners, or traditional advertisements. With the ability to generate endless variations of an image with slight variations in text descriptions, you can quickly and easily create diverse visual content for your campaigns. This innovative ad from Coca-Cola mixing film, 3D CGI, and Stable Diffusion (a deep learning text-to-image AI model) shows that even the big players are paying attention.

It is important to note that AI image generation is still in its early stages. While the technology shows great potential for assisting marketers by providing a constant stream of high-quality images, it's still not consistent or detailed enough to replace your graphic designer or product photographer. This means you should use AI image generation as a supplement to your current visual content creation, not as a complete replacement.

AI-generated audio

Audio is another area where AI is making an impact in marketing. One of its primary applications is through AI text-to-speech apps, which convert written text into spoken words. While the quality can vary between applications, some of these tools (i.e., Lovo, Murf) produce audio with remarkable human-like quality. Similar to image generation, it needs to be more sophisticated to completely replace the quality and nuance of a human voice actor. 

Possible use cases for these AI-generated voices include:

  • Adding narration to video content in multiple languages.
  • Generating audio ads for podcasts or radio shows.
  • Providing voiceovers for virtual assistants.
  • Even creating audiobooks.

When combined with AI-generated visuals, the possibilities for creating a large amount of diverse and engaging marketing content are virtually limitless.

Companies have also developed AI to work in the opposite direction, converting audio files into written content. This can be extremely helpful for content creators who struggle with transcribing interviews, for generating closed captions or subtitles for video content, or for turning podcasts into written content for blog posts. 

Another novel use for this is in analyzing phone conversations. AI can transcribe and analyze recorded phone calls to uncover valuable insights about customer behavior, preferences, and sentiment.

Personalized marketing experiences

One of the most exciting aspects of generative AI for marketing professionals is its potential to bring personalization to a new level. With traditional content creation methods, personalization was often limited to basic tactics such as using a recipient's name in an email subject line or segmenting audiences based on demographics. However, with generative AI, marketers are moving towards creating hyper-personalized content at scale. 

Envision email campaigns with sections of AI-generated content specifically tailored to each recipient's interests and automated email delivery times based on each customer's browsing behavior. Imagine chatbots that can engage in natural conversations with customers and recall past interactions, providing personalized assistance and product recommendations. These are just a few examples of how AI will continue transforming the customer experience by creating personalized marketing experiences. 

However, the integration of AI into personalized marketing has its challenges. The rapid pace at which consumer preferences change requires an equally agile marketing response. AI systems need to be thoroughly trained in your brand's voice and values to ensure that generated content aligns with your brand's identity and resonates with its target audience. Additionally, proper data management and privacy protocols must be in place to protect customer data and maintain trust. 

AI-powered predictive analytics

The transformative potential of AI-powered predictive analytics is becoming increasingly evident in performance marketing. This technology leverages vast amounts of data, combined with advanced algorithms and machine learning, to forecast future customer behaviors and trends. 

According to the Marketing Data State of Play 2024, 58% of marketers focus on sharpening their data analysis skills, underscoring the growing importance of predictive analytics in formulating marketing strategies. However, the report also indicates challenges in this area, with 46% of marketers not yet using automation tools for data cleaning and 41% not using them for data visualization. This highlights areas where generative AI can further streamline and enhance marketing analytics processes for many businesses.

The worldwide market for predictive analytics, valued at approximately $10.5 billion in 2022, is expected to reach $28 billion by 2027. This significant growth is driven by factors such as the increasing availability and quality of data, advancements in AI technology, and the growing demand for more personalized and targeted marketing. As we look towards 2024, the role of predictive analytics in digital marketing is set to become even more significant.

Uses of predictive analytics in marketing

Predictive analytics can used in a wide range of marketing areas, including customer segmentation, lead scoring, and campaign optimization. By leveraging AI-powered predictive analytics, marketers can better identify and target high-value customers, personalize messaging and offers for different segments, and predict the most effective channels and timing for their campaigns. Here are some specific use cases for how predictive analytics and the insights it provides can be leveraged in marketing:

  1. Optimizing Use of Resources: This technology assists in choosing the best times and channels for advertising, leading to more efficient use of marketing resources.
  2. Analysis of Customer Behavior: By segmenting target audiences and anticipating their behavior, predictive analytics enhances the effectiveness of marketing efforts, improving customer experiences and establishing long-term relationships.
  3. Strengthening Customer Retention: Insights from predictive analytics can help identify customers who are at risk of leaving and provide insights to prevent such churn, improving overall customer retention rates. It also can predict which customers are most likely to respond positively to retention campaigns.
  4. Targeting High-Conversion Segments: Businesses can concentrate their marketing efforts more effectively by identifying segments with higher conversion rates. This reduces costs while simultaneously increasing the ROI on marketing spend.
  5. Refining Content Distribution: Insights gathered from predictive analytics aid in crafting personalized content distribution strategies, ensuring content reaches the right audience at the right time through the most effective channels.
  6. Boosting Upselling and Cross-Selling: By analyzing previous purchase histories, predictive analytics enhances the success rates of upselling and cross-selling, offering customers relevant and timely recommendations. This not only increases revenue but also creates a better customer experience. 
  7. Optimizing Pricing Strategies: Predictive analytics can help identify the optimal price for a product or service, considering market trends, competitor pricing, and customer behavior. This ensures that businesses are not undercharging or overcharging for their offerings.
  8. Predicting Customer Lifetime Value (CLV): By analyzing customer data and behavior, predictive analytics can forecast the potential lifetime value of a customer to a business. This helps businesses prioritize high-value customers and tailor personalized experiences to retain them.

As we move forward, integrating AI with predictive analytics will continue to drive marketing innovation. With advancements in AI technology, these tools will provide increasingly accurate and insightful predictions, enabling marketers to develop more effective and personalized marketing strategies.

Data democratization in the AI era

Data democratization refers to making data accessible and understandable to everyone, from top-level executives to front-line employees. As data increasingly becomes the driving force behind business decisions, organizations must democratize data access and analysis. This approach ensures every team member can engage in data-driven decision-making, enhancing organizational agility and responsiveness.

An essential aspect of data democratization is equipping team members with the necessary skills to utilize data effectively. According to our Marketing Data State of Play 2024, a substantial number of marketers seek to improve their data skills, with 58% focusing on data analysis and 53% on data visualization. This highlights the need for ongoing training and support to ensure that all team members can utilize data responsibly and effectively in their roles.

As further highlighted in our guide to data democratization, this concept is more than just opening up access; it's about empowering every team member to utilize data effectively in their decision-making processes. By democratizing data, organizations enable a more inclusive environment where insights and knowledge are not just confined to data scientists or IT departments. This enables individuals at all levels of a company to make data-driven decisions, leading to better overall performance.

In the new AI era, making data more accessible significantly enhances the capabilities of marketing teams. For example, with AI-powered tools, marketers can easily analyze and derive insights from large datasets without needing specialized technical skills. This allows them to make data-driven decisions in real-time, leading to better campaign outcomes and customer experiences.

One significant concern about data democratization is the potential for misinformation and misinterpretation of data. To mitigate this risk, it's crucial to establish clear guidelines and processes for accessing and using data. This includes implementing proper training programs, establishing a centralized source of truth for data, and ensuring that all individuals understand how to interpret and use data accurately.

Ethics and transparency

As the use of AI in marketing continues to rise, so does the importance of ethical considerations and transparency in its application. A strong set of established AI ethics and principles is crucial for guiding the development and outcomes of AI technologies responsibly. These guidelines should address important issues like job security, misinformation, algorithmic bias, and the societal impact of AI, aiming to optimize its impact while minimizing risks.

Key ethical concerns include:

  1. Misinformation and False Content: AI's ability to generate content brings the risk of spreading misinformation or creating false content, making it imperative for marketers to ensure the accuracy and reliability of AI-generated material.
  2. Algorithmic Bias: AI systems can inherit biases present in their training data or algorithms, potentially leading to unfair or discriminatory outcomes. Marketers must strive for fairness and inclusivity in AI implementations. This also requires continuous monitoring and auditing of AI systems to detect and address any biases.
  3. Data privacy: As AI technology becomes more sophisticated and collects increasingly granular consumer data, there is a growing concern for protecting individual privacy rights. Companies must comply with regulations such as GDPR (General Data Protection Regulation) and implement ethical data practices to respect consumer privacy.
  4. Societal Impact: The broader societal implications of AI, such as its effect on employment and human interactions, must be carefully considered and managed. Companies should prioritize the well-being of their employees and consumers when implementing AI solutions.
  5. Transparency: Transparency is crucial in building trust with consumers and stakeholders. Marketers must ensure that AI systems are explainable and provide transparency into how they are used. 

Maintaining ethical standards

To uphold ethical standards, companies should:

  • Understand and Mitigate Risks: Recognize potential ethical issues and develop plans to address them, thereby safeguarding the brand and mitigating business risks.
  • Follow Published Guidelines: Follow guidelines set by authoritative bodies like UNESCO and prepare for evolving AI regulations. These guidelines often provide a framework for responsible AI use.
  • Foster Digital Literacy and Ethical Awareness: Building knowledge and awareness around AI ethics within marketing teams is crucial. This education helps teams make informed decisions that align with both company values and societal norms.
  • Commit to Responsible AI Use: Prioritize the responsible deployment of AI. This includes not exploiting AI capabilities for manipulative marketing practices and ensuring that AI applications enhance customer experience without compromising ethical standards.
  • Collaborate, Share, and Learn: The marketing community should collaborate to share best practices for responsible AI use. This collective effort will help advance ethical considerations in AI adoption.
  • Conduct Regular Audits: Companies should regularly audit their AI systems for potential biases and other ethical concerns. Auditing can identify areas of improvement and ensure compliance with ethical standards.

Looking ahead: The integral role of data hubs in AI-driven marketing

The current and future impact of generative AI in marketing is undeniable. It offers improvement and innovation in content creation, customer engagement, personalization, marketing spend optimization, and more. At the same time, it poses new ethical issues and challenges traditional marketing practices, potentially threatening jobs and the traditional agency model. This complex landscape requires a thoughtful and responsible approach to AI use in marketing, with the underlying role of data hubs becoming increasingly crucial.

This is where marketing platforms like Funnel come into play. Funnel acts as a centralized hub for marketing data, simplifying, integrating, and analyzing various data sources — which is crucial in an AI-driven marketing world. From optimized origination and storage to advanced sharing and analysis, Funnel can help you thrive in the AI-driven marketing domain.

The shift toward generative AI in marketing is inevitable, bringing with it both challenges and opportunities. Embracing platforms like Funnel ensures that your data is working for you rather than against you. With the right tools and strategies in place, businesses can harness the power of AI while maintaining ethical standards and maximizing results. It's time to start thinking about how your company can stay ahead in the AI-driven marketing landscape, and a robust data hub is a crucial step toward that goal. 

Disclaimer: The featured image for this article was created using generative AI.



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