Generative AI vs. predictive AI: Understanding the differences and synergies for modern marketing

Large language models (LLMs) have surged in capability and accessibility, leading to a renaissance in artificial intelligence discussions. You might recall the excitement that surrounded voice assistants when they first emerged—much like the current buzz around generative AI platforms like ChatGPT.

While the potential of generative AI is immense, marketers must ensure they have a solid data foundation to truly harness its capabilities. Quality data is non-negotiable; without it, even the most sophisticated models will yield only mediocre results. At Epsilon, we emphasize the importance of capturing and enriching first-party data to lay a robust groundwork for AI-driven marketing strategies.

In this blog, we’ll explore the distinctions between generative AI and predictive AI, their respective benefits and challenges and how they can be combined to elevate marketing efforts.

What are LLMs? Are LLMs different than AI?

LLM stands for Large Language Models and are a type of machine learning model designed for understanding and generating human language. The focus of LLMs has shifted significantly toward generative AI, a specialized application of LLMs that drives innovations in hyper-personalization, AI-generated marketing personas and more. LLMs are a subset of AI, and technically a more basic, foundational form of what we conceptually refer to as "AI."

What does AI mean in marketing?

AI in marketing refers to the use of artificial intelligence technologies to automate, enhance and optimize various marketing initiatives. As industries evolve, marketing is increasingly turning to AI tools to analyze vast amounts of data—ranging from customer behavior to social media interactions—to extract actionable insights that inform decision-making. To take advantage of all that generative AI has to offer, marketers need a solid data foundation.

What is predictive AI?

Predictive AI leverages statistical algorithms and historical data patterns to forecast future outcomes. It's instrumental in helping marketers determine who to target, when and with what messaging.

How predictive AI drives customer insights and business goals

• Improved decision-making: Predictive AI allows marketers to allocate resources more effectively and create personalized content based on customer behavior analysis.
• Automation: By automating the analysis of large datasets, predictive AI frees marketers to focus on strategic priorities.
• Boosts ROI: Targeting the right customers with tailored messaging enhances marketing returns.
• Competitive edge: Early visibility into emerging patterns gives businesses a distinct advantage.

Challenges of predictive AI

• Lack of creativity: Predictive models are focused on data and insight on what to do next, but they don't generate a new creative output, like an image, a framework or written content, like generative AI does.
• Bias in data: If the training data is flawed, the predictive models may also produce biased outcomes.
• Resource intensive: Implementing predictive algorithms can require significant processing power and expertise.

What is generative AI?

Generative AI uses deep learning to create new content by detecting patterns in source models. It can generate realistic images, text and audio, pushing creative boundaries.

Unleashing creativity and efficiency with generative AI

• Scales creativity and innovation: Generative AI can explore numerous creative ideas quickly, saving time and resources.
• Automation: It automates content creation processes, increasing productivity while reducing costs.
• Personalization: By utilizing past customer data, generative AI can generate tailored recommendations that enhance customer engagement and retention.

Challenges of generative AI

• Unpredictable quality: Output quality heavily depends on the training data; poor data leads to inferior results.
• Limited innovation: Generative AI often operates within the confines of existing data, which can stifle creativity.
• Potential for misinformation: Generative AI may produce inaccuracies, known as "AI hallucinations."

Predictive AI vs. generative AI: key distinctions

While both generative and predictive AI are valuable in marketing, they serve different purposes. Predictive AI focuses on analyzing historical data to forecast future events, while generative AI generates content based on existing patterns.

Realistically, the most ideal scenario for the future of marketing is for predictive AI to assess the data inputs in a marketing campaign or strategy--helping you determine who to reach and with what message--and then generative AI would then create that message in real time based on what is most likely to resonate with that person. The use of generative AI is still widely contested for outbound, consumer-facing marketing materials--and it should be. There is still a lot to still be tested in this capacity, which is why generative AI is currently most useful for generating drafts and options for creative outputs at early stages in the process, but not generating the actual creative that would be seen by a consumer.

By understanding these distinctions, marketers can leverage the strengths of both AI types to achieve superior outcomes.

Data requirements and outcomes

As marketers explore the potential of generative AI, it’s crucial to prioritize consumer privacy and ethical data usage. Generative AI can create marketing content rapidly, but this capability must be balanced against brand safety and compliance with legal standards.

Implementing a human oversight mechanism ensures that AI outputs adhere to brand guidelines and do not compromise consumer privacy. Continuous monitoring and compliance checks are essential to navigate the complexities of AI-generated content.

Use cases and business value

To effectively implement AI, businesses must prioritize a strong data foundation. Here are key steps to consider:

1. Data hygiene: Cleanse, structure and reinforce first-party data to ensure quality inputs for AI applications.
2. Access to data at scale: Implement a people-based identity system that connects customer data from various sources.
3. Real-time model updates: Look for solutions that can provide immediate updates to predictive and generative models, allowing for real-time customer engagement.

The power of AI synergy: How predictive and generative AI work together

Integrating predictive and generative AI can create a powerful synergy in marketing. While generative AI can produce diverse content, predictive AI ensures that this content reaches the right audience at the optimal time. This combination maximizes campaign efficiency and ROI and improves the overall customer journey.

Enhancing customer understanding and personalization

One of the results of fine-tuning your AI strategy? Deepening your customer understanding so you can better personalize your marketing messages. The right AI tools can give you the insights you need to reach customers and prospects with messaging that actually makes sense based on where they're at in the customer journey.

Optimizing campaigns and driving ROI

Beyond gaining a deeper understanding of your customers, AI can help you optimize campaigns by analyzing audience engagement to provide tailored recommendations so you can ensure every campaign performs to the best of its ability. And when campaigns actually perform, you're bound to see results when it comes to driving ROI.

Future outlook: the evolution of AI in marketing

Staying informed about the latest developments in AI is critical for marketers aiming to maximize their effectiveness.

To learn more about the connection between data, identify and AI, download the Epsilon sponsored white paper, Improve data quality to support quality AI outcomes, in which IDC’s Lynne Schneider explains why investing in data quality is essential for driving AI performance—and why having a strategy that refines the data your enterprise gathers into a clean and consolidated foundation for enrichment, analysis and action can lead to results.

This blog post was originally published on November 17, 2024, and has since been updated.