Unlocking the power of generative and predictive AI in marketing

The evolution of large language models (LLMs) has brought about a new era of AI innovation, generating as much excitement as the debut of voice assistants like Siri over a decade ago. But what exactly are LLMs, and how do they fit into the broader AI landscape?
LLMs are advanced machine learning models designed for language comprehension and generation. Recently, the focus has shifted to generative AI, a particular application of LLMs that has been instrumental in developing tools like ChatGPT. This technology has become more accessible to the public due to significant advancements in the scale of these models, with parameters growing from 175 billion to an impressive 1 trillion and beyond.
For marketers, generative AI opens up exciting possibilities. Conversations are buzzing about the potential for hyper-personalization, AI-generated marketing personas and GPT-powered interfaces that enhance data analysis and decision-making. However, to fully harness the power of generative AI, marketers must first ensure they have a strong data foundation.
Quality data is the cornerstone of effective AI. Even the most advanced AI models can only perform as well as the data they’re fed. This makes it essential to focus ensuring the integrity of your first-party data so you can harness its power to create the best possible outcomes for you and consumers.
In this article, we’ll address some of the most common questions about AI in marketing and explore how you can maximize its potential.

Decoding predictive and generative AI: What’s the difference?

Let’s break down the distinctions between predictive AI and generative AI, and how they can complement each other in the marketing sphere.
Predictive AI: This type of AI helps marketers make informed decisions about targeting—determining who should receive a message, when and where they should be reached, and what content is most likely to resonate. Predictive AI analyzes historical data and identifies patterns to forecast outcomes. Unlike traditional predictive analytics, which requires human intervention, predictive AI functions autonomously.
Generative AI: In contrast, generative AI is all about creation. It generates content across various formats, including text and images, tailored to specific needs. While generative AI won’t replace human creativity, it can significantly speed up the creative process by serving as a powerful tool for idea generation. Nevertheless, human oversight remains essential to ensure that the content aligns with brand guidelines and legal standards.
Predictive AI works behind the scenes, optimizing how marketers connect with consumers by making interactions more relevant and less intrusive. It determines:

• Who should receive a message
• When and where to deliver it
• What content is most likely to engage

Generative AI can then take this a step further by crafting personalized messages, advertisements, or emails based on individual preferences. While predictive AI provides the “science” of marketing through data-driven decisions, generative AI adds the “art” by creating compelling, tailored content. Together, they offer a powerful combination that truly helps marketers deliver the right message, at the right time, to consumers.

Building a strong predictive AI foundation

A robust predictive AI foundation is essential for achieving optimal results. One key element is the model’s training time.
Predictive AI models improve through continuous learning. The more time a model has spent analyzing data, the better it becomes at making accurate predictions. This ongoing training process allows predictive AI to evolve, becoming faster and more precise over time.
For example, a predictive AI model used to identify in-market customers becomes more effective as it learns. Over time, it can more accurately pinpoint potential buyers, allowing marketers to target them with timely messages that drive conversions.
Marketers should seek AI solutions that have the benefit of time and experience to ensure effectiveness, offer real-time updates, enabling immediate, data-driven decisions that scale effectively.

Preparing your data for AI success

For AI to deliver the best results, it requires high-quality data. Here are three key steps to ensure your data is AI-ready:

• Strengthen and collaborate: Enrich your first-party data while maintaining privacy standards. Apply rigorous data hygiene practices to cleanse and structure your data, ensuring it’s ready for AI applications, and work with trusted partners to supplement your data as needed.
• Scale your data access: Leverage a people-based identity framework. This allows you to build a comprehensive view of your customers across all touchpoints, not just within your brand’s interactions, which enables you to create optimal customer experiences.
• Ensure AI readiness: Make sure your data is accessible for AI-driven methodologies. Partnering with a marketing solution provider can help you prepare your data for AI, ensuring you truly get the most out of it.

Balancing innovation with privacy and ethics

As generative AI continues to evolve, it’s crucial for marketers to prioritize consumer privacy and data ethics.
Generative AI enables the creation of entire advertisements in real time, from copy to visuals. By integrating predictive and generative AI, marketers can optimize content for emotional impact while reducing waste and maximizing efficiency. However, this must be balanced with brand safety, content appropriateness and legal compliance, with human oversight playing a critical role.
To maintain data privacy and ethics, marketers must closely monitor AI outputs. A dedicated team should ensure that all generated content adheres to brand standards and complies with copyright laws. As data privacy consultant Jodi Daniels highlighted in a recent Forbes article, businesses could face significant risks if they use generative AI in ways that conflict with consumer data agreements.

Stay informed and keep learning

Like the best AI models, it’s important to stay informed and continuously learn about the latest advancements in AI.
To start, check out a Q&A with Steve Nowlan, SVP of Decision Sciences Analytics at Epsilon, wherein he shares insights on how organizations can fully leverage AI. His thoughts might spark new ideas and questions to keep the conversation going. And if you want to learn more about how Epsilon’s CORE AI makes real-time marketing decisions at the individual level, visit our website.

Zach Hover