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