The rise of large language models (LLMs) has marked a significant milestone in AI innovation, generating excitement similar to the introduction of voice assistants like Siri a decade ago. But what exactly are LLMs, and how do they fit into the broader AI landscape?
LLMs are advanced machine learning models that focus on understanding and generating language. In recent years, there has been a shift towards generative AI, a specific application of LLMs that has led to tools like ChatGPT. These models have become more accessible as their scale has grown, with parameters increasing from 175 billion to an impressive 1 trillion and beyond.
For marketers, generative AI offers exciting opportunities. The buzz around hyper-personalization, AI-generated personas, and GPT-powered interfaces for improved data analysis and decision-making is growing. However, to fully leverage the power of generative AI, marketers need to start with a solid data foundation.
Quality data is key to effective AI. Even the most sophisticated AI models can only perform well if the data they are given is of high quality. This makes it crucial to ensure the integrity of your first-party data so that it can be used to create the best outcomes for both your business and customers.
In this article, we’ll address some common questions about AI in marketing and explore how you can make the most of its potential.
Predictive AI vs. Generative AI: What’s the Difference?
Let’s break down the differences between predictive AI and generative AI, and see how they complement each other in marketing.
- Predictive AI: This type of AI helps marketers make informed decisions by predicting who should receive a message, when it should be delivered, and what content is most likely to resonate. Predictive AI looks at historical data to spot patterns and forecast future outcomes. Unlike traditional predictive analytics, which often requires human input, predictive AI works autonomously.
- Generative AI: In contrast, generative AI focuses on creating content. It can produce text, images, and other media tailored to specific needs. While generative AI doesn’t replace human creativity, it can speed up the creative process and serve as a tool for brainstorming ideas. However, human oversight is still needed to ensure that the content aligns with brand guidelines and legal standards.
Predictive AI works behind the scenes, optimizing how marketers interact with consumers. It helps determine:
- Who should get a message
- When and where to send it
- What content will be most engaging
Generative AI takes this further by crafting personalized messages, ads, or emails based on individual preferences. While predictive AI handles the data-driven side of marketing, generative AI brings the creative side by producing tailored content. Together, they provide a powerful combination that helps marketers reach consumers with the right message at the right time.
Building a Strong Predictive AI Foundation
A solid foundation for predictive AI is essential for achieving the best results. One important factor is the model’s training time.
Predictive AI models improve through continuous learning. The more time a model spends analyzing data, the better it gets at making accurate predictions. This ongoing training process allows predictive AI to become more precise and faster over time.
For example, a predictive AI model used to identify in-market customers becomes more effective as it learns. Over time, it can accurately identify potential buyers, allowing marketers to send them timely messages that increase conversions.
Marketers should seek AI solutions that benefit from time and experience, ensuring effectiveness and real-time updates. This enables immediate, data-driven decisions that scale effectively.
Preparing Your Data for AI Success
For AI to deliver the best results, it’s important to use high-quality data. Here are three key steps to get your data ready for AI:
- Strengthen and Collaborate: Enhance your first-party data while maintaining privacy standards. Clean and structure your data to ensure it’s ready for AI use, and collaborate with trusted partners to supplement your data as needed.
- Scale Your Data Access: Use a people-based identity framework to build a comprehensive view of your customers across all touchpoints. This helps create better customer experiences, not just within your brand’s interactions.
- Ensure AI Readiness: Make your data easily accessible for AI-driven strategies. Working with a marketing solution provider can help ensure your data is properly prepared to maximize its potential.
Balancing Innovation with Privacy and Ethics
As generative AI continues to grow, marketers must keep consumer privacy and data ethics in mind.
Generative AI allows for real-time creation of advertisements, including copy and visuals. By combining predictive and generative AI, marketers can craft content that has emotional impact while reducing waste and maximizing efficiency. However, this must be done carefully, ensuring brand safety, content appropriateness, and legal compliance, with human oversight playing a key role.
To maintain privacy and ethics, marketers need to closely monitor AI-generated content. A dedicated team should ensure that all output meets brand standards and adheres to copyright laws. As data privacy consultant Jodi Daniels pointed out, businesses could face significant risks if they use generative AI in ways that violate consumer data agreements.
Stay Informed and Keep Learning
Just like the best AI models, it’s important to stay up-to-date and continue learning about the latest advancements in AI.
To get started, check out a Q&A with Steve Nowlan, SVP of Decision Sciences Analytics at Epsilon, where he shares insights on how organizations can make the most of AI. His advice could inspire new ideas and questions that keep the conversation going. If you want to learn more about how Epsilon’s CORE AI makes real-time marketing decisions at an individual level, visit their website.
