AI for Marketing: A Practical Guide to Getting Started
AI has rapidly transformed from a novelty to a critical component of your marketing strategy and martech stack—and dabbling in ChatGPT doesn’t cut it. Unfortunately, there aren’t many practical resources designed to help you understand your options.
If you’re new-ish to AI and looking to get the lay of the land, you’re in the right place. This marketing AI guide breaks down the basics of artificial intelligence:
- What is AI?
- What is AI for marketing?
- How does it work?
- How can you start using it effectively?
Our goal is to demystify AI for marketing professionals and provide a practical roadmap for incorporating these powerful tools into your tech stack.
What is AI?
Artificial intelligence (AI) refers to technologies that can complete tasks that traditionally require a human.
These technologies make it easy to consume and act on enormous amounts of data to achieve a specific goal.
For example:
- Popular digital assistants (like Siri and Alexa) scour the internet to answer our questions
- Directions apps (like Google Maps, Apple Maps, and Waze) use real-time traffic data to provide routes and ETAs
- Social media algorithms use behavioral data to show us content we’re most likely to engage with
“Artificial intelligence refers to computer systems that can perform complex tasks normally done by human-reasoning, decision making, creating, etc.” —NASA
What is AI for marketing?
Like Siri or Google Maps, AI tools designed for marketing make it simple to consume and act on enormous amounts of data to achieve specific goals. Imagine having your very own Siri for Marketing—but instead of giving you driving directions and dinner recipes, she helps you:
- Combine and analyze various customer data sets
- Automate personalized experiences
- Improve performance against your specific business goals
It would totally transform your life as a marketer, just like these tools transformed our lives as consumers.
Generative AI vs. predictive AI
Depending on what you’re trying to accomplish with AI, you may rely on predictive AI and/or generative AI.
Predictive AI is the forecasting expert on your team. Just as your analytics specialist might predict which campaigns will perform best based on historical data, predictive AI does this automatically and at scale. It answers questions like:
- Which customers are likely to buy?
- When should we send this email for maximum opens?
Generative AI (GenAI) is the creative producer on your team. GenAI can produce copy (hello, ChatGPT), and as long as you provide the data, it can also create audience segments, campaign structures, creative, and personalized customer journeys.
Related content: Generative AI for Marketing—Content to Customer Journeys
What are AI Agents?
One of the easiest ways for marketers to take advantage of both predictive AI and generative AI is through AI agents.
AI agents are simply specialized AI tools designed to handle one specific marketing task really well. Think of agents as digital specialists on your marketing team. Just as you might have a dedicated email marketing specialist, social media manager, or analytics expert, AI agents also provide one specific area of expertise.
On the flip side, think of tools like ChatGPT as your generalists. General-purpose AI tools handle a wide variety of tasks reasonably well—but they lack the specialization, customization, and integration of purpose-built agents.
How marketing AI agents drive results
Marketing AI agents are AI tools purpose-built for specific marketing tasks.
These agents help you offload time-consuming, repetitive tasks, so you can focus your resources on more strategic and creative initiatives that add value. Each agent works independently but can also be connected to other agents to automate entire workflows.
For example, you could create an agent workflow like the following:
- A segmentation agent identifies high-value prospects.
- A content creation agent generates personalized outreach emails for each segment.
- A campaign optimization agent tests different subject lines and send times.
- A reporting agent delivers performance metrics that feed back into refining your segments.
The real innovation with agents is their ability to immediately act on intelligence without human intervention. It takes human marketers (significant) time to collect data, identify trends, debate strategies—and finally act on the insights.
Marketing AI agents eliminate these delays by collapsing this timeline, integrating data interpretation with instant execution.
Learn more about AI agents: Reduce the Distance Between Data and Action by Combining Agents with Intelligence
How to Use AI in Marketing: 4 Real-World Use Cases
You’ve probably tried out creating AI content with ChatGPT, Gemini, or Claude, but drafting content barely scratches the surface of AI’s marketing capabilities. AI offers a variety of real-world opportunities for marketers to use today.
1. Audience segmentation with AI
Traditional audience building requires selecting multiple variables and building numerous conditional statements—tedious at best. With AI, you can use natural language to define your target segments and generate the audience instantly.
Let’s say you’re looking to target people on the East Coast who like luxury footwear.
- Traditional segmentation methods: Select all geographic locations (up to 14 states), specify price points, etc.
- Segmentation with AI: Tell your agent, “I want to target East Coast sneaker heads.”
More advanced AI systems can also forecast how these segments will perform in campaigns.
2. AI-powered personalization
Marketers have been striving to execute better personalization for a decade or more. The challenge isn’t a lack of data—companies have more customer information than ever before. The problem is turning this mountain of data into meaningful action.
That’s where AI excels. AI makes it possible to execute marketing personalization at a scale and depth that would be impossible to achieve manually. In just a blink of an eye, AI crunches the data and takes action to:
- Dynamically adjust website content based on visitor behavior
- Customize email content for individual recipients (beyond name insertion!)
- Recommend products based on purchase history and browsing behavior
- Personalize ad creative and messaging based on intent signals
- Adapt offers and promotions to match individual price sensitivity and purchase patterns
Learn more: How AI Helps Marketers Achieve True Personalization
3. AI customer journey mapping and optimization
Manually mapping customer journeys usually relies on a lot of assumptions, and the results end up sitting on a shelf as a static document. AI transforms customer journey mapping into a dynamic, data-driven process that delivers more effective, customer-centric experiences.
- Identify actual paths customers take across touchpoints (rather than theoretical journeys)
- Highlight friction points where customers commonly drop off
- Recommend optimal next actions for customers at different journey stages
- Predict which journey paths are most likely to result in conversion
- Automatically adjust journey flows based on real-time performance data
4. AI-powered predictive analytics
Predictive analytics help you forecast outcomes to make more effective, data-driven marketing decisions. AI can help you:
- Predict customer lifetime value to identify high-potential accounts
- Predict churn to flag at-risk customers before they leave
- Predict conversion probability to focus on the most promising leads
- Optimize spend by predicting which channels will deliver the best ROI
- Analyze trends to identify emerging opportunities before competitors
These predictive capabilities allow you to be more proactive, addressing potential issues before they arise and capitalizing on opportunities at the optimal moment.
Are You Ready to Implement AI?
To set your AI initiatives up for success, it’s important to cover off on a few key preparedness issues before diving in:
- Data readiness
- Integration with existing tech stack
- Team preparation
- Compliance and ethical concerns
You can address several of these issues by creating a solid AI policy for your business. Creating an AI policy will insulate you from risk and help you innovate more quickly as technology evolves.
Learn how to create an AI policy: Why Every Business Needs an AI Policy: A Blueprint for Success and Compliance
Data readiness requirements
The saying “garbage in, garbage out” holds true in many cases, including AI initiatives.
Before implementing AI in marketing, you need a solid foundation of clean, high-quality data and the ability to integrate it from various sources (e.g. CRM systems, website interactions, purchase history, and third-party sources). You should also have predefined processes for governing your data.
Before starting any AI projects:
- Audit existing data sources and quality
- Address data gaps and quality issues
- Create necessary data integrations and pipelines
- Establish data governance practices for ongoing quality
Integration with existing tech stack
AI tools must work alongside your existing marketing technologies. If your existing martech vendors offer AI capabilities, make use of them first.
If not, you’ll have to decide between custom AI development or standalone AI tools. You’ll also need to verify that your current systems have APIs that allow access to necessary data.
Team preparation
Your new AI tools won’t have much value if your team members aren’t prepared to use them well. Consider your team’s AI literacy—do they understand basic AI concepts and applications? Do they know how to use effective prompts to get desired outputs? Do they know how to act on AI-generated insights?
To prepare your team:
- Provide basic AI education for all team members
- Identify AI champions within the organization and offer specialized training
- Establish processes and training for working with AI tools
- Establish processes for sharing key learnings amongst team members
Compliance and ethical concerns
Remember GDPR and CCPA? Yes, they still exist, and they may apply to your AI applications. AI implementation must account for various regulatory and compliance considerations, such as privacy, consent management, data storage, and more. Work with your legal and compliance teams early in the AI implementation process to prevent costly problems later.
Beyond legal requirements, you should also consider the ethics of your AI applications:
- Be transparent with customers about how AI is being used in their experiences
- Collect only the data you need for your specific outcomes
- Provide meaningful benefits to customers in exchange for their data
- Maintain appropriate human review of AI-generated content and decisions
- Keep your eyes open for biases or prejudices that may crop up in your AI outputs
Getting Started with AI: Step-by-Step Roadmap
1. Determine risk tolerance and constraints
The first step in getting started with AI is to determine your organizational and/or regulatory limitations.
- Are there stakeholders that will prevent you from accessing certain data?
- Are there regulations that prohibit certain data use (or make it riskier)?
- Are there any factors that may preclude your use of AI altogether?
Roman Gun, VP of Product at Zeta, explained: “We’re seeing a lot of desire from our financial institution clients to use our AI offering, but they’re limited in what they can do because they get a lot of burden from regulators.”
This is an important consideration when outlining potential AI use cases.
2. Identify appropriate AI use cases
When it comes to your early AI projects, you want to be the mouse that ate the elephant—you want to tackle one small bite at a time. As you address and showcase success with smaller use cases, you’ll then be able to more effectively tackle larger, more complex use cases. Plus, AI agents excel at achieving one task at a time.
Your risk tolerance and other constraints will help you determine the best use cases to start with. Roman explained that many financial institutions choose to implement AI for campaign QA. While QA is a low risk use case, it’s a tedious task for marketers. Implementing AI for QA allows these marketers to achieve significant efficiency gains without running afoul of industry regulations.
As you get started, look for discrete tasks and simple processes that could be automated.
For example, instead of looking to orchestrate end-to-end customer journeys with AI, start with targeted next-best-offer recommendations for your highest-value segments.
Instead of transitioning all of your audience segmentation to AI, start with AI-assisted segmentation for a specific product line.
3. Find the right tools
Out-of-the-box large language models (LLMs) like ChatGPT, Claude, and Gemini are all the rage, but they do have their drawbacks. Namely, they are not integrated with the tools you use to activate your marketing efforts.
While ChatGPT can write email content, for example, it cannot send emails. While you can manually upload performance data to Claude for analysis, it cannot collect that data for you or act on it to optimize campaigns.
The most important factor to consider when evaluating AI tools for marketing is therefore full integration with your tech stack.
Beyond tech stack integration, you may also want your AI technology to include:
- Pre-built agents designed to complete tasks related to your target use cases
- The ability to easily build your own agents for bespoke tasks
- The ability to chain agents together to automate more complex workflows
- An intuitive user experience that doesn’t require extensive technical expertise
4. Implement and iterate
As you test out your AI tools for your designated use cases, it’s important to define and track success metrics that are aligned with your use cases—and that you can easily measure. These metrics could include efficiency and productivity gains, like:
- Hours saved
- Campaigns created
You can also measure simple outcomes-based metrics, like:
- Decrease in customer acquisition cost
- Increase in open and/or click rates
Monitor your performance against your chosen metrics and adjust your approach accordingly. Plan for rapid iteration as your results roll in.
5. Expand into other use cases
As you see success with your initial use cases and get comfortable with the tools, you can gradually expand into additional use cases.
If you started with implementing AI-driven campaign QA, you may expand into using AI to write the campaign copy. From writing campaign copy, you may look to use a tool like Zeta’s visual composer to generate the campaigns’ creative assets.
Remember, each agent is designed for a specific task—but you can create more complex workflows by chaining groups of agents together.
Advantages of Zeta’s Approach to AI
Zeta has spent years pioneering AI for marketing and refining our architecture to offer you the best—and easiest to use—AI solutions. What sets us apart in the market?
Focus on driving outcomes
Built with AI at its core, the Zeta Marketing Platform (ZMP) helps you automate and optimize your campaigns for maximum efficiency—and impact. Zeta’s AI is laser-focused on driving concrete business results like conversion rates, sales, revenue growth, and return on investment.
The ZMP offers a wide range of tools to make the most of every interaction. For example:
- Strategically distribute your resources by focusing on the marketing efforts that are predicted to yield the highest returns.
- Dynamically adjust your campaigns in real time, powered by advanced modeling.
- Proactively generate and act against recommendations tied to your specific business goals.
In the near future, you can expect to see the launch of our new Guidance Center, an entire recommendation framework that uses human language to tell you exactly how to optimize your campaigns against your business outcomes. The tool will also provide the forecasted impact of each optimization, and, once you execute, the actual impact will be included in your reports.
Custom AI for every marketer
Zeta has a library full of pre-built AI agents to help you perform a variety of marketing tasks—like generating content, analyzing customer data, optimizing campaigns, and personalizing customer interactions. Plus, you can combine AI agents into workflows that accomplish complex objectives.
Learn more about Zeta’s Agentic Workflows.
But if you can’t find what you’re looking for, you can create your own custom AI agent using our PLACE framework. This framework guides you through an easy process to get exactly what you’re looking for from your agent. It’s a bold approach that gives you the ability to make the most of AI in your day-to-day work.
Zeta also allows you to bring your own models to the platform and use them immediately. Soon, you’ll be able to build your own models directly in the platform, no data scientist required.
Foundation of privacy
Privacy is not just tacked on to our AI tools—it’s built into all of Zeta’s solutions.
Our flagship customer data platform (CDP) functions on a foundation of governance, permissions, and (you guessed it!) privacy. On the flip side, new SaaS vendors without a core foundation of privacy offer fewer assurances, and using an LLM out of the box offers virtually none.
Take Your Next Step with AI
Are you ready to advance your AI game beyond ChatGPT? The technology exists today to help you work smarter, create more meaningful connections with customers, and drive better business results.
Zeta’s AI capabilities can help you maximize efficiency and transform your marketing strategy—without replacing the human creativity that makes your brand unique. Learn more about Zeta’s AI capabilities.
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