Marketing will soon become so personalized that it doesn’t feel like marketing at all. 

Every brand touchpoint will feel like someone solving a problem or fulfilling a timely need, whether it’s through a social media ad, onboarding email, or chatbot conversation. Organizations will achieve this by using their first-party data to deliver hyper-relevant messages that reflect the unique needs, personality, and preferences of each individual customer or prospect. 

Of course, making this a reality requires considerable time and testing. On their own, it would take marketers years to learn about a customer at an individual level and orchestrate highly personalized and timely experiences. But they don’t have to. 

Agentic AI, a powerful type of AI that can execute tasks autonomously, is reinventing what’s possible with personalization. It greatly augments what marketers can accomplish, no matter their headcount or resources, empowering teams to execute highly effective campaigns at scale – and often at a depth humans don’t have the luxury to pursue.  

Although agentic AI is still new, the technology is evolving rapidly, and we’ve only begun to realize its potential. Teams have a competitive necessity to lay the optimal foundation today for long-term, scalable growth with agentic AI. 

To help your organization begin integrating agentic AI for marketing and campaign personalization, let’s explore the current state of agentic AI in marketing, where it’s headed, and use cases you can start exploring.  

What is agentic AI, and how do marketing teams use it?

Agentic AI can orchestrate a series of activities to achieve an organizational goal. Much like humans, agentic AI can:

• Review customer data to draw insights
• Develop a campaign to accomplish a goal
• Assess the results to inform future strategies

And agentic AI can do this at an infinite scale in real time. 

Organizations across industries are building AI agents, including ones designed to accomplish sales and marketing tasks. Individual agents trained in specific ways coordinate with each other autonomously to complete complex marketing needs with ease. 

GrowthLoop’s Compound Marketing Engine, for example, includes an AI Studio with a Data Agent, Audience Agent, Journey Agent, and more. Customers use a chat interface to explain how they need help, and the agents handle it from there.

The key is for agentic AI to invest in and activate on an organization’s complete dataset instead of relying on fragmented data silos. Full data access ensures the best campaign recommendations and faster iteration cycles. A data cloud is the ideal data storage location to maintain the customer 360 and connect to every sales, marketing, and business intelligence system. 

What’s the difference between generative AI and agentic AI?

Generative AI creates new content (text, audio, video) based on its training data and user prompt. Popular generative AI tools include ChatGPT, Claude, and Perplexity, among many others.

Agentic AI includes generative AI capabilities, but it also makes decisions and takes action autonomously. Agentic AI orchestrates complex tasks and continually improves its outputs, freeing up time for marketers to address other opportunities in the customer journey.

Agentic AI enables one-to-one (1:1) personalization 

Agentic AI accelerates many marketing tasks, and it is especially powerful for enabling one-to-one personalization.

Personalized experiences are proven to attract new customers, boost lifetime sales, and strengthen loyalty. Customers want to receive messages and offers that reflect their individual needs. The more helpful and personalized marketing is, the more useful and thus effective it is. Companies have worked hard to improve their personalization strategies with much-needed help from technology:  

• Marketing started as a one-to-many approach, delivering generic messages or offers written to an entire customer or prospect base. This broad marketing casts a wide net and is highly ineffective. 
• As organizations collect customer data, marketing teams can create more specific customer personas to tailor messages for targeted groups of customers. These personas help teams drive more successful campaigns and improve their campaign efficiency, but they rely on generalizations that neglect individual customer needs. 
• Now, agentic AI empowers teams to orchestrate experiences designed for exact individuals based on their specific attributes tracked in first-party data. These messages are highly effective, and agentic AI enables real-time iteration that drives compound marketing growth.

1:1 personalization at scale is not possible without agentic AI, because it takes humans days, weeks, or even months to develop personalized content, messaging, and targeting. 

As a customer base grows and teams get more granular in their testing strategies, it would require hundreds of marketers working 24/7 to achieve true 1:1 personalization. 

Agentic AI systems can collect and analyze data on a massive scale to create 1:1 personalized campaigns. The systems can also conduct tests to continually learn what works best for individual customers. This real-time learning empowers teams to capitalize on valuable test results while campaigns are still in-market and relevant. 

Real-world use cases for agentic AI in marketing personalization

Personalization spans every message and campaign element, including delivery time, channel, message and offer, content format, and more. AI orchestrates campaigns and conducts ongoing testing and iteration to find the ideal timing, delivery channel, and offer for any need in the customer’s journey:

• Individuals who typically browse channels early in the day, for example, might receive messages and offers only during the morning, instead of a pre-set time dictated for the entire outreach list. 
• People who primarily use LinkedIn mightreceive offers there instead of on channels where they do not engage. 
• Customers could receive discounts or offers that reflect their shopping habits, instead of generic offers that ultimately won’t convert.

Consider these ways agentic AI will improve campaign results and deliver hyper-personalized messages:

Customer acquisition

Agents can suggest the optimal customer acquisition strategy based on a prospect’s data, like sending an email sequence or multichannel advertising strategy that spotlights a problem they likely face. 

A bank, for example, could collect first-party data for a specific user by following their experience with the bank, noting the type of content they consume and which service(s) they use. An agentic AI system could assess that the user is shopping for homes, and the system can then target them with highly personalized ads promoting homebuying services. The system can orchestrate an omnichannel experience and deliver content on the best channels for the individual user.  

This removes the manual lift of creating audience segments and executing campaigns in a timely manner while gradually lowering the total customer acquisition cost and raising revenue. 

Cross-sell and upsell campaigns

Most organizations use AI to suggest complementary products or services. Retailers, for example, use AI to understand that if customers purchase a specific hat, they also commonly purchase a specific shirt. These broad purchase correlations have helped organizations boost sales, and these capabilities will soon be supercharged with agentic AI.

Agentic systems can analyze massive amounts of customer data faster than a human analyst, understanding nuances in customer data and purchase history to offer products and services that have the highest likelihood of driving conversions, upsells, or cross-sells. Agentic AI can create tailored emails, advertisements, or push notifications that spotlight products or services that individual buyers will be interested in. 

Churn winback

Agentic AI can continually monitor customer data, trends, and potential signals to notice if someone is likely to churn. The system can coordinate a series of efforts to re-engage them before they’re lost. 

For example, a streaming provider can identify customers who are no longer viewing movies or shows on the platform. The agentic AI can tailor messages that spotlight content the customer is likely interested in based on their viewing history. Or, the system can deliver a survey to understand what media the customer would like to see on the platform.  

If customers do churn, agentic AI can test different messages, offers, and delivery channels — similar to how it operates with other acquisition campaigns — and analyze which elements are more likely to win the customer back.

Preparing your organization for agentic AI

Agentic AI will become a competitive essential for organizations to orchestrate 1:1 personalized messages and campaign journeys. No team is accomplishing this yet, but you can move forward on your agentic AI journey by following this advice:

Prioritize a first-party data strategy

Any AI tool is only as valuable as the data it can access. Agentic AI is no exception. Your organization needs a first-party data strategy to maintain optimal data hygiene and consistently collect more insights that will fuel personalized campaigns and journeys. A data cloud is the optimal storage location to preserve your customer 360 and leverage AI. 

Invest in a first-party data strategy and collect and organize datasets in a thoughtful, systematic way to position your team for ongoing success. Your data is your key competitive differentiator. 

Consider the art of the possible

Does a specific marketing task take considerable time for your team? Is a challenge holding you back from focusing on other priorities? See if an agent can help.

Keep an open mind when considering ways to implement agentic AI. By assuming an agent can support a task, you will find out how it can assist instead of getting caught up in doubt or hesitation. This exploration may uncover new use cases for agentic AI.

Take the winback example we mentioned earlier. If your team is spinning its wheels on determining churn signals and winback ideas, why not leverage agentic AI to perform that analysis and brainstorming for you? See what it comes back with and use it to inform your hypotheses and future tests.

Hold vendors accountable

As a marketer investing in SaaS technology, hold your vendors accountable. 

Your results with any platform will change greatly as agentic AI becomes more mainstream. At GrowthLoop, we envision a future where business models will be outcomes-focused, or results as a service, as opposed to selling seats and licenses for software. 

Ask vendors how they’re investing in agentic technology to drive better outcomes through their service or solution. Vendors will quickly fall behind if they fail to integrate agentic AI in ways that supercharge your results. 

Balance personalization with privacy

Customers may be surprised as advertisements and messages become increasingly focused, especially as teams get started. It’s understandable for humans to be shocked by how much brands know about them and can use that data for highly personalized experiences.

Be transparent with customers about how you will use their data and what they can expect. Give customers control over how their data is used, if possible. Ideally, your organization will deliver experiences so valuable that you will earn your customers’ trust and the right to continue leveraging their data. 

AI-powered personalization at scale

Agentic systems will soon be commonplace in organizations, empowering marketing teams to fulfill the level of personalization we’ve strived to deliver for decades.

These advanced agents are designed to help you treat customers as true individuals and orchestrate the personalized journeys they expect. Start experimenting with use cases today to get comfortable with the technology and deliver immediate time savings that help you focus on other strategic priorities. 

This complete personalization will forever change how customers expect brands to engage them. Are you ready to meet this need?