How To Build A Marketing Personalization Agent

One of the most promising applications for AI agents is personalized marketing.

Every year, ecommerce, insurance, banking, media, and healthcare businesses invest enormous amounts of time and money in creating tailored marketing messaging for customers at all stages of the customer journey.

However, the entire MarTech industry is on the verge of significant disruption - and the software vendors are aware of it.

Personalization engines are nothing new. What's new, is our ability to build end-to-end personalization pipelines at a fraction of the time and cost, thanks to frameworks like LangGraph. AI agents can now quickly enable fully automated marketing flows that were unthinkable just a few years ago.

In this playbook, I will show you how to build a LangGraph personalization agent capable of generating real business value.

This will be a lot to digest, so I will break up the development into a part 1 and a part 2 consisting of the following:

In this first playbook, I'm going to:

1. Explain why agents will leave traditional marketing software in the dust.
2. Build a marketing Personalization agent in LangGraph, step-by-step. The agent can be used to personalize the web and emails and support paid media targeting.
3. Modify the agent for two different types of targeted communication.

In part 2, I will show you how to deploy and turn the agent into a working product. Specifically, we will:

1. Build the execution layer for the agent actions. This will allow us to use the agent on multiple platforms.
2. Deploy and launch the agent into production.
3. Finally, I will give you proven business tactics for increasing revenue with your new personalization engine.

You'll find all the code organized in steps linked at the end of the playbook. Since we'll use Shopify as an example, you also get a LangGraph template to be re-used for building Shopify agents.

Let's get started. 🚀

Refining Personalization

Personalization engines shape how businesses communicate with customers by analyzing behavior patterns and adapting content dynamically.

While simple rules can segment customers by obvious traits, true personalization requires understanding broad purchase patterns and subtle individual preferences to create relevant interactions.

The impact is significant - correctly personalized experiences routinely deliver 10-30% revenue increases over generic approaches.

AI-driven agents are transforming traditional marketing personalization software by offering more than just automation. Their ability to be "chained" together enables a fluid movement from broad strategic decisions to hyper-specific tactical actions.

Rather than relying on rigid rules or siloed platforms, AI agents consider the context, refine insights at each step, and produce loosely coupled marketing actions.

This “chain” approach is illustrated in the diagram, where the Data Context and Customer Journey Stage feed into a central node (representing the coupling of Strategy with Tactic), which then informs the Next Best Action and culminates in a final Action.

The first part of the chain can be configured to produce a strategic action:

This customer might benefit from a re-engagement campaign, possibly with personalized offers related to their previous purchase (athletic/sports accessories) to encourage them to return to the store.

This strategic action can then be refined using another agent to produce very specific recommendations:

Use the Loyalty program focused on repeat purchases. The following products are purchased by similar customers and have high repeat purchase ratios:
   - Mizuno Wave Rider 24
   - Nike LeBron 18
   - Asics Gel-Nimbus
   - Air Jordan
   - Adidas NMD R1
Offer exclusive discounts in the range of 5-15% or early access to new releases.

In the previous playbooks, we laid the groundwork for producing recommendations to increase CLV:

Cross-Selling and Re-Selling with LangChain

In this playbook, we’ll implement a market basket recommender to find products that can be used for cross-selling. We’ll then use this with the Next Product Recommender and LangChain to optimize CLV.

RabbitmetricsBenjamin Falkeborg

What we will build here allows you to generate the strategic actions and extend these tactical recommendations into a full-fledged personalization pipeline.

Personalization Solution Design

The solution design I will present has been tested over time and implemented in various forms using a combination of data pipelines and machine learning across multiple industries.

But instead of the traditional approach, we will implement this proven solution much faster with agents:

The entire personalization solution consists of four main components

#1 The context extraction

To decide what actions to take, the agent will need context. In our case, the context consists of available customer data. This will be retrieved from the Shopify Admin API.

#2 The personalization engine

The Engine itself maps context to actions via prompts. The prompt tells the agent what structured output is needed, and the LangGraph flow determines the action.

#3 The activation layer

The activation layer is where the agent's actions will come into effect. To generate business value, actions need to guide business decisions.

#4 The feedback mechanism

The feedback mechanism monitors and optimizes the personalization system to ensure that it generates an uplift. This part will be covered separately as we go deeper into generating marketing insights.

We will start at the heart of the personalization agent and build the component that will generate the strategic actions: