What do you mean advanced segmentation?
Advanced segmentation is the practice of going beyond basic filters, such as location or device type, and grouping subscribers based on meaningful actions and interactions. This includes behaviors like the pages they visit, the links they click, the features they use, and other engagements with your brand across your website, app, or emails.
Advanced segmentation features to look for
Here are the advanced segmentation features you should look for in your ESP:
- Behavioral segmentation
Behavioral segmentation groups users based on how they interact with your product, website, and marketing channels. This includes actions like downloading an app, completing signup, starting a free trial, opening or clicking emails, using specific features, upgrading a plan, or making a purchase.
To support this, your ESP should be able to capture behavioral events across channels and convert them into usable segments. Look for platforms that support event tracking through APIs or native integrations, making it possible to pull in app data
Time-based and recency filters
Time-based and recency filters allow you to segment users based on when an action occurred rather than simply where it occurred. Common examples include filters like last email opened, last website visit, or last purchase date.
To use this effectively, your ESP should let you define flexible time windows, such as users active in the last 7, 30, or 90 days. This makes it easier to run re-engagement campaigns, win-back flows, or prioritize outreach to users who are currently active or showing signs of renewed interest.
Multi-condition logic and nested rules
Advanced segmentation platforms allow AND/OR logic, nested groups, and combinations of behavioral, profile, and event criteria for surgical targeting.
For example, target users who opened at least three emails AND clicked a product link OR visited the checkout page in the past 10 days. Look for platforms that offer both a drag-and-drop interface for non-technical users and an advanced query option for analysts.
Dynamic segmentation
Dynamic segmentation is a method of automatically updating audience groups in real time. Users are added to a segment when they meet the defined criteria and removed when they no longer qualify, ensuring that each segment always reflects the most relevant and up-to-date audience.
In an ESP, dynamic segments rely on real-time data integration from multiple sources such as CRMs, e-commerce platforms, and web analytics tools. The ESP continuously monitors these data streams and recalculates segment membership automatically, eliminating the need for manual updates and ensuring accuracy at scale.
CDP-driven audiences and cross-system segmentation
Customer Data Platforms (CDPs) centralize data from all your touchpoints like website behavior, app usage, purchase history, support interactions, and offline activity into a single, continuously updated customer profile. Instead of recreating the same audience in multiple tools, a CDP allows you to define segments once and sync them across email, CRM, ads, and other marketing systems.
For example, you can create a “high-value webinar attendees” segment inside your CDP and have it automatically update and activate across campaigns, sales outreach, retargeting ads, and lifecycle automation. This eliminates manual duplication, ensures consistency, and guarantees that the same audience receives coordinated messaging across channels.
Prompt-based segmentation
With the rise of AI, it has become possible to create segments now allow marketers to create segments using simple prompts instead of manual rule building. Instead of selecting filters and conditions one by one, you can describe your use case in plain language and instantly generate a working segment.
With Mailmodo, creating advanced segments is as easy as typing a simple prompt. Marketers can describe the audience they want in plain language, like “users who signed up in the last 30 days and clicked a product link” and Mailmodo’s AI automatically generates the segment, suggests relevant variations based on behavior and past campaign performance, and helps identify high-impact users for personalized targeting.
Final thoughts
Now you know some of the key features to look for if you want to do advanced segmentation, the next step is putting them into practice to make your campaigns smarter and more relevant.
Once your data is organized, you can begin defining meaningful segments that reflect real user intent and behavior. Finally, combine these segments with automation to ensure they update dynamically, keeping your campaigns timely, relevant, and highly personalized.
