Here's the truth nobody wants to admit: traditional rule-based segmentation is broken. Not because it doesn't work – it absolutely does when done right – but because doing it right is fundamentally unsustainable for most brands and agencies.

Most marketers have a good sense of what they should be doing with segmentation, from creating behavioral pattern segments to matching content to segment for improved deliverability. This is why Klaviyo reports highly segmented lists return more than 3X the revenue per recipient of unsegmented lists.

But who has the time? The reality is maintaining proper rule-based segmentation is brutally time-intensive:

• Hours spent analyzing customer data to identify meaningful patterns

• Regular maintenance of segment rules as customer behavior changes to prevent deliverability decay

• Constant monitoring to ensure segments don't become too small or too broad

• Weekly or monthly updates to keep segments relevant

• Time-intensive testing to validate segment performance

• Additional content creation for each segment you add

The "Good Enough" Segmentation Trap

This is why most brands fall into what I call the "good enough" segmentation trap. You know the one – create your standard engagement segments (30/60/90 days), maybe a win-back segment, and call it a day. It's not ideal, but it's what you can manage given everything else on your plate.

Even though none of us are going in with the goal of being just "good enough", this trap is seductive because it feels responsible. You're doing segmentation, right? You're following best practices. You're not batch-and-blasting to your entire list as one segment (and if you are, stop reading right now and install Raleon because it will be the fastest win you've ever seen).

But here's what's actually happening because of this segmentation trap:

• Revenue leakage from poorly targeted emails and missed cross-sell opportunities

• Lower customer lifetime value from poor targeting

• Deliverability decay from sub-optimal engagement

• Reduced repeat purchase rates

• Missed reactivation opportunities with lapsed customers

The most successful brands have historically solved this through brute force – either with large internal teams or by hiring specialized agencies to overcome their lack of time. But even then, they're constantly fighting time constraints. More segments mean more content creation, more analysis, more maintenance, and more complexity.

It's a resource problem masquerading as a strategy problem. And this is exactly why AI segmentation isn't just an improvement on rule-based segmentation – it's a completely different approach that solves the fundamental resource constraints that have held email marketers back for years. That's without even getting to the fact it performs better.

AI Segmentation: The Next Phase of Email Segmentation

AI segmentation isn't just an iteration on rule-based segmentation - it's a completely different approach that solves the core problems that have plagued email marketers for years.

Instead of static rules like "purchased in last 90 days AND opened email in last 30 days," AI segmentation uses predictive signals that automatically adjust based on actual customer behavior patterns. It's based on their behavioral intent. Think of it like having a master email strategist working 24/7 to optimize your segments. These patterns all funnel into AI Signals that make creating and managing segments 90% easier. It's a flywheel for your brand that's always running.

In fact, in the last 5 years, buyer intent data has been a huge benefit in the business-to-business marketing space [1]. But it hasn't become as prevalent yet in more of the business-to-consumer (such as DTC and retail) space.

Why Behavioral Intent Works

Deliverability has caused email segmentation to largely be based on engagement. While that's effective, a customers engagement with an email is not an indication of their behavioral intent. Engagement tells you who interacted; behavioral intent tells you who will transact.

The DMA's recent report shows how important this distinction is, as "open rates have nearly doubled while click-through-rates have remained the same" [2].

One looks in the rear-view mirror, the other scans the road ahead. Here's a few ways to think about the difference between predictive, AI segmentation and static email segments of today:

1. Predictive vs Prescriptive: Rather than relying on rigid rules, AI looks at customer behavior patterns in your data to predict future behavior. The results speak for themselves. We've seen brands send emails to 7% of their 90 day engaged list and get 500% better email revenue results.

2. Dynamic vs Static: Traditional segments are like snapshots - they're outdated the moment they're created. AI segments use signals to automatically evolve as customer behavior changes. Next purchase flows are massive problems here. You try to dial in the right timing: 30 days after purchase, 45? In reality it's different for each customer. That's why we've seen a 242% revenue increase on these flows when using AI.

3. Holistic vs Siloed: Instead of looking at isolated data points, AI considers the entire customer journey, finding patterns humans might miss.

We've all been hearing about aspects of behavioral intent for years, under the guise of "personalization". But that's too broad a term, and too often thought of a just changing the text in an email. When done properly, though, companies are driving as much as 25% more revenue through more behavioral-intent focused data [3].

How Behavioral Intent Works with AI Segmentation

Think of AI segments that use behavioral intent to drive them like Waze for your email list. It's always recalculating to find the fastest route to a sale. This is a very different mentality than most lists, which are about whether someone opened your email.

Using behavioral intent to create signals that can be used in segments works across 5 stages:

If you think about rule-based segments like old, printed maps, then behavioral-intent driven segments are like live GPS. They reroute the moment traffic changes.

Getting Started with an AI Segmentation: A Crawl, Walk, Run Approach

The shift from purely rules-based, static segments to predictive, AI segments can feel like a lot. That's why we don't recommend changing everything all at once. It's always best to eat an elephant in small bites. This podcast also covers the most common questions and ways to start on getting start:

Crawl: Start with One Core Customer Journey

The best place to start is with your first purchase to second purchase pipeline. Why? Because this is where most brands leak revenue, and it's a journey that exists for every ecommerce business.

Instead of just sending time-based follow-ups, try these AI-powered segments:

• One-Time Buyer + On-Site Engagement: Target browsers who've purchased once and are showing renewed interest

• One-Time Buyer + Promotion Responsive: Focus discounting on those most likely to convert

• One-Time Buyer + Next Purchase Ready: Prioritize your highest intent group

Action Step: Take your existing post-purchase flow and split it based on these three segments. Create slightly different messaging for each:

• For browsers: Focus on new products in categories they're viewing

• For promotion-responsive: Lead with your offer

• For purchase-ready: Emphasize social proof and urgency

If you're a subscription brand, another really easy starting place is using our Replenishment Ready signal as the trigger for when to hit customers who haven't subscribed yet to rebuy.

Walk: Enhance Your Core Flows

Once you've seen success with your first purchase flow, expand AI segmentation to your other core automations:

Subscription Growth Program Instead of basic post-purchase subscription offers, target:

• Replenishment Ready customers (they're perfect subscription candidates)

• Had Subscription + Replenishment Ready (churned subscribers still buying one-off)

• Subscription History + Return Risk (focus on satisfaction)

Strategic Discount Campaigns Replace blanket promotional emails with:

• Promotion Responsive + On-Site Engagement (high intent)

• Discount Dependent + Next Purchase Ready (conversion focus)

• Loyal but At-Risk + Discount Dependent (strategic reactivation)

Action Step: Choose one flow to enhance each month. Start with your highest-volume automation for maximum impact. Or if you want to do some incremental testing first, start with a more niche flow. Just make sure it gets enough volume to indicate impact.

Run: AI-First Campaigns and Flows

At this stage, you've seen the revenue lift AI segments deliver, and you've seen how easy they are to create and manage. Now it's time to update all your flows and campaigns to be AI segments (and still leveraging important engagement signals). This stage is more strategic and less prescriptive. The best agencies and brands often work through it in the following steps:

1. Update remaining flows

2. Add new flows because of new segment possibilities to maximize customer engagement and revenue per segment

3. Evaluate new segment opportunities for campaigns

4. Create content for those new segments

If you need assistance on the "run" stage or earlier stages, we're also always here to help! We've worked with many agencies and brands around this shift.

Fast Results and Tests with an AI Segmentation Engine

The beauty of the above approach is that you can start seeing results within your first month of implementation, while building toward fully adopting an AI Segmentation Engine. Each phase builds on the last, allowing you to scale based on actual results rather than theory.

The future of email marketing isn't just about having AI segments - it's about using them strategically to create better customer experiences. Start with one journey, prove the concept, then expand. Your customers (and your revenue) will thank you.

Citations

[1] https://zymplify.com/top-55-buyer-intent-data-statistics

[2] https://dma.org.uk/research/email-benchmarking-report-2023

[3] https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying