Building Klaviyo Segments From Store Behavior

Segmentation analysis is worth nothing until it lives inside the tool that sends the emails. This guide translates the segments you have already defined, RFM (recency, frequency, monetary) tiers, discount-dependent buyers, your top 20%, and product affinity pairs, into concrete Klaviyo segment definitions you can build in the condition builder today. Each recipe below spells out the exact conditions, in Klaviyo's own language, so you can rebuild them from scratch.

Klaviyo segments are live. They evaluate the conditions continuously, so a profile enters and leaves as its behavior changes. That is what makes them the right home for behavioral segments: define the rule once, and the audience stays current without a manual re-upload.

How Klaviyo's Condition Builder Works

Every Klaviyo segment is a set of conditions joined by AND and OR. The condition you will use most is "What someone has done (or not done)," built on a metric like Placed Order. That metric takes a frequency and a timeframe, and you can stack filters on its dimensions.

The pieces of a Placed Order condition are:

CopyWhat someone has done (or not done)
  → Placed Order
  → [at least once | at least N times | equals N times | zero times]
  → [over all time | in the last N days | between dates | since date]
  → Add filter (optional): where [dimension] [operator] [value]
     dimensions include: Discount Code, $value (order value),
     Items, Product Name, Collections, Product Categories

Two other condition types matter here. "Properties about someone" reads profile fields, including Klaviyo's predictive ones like Historic Customer Lifetime Value and Predicted Customer Lifetime Value. And "If someone is or is not in a list or segment" lets you compose segments from other segments. With those three, every recipe below is buildable.

Five Segment Recipes, With Exact Conditions

Each recipe maps one analytical segment to a Klaviyo definition. Build each as a new segment (Lists and segments, then Create segment, then Create list or segment, then Segment), paste in the conditions, and name it as shown. Set frequency and timeframe values to match your own catalog and order cadence; the numbers below are sensible D2C starting points.

Recipe 1: High-Value Repeat Buyers (Your Top 20%)

Target the customers who drive most of your revenue: repeat buyers with high lifetime value who are still active. This is the audience for early access, loyalty perks, and no discounts, because they do not need them.

CopySomeone is in this segment if ALL of the following:
  Placed Order at least 3 times over all time
  AND Historic Customer Lifetime Value is at least 300
  AND Placed Order at least once in the last 90 days

The first two conditions define value; the third keeps lapsed customers out so you are not calling churned buyers your best. Set the CLV floor to roughly the top-quintile threshold from your own analysis. See identifying your top 20% of customers for how to find that number.

Recipe 2: At-Risk High-Value Customers (Win-Back)

Target previously good customers who have gone quiet. This is the classic RFM "at risk" tier: strong monetary and frequency, weak recency. Catching them before they fully churn is far cheaper than acquiring a replacement.

CopySomeone is in this segment if ALL of the following:
  Placed Order at least 2 times over all time
  AND Historic Customer Lifetime Value is at least 200
  AND Placed Order zero times in the last 120 days

Set the recency window to a multiple of your average time between orders, not a round guess. If customers typically reorder every 45 days, 120 days of silence is a real signal. If they reorder twice a year, it is not. The RFM segmentation guide for Shopify covers how to set those thresholds from your data.

Recipe 3: Discount-Dependent Customers

Target customers who only ever buy on promotion. Isolating them protects margin: you can hold them out of full-price campaigns, and stop bleeding discount into orders that would have happened anyway. The trick is two conditions, one for "has used a code" and one for "has never bought without one."

CopySomeone is in this segment if ALL of the following:
  Placed Order at least 2 times over all time
    where Discount Code is set
  AND Placed Order zero times over all time
    where Discount Code is not set

The second condition is what makes this precise. It excludes anyone who has ever paid full price, leaving only customers whose every order carried a code. Loosen it to "at least 2 times where Discount Code is set" alone if you want everyone who leans on discounts, not only the pure cases. For what to do with this group, see handling discount-dependent customers.

Recipe 4: One-Time Buyers to Convert

Target recent first-time buyers who have not come back yet. The single biggest retention lever for most D2C stores is the first-to-second purchase, and this segment is the audience for a second-order nudge before they cool off.

CopySomeone is in this segment if ALL of the following:
  Placed Order exactly 1 time over all time
  AND Placed Order at least once in the last 60 days

The 60-day window keeps the segment warm. A first-time buyer from two years ago belongs in a win-back flow, not a second-purchase nudge. Tighten the window if your product has a short natural reorder cycle, loosen it for considered, infrequent purchases.

Recipe 5: Product Affinity Cross-Sell

Target buyers of one product who have not bought its natural companion. This is affinity analysis turned into an audience: if reed refills sell strongly to diffuser buyers, build the segment of diffuser buyers who have never ordered a refill and send them one.

CopySomeone is in this segment if ALL of the following:
  Placed Order at least once over all time
    where Product Name contains Diffuser
  AND Placed Order zero times over all time
    where Product Name contains Reed Refill

Swap Product Name for Collections if you sell many variants and want to match a whole category rather than one title. Use Collections contains for the anchor and zero times for the companion collection. The pairs worth building this around come straight out of product affinity analysis, ranked by lift so you are cross-selling genuine relationships rather than coincidences.

A Note on Klaviyo's Predictive Segments

Klaviyo offers predictive properties, Predicted CLV, Churn Risk Prediction, and Expected Date of Next Order, that can replace some manual conditions. They are useful, but honest scoping matters: Klaviyo only computes them once a store has enough order history, roughly 500 orders and 180 days for a customer to receive predictions. Below that volume the fields are blank and any segment built on them stays empty.

When the predictions are populated, they make good conditions. "Churn Risk Prediction is high" is a cleaner win-back trigger than a fixed day count, and "Predicted Customer Lifetime Value is at least X" sharpens the top-20% segment. Treat them as an upgrade to the recipes above once you have the data volume, not a replacement for understanding the underlying behavior.

From Segment to Send

Building the segment is step one. Point a flow or campaign at it and the definition earns its keep. High-value buyers get early access and loyalty perks. At-risk customers get a win-back sequence. Discount-dependent buyers get held out of full-price sends. One-time buyers get a second-purchase nudge. Affinity segments get a targeted cross-sell.

Keep the conditions in sync with the analysis behind them. When your reorder cadence or your top-quintile CLV shifts, update the thresholds so the segments keep meaning what their names claim. For how these tiers fit together as one system, see the customer segmentation guide.

Frequently Asked Questions

What is the difference between a list and a segment in Klaviyo?

A list is a static set of profiles you add to manually or through a signup form; membership does not change on its own. A segment is dynamic and defined by conditions, so profiles enter and leave automatically as their behavior or properties change. Behavioral audiences like repeat buyers or at-risk customers belong in segments, since they need to stay current without a manual re-upload every time someone places an order.

How do I build a segment of customers who only buy with a discount?

Use two conditions joined by AND. First, Placed Order at least 2 times over all time where Discount Code is set. Second, Placed Order zero times over all time where Discount Code is not set. The second condition excludes anyone who has ever paid full price, leaving only customers whose every order used a code. Confirm your Shopify discounts populate the Discount Code dimension, since automatic promotions sometimes do not.

How do I segment customers by which product they bought?

Add a filter to a Placed Order condition. Choose Placed Order at least once over all time, then Add filter and set where Product Name contains your product, or where Collections contains a category. To find buyers of one product who skipped a companion, add a second condition: Placed Order zero times over all time where Product Name contains the companion item. That gives you a ready cross-sell audience.

Can I use Klaviyo predictive analytics for segmentation?

Yes, once your store has enough data. Klaviyo computes predictive properties like Predicted CLV, Churn Risk Prediction, and Expected Date of Next Order after roughly 500 orders and 180 days of history. Below that they stay blank and segments built on them are empty. When populated, they make strong conditions, for example Churn Risk Prediction is high as a win-back trigger, but they supplement behavioral rules rather than replacing the need to understand them.

How often should behavioral segments update in Klaviyo?

Segments update automatically and continuously, so you do not refresh them manually. What needs periodic review is the thresholds inside them. Revisit your CLV floors, recency windows, and reorder cadence every quarter or after a major pricing or catalog change, since the numbers that defined your top tier or your at-risk window can drift. The segment stays live, but its conditions should keep matching current behavior.

The Takeaway

A behavioral segment is only as good as the conditions behind it, and Klaviyo's condition builder can express every tier your analysis produced: high-value repeat buyers, at-risk customers, discount-dependent buyers, one-time buyers, and affinity cross-sells. Build each from the exact conditions above, set the thresholds from your own data, and point a flow at it.

These recipes are the activation layer for the rest of the segmentation work. Start from the customer segmentation guide, size each tier with RFM segmentation and your top 20% analysis, then build the segments here.