Demographic data tells you who your users are. Behavioral data tells you what they do. For e-commerce, what users do (what they buy, how often, how much they spend) is far more predictive of future behavior than their age, location, or job title. Purchase-based segments let you group users by their actual buying patterns and target them with relevant campaigns.
This article covers the segment rules available for e-commerce data, how to combine them into useful audience definitions, and how to use those audiences across smart banners, in-app messages, and deep link routing.
For segment filter rules and configuration, see the audience filter rules documentation. For the e-commerce events that power segmentation, see the e-commerce analytics guide.
Why Behavioral Segmentation Wins
Consider two users of a fashion e-commerce app:
- User A: Female, 28, lives in Chicago, signed up 6 months ago
- User B: Has made 12 purchases, average order value of $94, last purchased 3 days ago, buys primarily from the "shoes" category
Which user would you rather target with a new shoe collection campaign? User B, obviously. You know nothing about User B's demographics, but you know everything that matters: they buy shoes, they buy frequently, they spend well, and they are currently active.
This is the core argument for behavioral segmentation. Research from McKinsey consistently shows that purchase-based personalization drives 10-15% higher revenue than demographic targeting alone. The data you already have from your e-commerce events is more valuable than any third-party audience data you could buy.
The Eight E-Commerce Segment Rules
Tolinku provides eight segment rules specifically for e-commerce data. Each rule evaluates a user's purchase history and returns true or false, determining whether the user belongs to the segment.
1. total_revenue
Segment users by their cumulative spend (in your base currency).
| Operator | Example | Matches |
|---|---|---|
| greater_than | total_revenue > 500 | Users who have spent more than $500 |
| less_than | total_revenue < 50 | Users who have spent less than $50 |
| between | total_revenue between 100 and 500 | Mid-tier spenders |
Why it matters: Revenue is the most direct measure of customer value. Segmenting by total revenue lets you identify your VIPs and treat them accordingly.
2. order_count
Segment users by the number of completed purchases.
| Operator | Example | Matches |
|---|---|---|
| greater_than | order_count > 5 | Repeat buyers |
| equals | order_count = 1 | One-time buyers |
| less_than | order_count < 3 | Low-frequency buyers |
Why it matters: Order count measures purchase frequency independent of order size. A user with 20 orders of $15 each is a very different customer than a user with 1 order of $300, even though their total revenue is similar.
3. last_purchase_days
Segment users by the number of days since their most recent purchase.
| Operator | Example | Matches |
|---|---|---|
| less_than | last_purchase_days < 30 | Active buyers (purchased in the last month) |
| greater_than | last_purchase_days > 90 | Lapsed buyers (no purchase in 3 months) |
| between | last_purchase_days between 30 and 90 | At-risk buyers |
Why it matters: Recency is the strongest predictor of future purchases. Users who bought recently are far more likely to buy again than users who have gone quiet. This rule is essential for re-engagement campaigns.
4. avg_order_value
Segment users by their average order value (AOV).
| Operator | Example | Matches |
|---|---|---|
| greater_than | avg_order_value > 100 | High-AOV buyers |
| less_than | avg_order_value < 25 | Low-AOV buyers |
Why it matters: AOV segments help you tailor promotions. High-AOV buyers respond to premium product recommendations. Low-AOV buyers respond to bundles and "spend $X, get free shipping" offers.
5. cart_abandoned
Segment users based on whether they have an abandoned cart.
| Operator | Example | Matches |
|---|---|---|
| equals | cart_abandoned = true | Users with an active abandoned cart |
| count_greater_than | cart_abandoned_count > 3 | Serial cart abandoners |
Why it matters: Cart abandoners are your warmest leads. They have already selected products and started the checkout process. A well-timed reminder with a deep link back to their cart can convert a significant percentage.
6. purchased_item
Segment users based on specific items they have purchased.
| Operator | Example | Matches |
|---|---|---|
| includes | purchased_item includes "SKU-001" | Users who bought a specific product |
| excludes | purchased_item excludes "SKU-042" | Users who have NOT bought a specific product |
Why it matters: Item-level targeting enables cross-sell and upsell campaigns. If a user bought a camera, show them lenses and accessories. If they bought a subscription, do not show them the subscription signup banner.
7. purchased_category
Segment users based on product categories they have purchased from.
| Operator | Example | Matches |
|---|---|---|
| includes | purchased_category includes "shoes" | Users who have bought shoes |
| excludes | purchased_category excludes "electronics" | Users who have never bought electronics |
| count_greater_than | purchased_category_count > 3 | Users who buy across multiple categories |
Why it matters: Category-level segmentation is broader than item-level but more specific than total revenue. It captures shopping preferences without requiring exact product knowledge.
8. coupon_used
Segment users based on whether they have used coupons or discount codes.
| Operator | Example | Matches |
|---|---|---|
| equals | coupon_used = true | Users who have redeemed at least one coupon |
| count_greater_than | coupon_used_count > 5 | Heavy coupon users |
Why it matters: Coupon behavior reveals price sensitivity. Users who never use coupons may be brand-loyal and willing to pay full price. Users who always use coupons will wait for a deal, and targeting them with full-price promotions is a waste.
Building Useful Segments
Individual rules are building blocks. The real value comes from combining them into segments that map to specific business scenarios.
High-Value Customers (VIPs)
total_revenue > 500
AND order_count > 5
AND last_purchase_days < 60
These users spend a lot, buy frequently, and are currently active. They deserve premium treatment: early access to sales, exclusive products, loyalty rewards. Do not waste their attention on generic promotions.
Cart Abandoners (Hot Leads)
cart_abandoned = true
AND last_purchase_days < 90
These users have items in their cart and have purchased before (so they trust your app enough to buy). They are your highest-conversion opportunity. Send them a deep link that returns them to their exact cart.
Lapsed Buyers (Win-Back Targets)
order_count > 2
AND last_purchase_days > 90
AND last_purchase_days < 365
Users who bought multiple times but have gone quiet. They liked your product but something changed. A re-engagement campaign with a discount or new product announcement can bring them back. Deep links to their previously purchased categories work well here.
Category Enthusiasts
purchased_category includes "running-shoes"
AND order_count > 3
Users who repeatedly buy from a specific category. Target them with new arrivals and restocks in that category. These users respond to deep links that land directly on the category page with new items highlighted.
Coupon Hunters
coupon_used_count > 3
AND avg_order_value < 40
Price-sensitive users who buy primarily when there is a deal. Target them with time-limited offers and clearance sales. Do not send them full-price product promotions.
First-Time Buyers (Onboarding)
order_count = 1
AND last_purchase_days < 14
Users who just made their first purchase. They are in the critical window where you can convert them from a one-time buyer to a repeat customer. Send them recommendations based on their first purchase, a loyalty program invitation, or a "complete your look" campaign.
High-AOV Window Shoppers
avg_order_value > 150
AND order_count = 1
AND last_purchase_days > 30
Users who made one large purchase but have not returned. They are willing to spend but have not formed a habit. A targeted campaign with premium products in their purchase category can trigger a second purchase.
Using Segments in Campaigns
Segments become powerful when you connect them to delivery channels. Here are the three primary channels in Tolinku.
Smart Banners
Smart banners appear at the top of your mobile web pages, prompting users to open the app. With audience segments, you can customize the banner message and deep link destination:
| Segment | Banner Message | Deep Link Target |
|---|---|---|
| Cart abandoners | "Your cart is waiting. Open the app to checkout." | Cart screen |
| Lapsed buyers | "New arrivals in [their category]. Open the app to browse." | Category page with new items |
| VIPs | "Exclusive early access to our summer sale." | Sale landing page |
| First-time buyers | "Download the app for 10% off your next order." | App store with deferred deep link |
The key is relevance. A generic "Open in app" banner performs far worse than a banner that speaks to the user's actual behavior.
In-App Messages
For users already in the app, in-app messages can surface segment-specific promotions:
- VIPs see early access notifications for new products
- Cart abandoners see a reminder about their saved items
- Category enthusiasts see new arrivals in their preferred categories
- Coupon hunters see active promotions and deals
Deep Link Routing
Segments can also influence where a deep link sends the user. A deep link to your homepage could route to different screens based on the user's segment:
- VIP users land on a curated "For You" page
- Lapsed users land on a "Welcome Back" page with a re-engagement offer
- New users land on an onboarding flow
This requires server-side segment evaluation at link resolution time, which Tolinku supports through its routing rules.
Segment-Driven Re-Engagement Campaigns
The most effective re-engagement campaigns combine segments with timing and channel selection:
The Win-Back Sequence
- Day 30 (user enters "at-risk" segment): Send push notification with deep link to personalized recommendations
- Day 45 (user still in segment): Send email with curated products from their favorite categories
- Day 60 (user still in segment): Send push notification with a discount code and deep link to the offer
- Day 90 (user enters "lapsed" segment): Final email with "We miss you" messaging and a significant incentive
Each touchpoint uses a deep link that opens the app directly to relevant content. No home screen, no navigation, no friction.
The Loyalty Ladder
Use milestone-based segments to progressively reward customers:
| Milestone | Segment Rule | Action |
|---|---|---|
| First purchase | order_count = 1 | Welcome email, loyalty program invite |
| 5th purchase | order_count >= 5 | Silver tier unlock, 5% ongoing discount |
| $500 lifetime spend | total_revenue >= 500 | Gold tier unlock, free shipping |
| 10th purchase | order_count >= 10 | VIP access, early sale access |
Measuring Segment Performance
Track these metrics for each segment to evaluate your targeting effectiveness:
| Metric | What It Tells You |
|---|---|
| Segment size | How many users match the criteria |
| Conversion rate | Percentage of segment members who take the desired action |
| Revenue per segment member | Average revenue generated from users in this segment |
| Segment growth/decline rate | Whether the segment is growing (good for VIPs, bad for lapsed) |
| Campaign lift | Revenue difference between targeted segment members and a control group |
Review segment definitions quarterly. As your user base evolves, thresholds that made sense six months ago may no longer produce meaningful groupings.
Conclusion
Purchase-based audience segments let you move beyond "blast everyone" marketing to targeted campaigns that match the user's actual behavior. The eight e-commerce segment rules (total_revenue, order_count, last_purchase_days, avg_order_value, cart_abandoned, purchased_item, purchased_category, coupon_used) provide the building blocks for any segmentation strategy.
The most effective segments combine multiple rules to identify specific behavioral patterns: VIPs, cart abandoners, lapsed buyers, category enthusiasts. Connect those segments to smart banners, in-app messages, and deep link routing to deliver the right message to the right user at the right time.
For segment configuration and filter rules, see the audience filter rules documentation. For the e-commerce data that powers segmentation, see the e-commerce analytics guide and e-commerce analytics features.
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