# Customer Segmentation
By understanding who your customers are and how they behave, you can tailor messaging, using smart filters to deliver first-class communications.
## Best Practices
When configuring customer segments, it’s important to consider the following:
* Define what outcome you want, whether it be repeat purchases, reduced returns, reactivation, or upsell
* Base segments on real actions like order frequency, return rate, or spend
* Use criteria that support specific messaging and campaign logic
* Use A/B testing and analytics to refine segments and messaging
* Trigger automated campaigns for each segment for consistent, relevant communication
## Use Cases
Find practical segmentation examples that help you deliver personalized post-purchase experiences and drive long-term value.
Repeat Customers with Minimal Returns
**Use case:** Identify loyal customers that purchase frequently and rarely return items through exclusive loyalty programs and personalized campaigns.
**Segment settings:**
* Average days between order ≤ 14
* Total article return rate ≤ 5%
**Goal:** Increase customer lifetime value and drive continued growth through tailored engagement.
**Copy the code:**
Speed up your setup by simply copying and pasting the segment settings directly into the Smart Segmentation filter for an audience.
```json
{
"$and": [
{
"customer_segment.outbounds_avg_time_delta": {
"$lte": 14
}
},
{
"customer_segment.returns_articles_ratio": {
"$lte": 5
}
}
]
}
```
High Spending Recent Buyers
**Use case:** Engage recent big spenders with targeted offers and premium product recommendations.
**Segment settings:**
* Last purchase date ≤ dd/mm/yyyy – interval ’14 days’
* Total amount of purchase value ≥ $1,000
**Goal:** Capitalize on current interest in your brand and convert into repeat buyers.
**Copy the code:**
Speed up your setup by simply copying and pasting the segment settings directly into the Smart Segmentation filter for an audience. Don't forget to add the required last purchase date as well!
{
"$and": [
{
"customer_segment.last_purchase_date": {
"$lte": "yyyy-mm-dd"
}
},
{
"customer_segment.outbounds_value_total": {
"$gte": 1000
}
}
]
}
Lapsed Frequent Buyers
**Use case:** Target previously frequent shoppers who haven’t purchased recently using personalized win-back campaigns and loyalty incentives.
**Segment settings:**
* Average days between order ≤ 14
* Last purchase date ≤ dd/mm/yyyy – interval ’60 days’
**Goal:** Reengage customers with your brand and reactivate their purchasing behavior.
**Copy the code:**
Speed up your setup by simply copying and pasting the segment settings directly into the Smart Segmentation filter for an audience. Don't forget to add the required last purchase date as well!
```json
{
"$and": [
{
"customer_segment.outbounds_avg_time_delta": {
"$lte": 14
}
},
{
"customer_segment.last_purchase_date": {
"$lte": "yyyy-mm-dd"
}
}
]
}
```
Customers with High Return Risk
**Use case:** Focus on customers with high return rates by proactively addressing pain points through tailored retention efforts, such as enhanced product information.
**Segment settings:**
* Total article return rate ≥ 40%
**Goal:** Improve customer satisfaction and reduce return rates among these at-risk customers.
**Copy the code:**
Speed up your setup by simply copying and pasting the segment settings directly into the Smart Segmentation filter for an audience.
```json
{
"customer_segment.returns_articles_ratio": {
"$gte": 40
}
}
```
Premium Customers, Frequent Returners
**Use case:** Aim to retain high-spending customers who also return items often with proactive support and personalized content, such as improved product fit service.
**Segment settings:**
* Total amount of purchase value ≥ $1,000
* Total article return rate ≥ 30%
**Goal:** Improve the customer experience to reduce return rate while maintaining retention.
**Copy the code:**
Speed up your setup by simply copying and pasting the segment settings directly into the Smart Segmentation filter for an audience.
```json
{
"$and": [
{
"customer_segment.outbounds_value_total": {
"$gte": 1000
}
},
{
"customer_segment.returns_articles_ratio": {
"$gte": 30
}
}
]
}
```
Frequent Low Value Buyers
**Use case:** Identify customers with frequent but low value orders with targeted incentives, such as free shipping or discounts on bundled products.
**Segment settings:**
* Average basket size ≤ 2
* Total amount of orders ≥ 10
**Goal:** Encourage customers to increase their order value and maximize overall revenue per transaction.
**Copy the code:**
Speed up your setup by simply copying and pasting the segment settings directly into the Smart Segmentation filter for an audience.
```json
{
"$and": [
{
"customer_segment.outbounds_avg_basket_size": {
"$lte": 2
}
},
{
"customer_segment.outbounds_number_total": {
"$gte": 10
}
}
]
}
```
***
{% hint style="info" %}
#### Check out the parcelLab guides
For further information on using smart segmentation, see our user guide documentation:
* [Set up target audiences for your communications](https://how.parcellab.works/guides/experience-design/setup/audiences)
* [Schedule personalized campaigns](https://how.parcellab.works/guides/experience-design/automation/campaigns)
{% endhint %}
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