Customer Filters and Segmentation
Overview
Customer filters allow you to segment your customer base using specific criteria. Find customers who meet certain conditions, create targeted marketing campaigns, and analyse customer behaviour patterns. Combine multiple filters to create precise customer segments for personalised communication.
Key benefits:
Target specific customer groups with relevant offers
Identify at-risk customers who need re-engagement
Find your most valuable customers for VIP treatment
Analyze customer behavior patterns
Clean your database by finding incomplete profiles
Accessing customer filters
Navigate to the left-hand menu
Click the Customers icon
Select the My Filters tab at the top of the screen
Available filter parameters
Customer information filters
Name Filter by customer's first or last name.
Equal to / Not equal to specific name
Contains specific text
Use for finding specific customers or name patterns
Email Filter by email address.
Equal to / Not equal to specific email
Contains specific domain (e.g., @gmail.com)
Find customers from specific email providers
Phone Filter by phone number.
Equal to / Not equal to specific number
Contains specific digits or area code
Identify customers by region or phone pattern
Gender Filter by customer gender.
Male / Female / Other / Unknown
Target gender-specific promotions
Identify profiles with missing gender data
Customer birthday Filter by birth date or birth month.
Specific date range
Birth month (e.g., all January birthdays)
Age ranges
Create birthday campaigns or age-targeted offers
Registration date Filter by when customer profiles were created.
Date range (from date to date)
Specific period (e.g., last 30 days)
Find new vs. long-time customers
Analyze acquisition trends
Card-related filters
Card balance Filter by points, stamps, or monetary balance on customer cards.
Equal to / Not equal to
Greater than / Less than
Range of values (e.g., 50-100 points)
Find customers close to rewards or with high balances
Card serial number Filter by specific card identifier.
Equal to specific serial number
Useful for tracking specific card batches or issues
Card status Filter by card installation status.
Installed (active cards on devices)
Not installed (issued but not activated)
Deleted (removed from devices)
Identify customers who need installation assistance
Current number of uses Filter by how many times cards have been used.
Equal to / Greater than / Less than
Range of values
Find frequent users or inactive cardholders
Number of stamps Filter by stamp count on stamp cards.
Equal to / Greater than / Less than
Range of values
Identify customers close to rewards
Unused rewards Filter by available rewards customers haven't redeemed.
Has unused rewards
Number of unused rewards
Encourage redemption with targeted reminders
Technical filters
Device type Filter by platform customers use.
iOS (iPhone/iPad)
Android
Unknown
Send platform-specific instructions or offers
UTM tag Filter by acquisition source or campaign.
Equal to specific UTM tag
Multiple UTM tags
Analyze which marketing channels work best
Target customers from specific campaigns
Filter operators and logic
Comparison operators
Equal to: Exact match to specified value.
Example: Card balance equal to 100
Not equal to: Any value except the specified one.
Example: Gender not equal to Unknown
Greater than / Less than: Numerical comparisons.
Example: Card balance greater than 50
Example: Number of uses less than 3
Range of values: Between two values (inclusive).
Example: Registration date from 2024-01-01 to 2024-12-31
Example: Card balance from 50 to 100
Contains: Text contains specified string.
Example: Email contains "@gmail.com"
Example: Name contains "Smith"
Array of values: Equal to one from a list.
Example: UTM tag equals one of: facebook, instagram, twitter
Combining filters
AND condition: Customers must meet ALL specified criteria.
Example: Card balance > 100 AND Device type = iOS
Result: iOS users with more than 100 points
OR condition: Customers meet ANY of the specified criteria.
Example: Card balance > 100 OR Number of uses > 10
Result: Customers with high balance or high usage
Complex combinations: Mix AND/OR conditions for precise targeting.
Example: (Card balance > 50 AND Device = iOS) OR (Card balance > 100 AND Device = Android)
Pre-built filter groups
Filter groups are ready-to-use presets that combine multiple filters for common use cases. Apply entire groups with one click or use individual filters from the group.
RFM Segments
RFM (Recency, Frequency, Monetary) analysis segments customers based on visit timing and frequency. The system automatically categorizes customers into segments based on their behavior.
How RFM works:
Recency: How recently customers visited (days since last visit)
Frequency: How often customers visit (number of visits)
Segments: Automatic classification based on these metrics
RFM segment definitions:
Last visit: 0-30 days (Recent customers)
0-3 visits: RFM - Beginners
New customers just starting their journey
Need nurturing and welcome engagement
4-7 visits: RFM - Growths
Developing loyalty, showing consistent interest
Ready for rewards and incentives
8-12 visits: RFM - Champions
Your best active customers
High value, frequent visitors
Last visit: 31-60 days (Moderately recent)
0-3 visits: RFM - Doubtful
Limited engagement, risk of leaving
Need re-engagement campaigns
4-7 visits: RFM - Medium (Borderline)
Previously engaged, now slowing down
Target with special offers to retain
8-12 visits: RFM - Loyal - Regular
Strong history but recent pause
Remind them of your value
Last visit: 61-90 days (At risk)
0-3 visits: RFM - Sleeping
Minimal engagement, likely inactive
Win-back campaigns needed
4-7 visits: RFM - At Risk
Good history but fading away
Urgent re-engagement required
8-12 visits: RFM - Needs Attention
Valuable customers becoming inactive
Priority retention efforts
Using RFM segments:
Target Champions with VIP rewards
Re-engage At Risk customers with special offers
Welcome Beginners with onboarding campaigns
Win back Sleeping customers with incentives
Health Group
The Health group identifies customers with incomplete data or inactive cards. Use these filters to clean your database and improve data quality.
Available Health filters:
Card status not installed
Customers issued cards but haven't activated them
Send installation reminders
Provide setup assistance
Unknown gender
Customers with missing gender information
Request profile completion
Update records for better targeting
Empty birthday
Customers without birth dates recorded
Collect birthday data for campaigns
Improve personalization opportunities
Using Health filters:
Identify incomplete profiles
Send data completion requests
Improve targeting accuracy
Clean your customer database
Loyalty Group
The Loyalty group identifies active, engaged customers based on card installation and platform usage.
Available Loyalty filters:
Card status installed
Active customers with cards on their devices
Your engaged customer base
Prime targets for promotions
Apple Wallet
Customers using iOS/Apple Wallet
Send iOS-specific communications
Optimize for Apple platform features
Wallet Passes
Customers using generic wallet apps
Platform-agnostic card holders
Google Pay
Customers using Android/Google Wallet
Send Android-specific communications
Optimize for Google platform features
PWA (Progressive Web App)
Customers using home screen installation
Web-based card access
Using Loyalty filters:
Target active users with new offers
Send platform-specific instructions
Reward engaged customers
Analyze platform preferences
Creating custom filters
Step-by-step filter creation
Step 1: Choose your parameter Select the customer attribute you want to filter by (balance, device, date, etc.).
Step 2: Select operator Choose how to compare (equal to, greater than, contains, etc.).
Step 3: Enter value Specify the comparison value, range, or list.
Step 4: Add more filters (optional) Combine multiple filters with AND/OR logic.
Step 5: Save filter Save your custom filter for future use.
Filter examples
High-value customers:
Card balance > 100
Number of uses > 10
Card status = Installed
Re-engagement campaign:
Last visit 60-90 days ago
Number of uses > 5
Unused rewards > 0
Birthday campaign:
Birthday month = Current month
Card status = Installed
Installation follow-up:
Card status = Not installed
Registration date = Last 7 days
Platform-specific:
Device type = iOS
Card balance > 50
Using filters effectively
Marketing campaigns
Targeted promotions: Create customer segments and send relevant offers:
VIP rewards for Champions
Win-back offers for At Risk customers
Welcome bonuses for Beginners
Timing campaigns: Use date and recency filters:
Birthday specials
Anniversary rewards
Re-engagement after 60 days
Platform optimization: Send platform-appropriate content:
iOS users: Apple Wallet features
Android users: Google Pay benefits
Database maintenance
Data quality:
Find incomplete profiles (Health group)
Request missing information
Update customer records
Inactive customers:
Identify not-installed cards
Send installation reminders
Clean up old records
Engagement analysis:
Compare RFM segments over time
Track customer lifecycle stages
Measure retention effectiveness
Best practices
Start simple: Begin with pre-built groups before creating complex custom filters.
Test filters: Review filtered results before sending campaigns to ensure accuracy.
Document filters: Name custom filters clearly and note their purpose.
Regular review: Update filters as your business needs change.
Combine strategically: Use AND/OR logic thoughtfully to avoid overly narrow segments.
Frequently asked questions
How many filters can I combine? You can combine multiple filters. The system supports complex filter combinations with AND/OR logic.
Can I save custom filters? Yes, save frequently used filter combinations for quick access in future campaigns.
Do filters update in real-time? Yes, filters reflect current customer data when applied.
Can I export filtered customer lists? Yes, apply filters then use the export function to download the filtered list.
What's the difference between RFM segments and custom filters? RFM segments are automatic classifications based on visit patterns. Custom filters let you create any criteria combination.
How often are RFM segments updated? RFM segments update automatically based on current customer visit data.
Can I edit pre-built filter groups? Pre-built groups are fixed, but you can use them as templates and modify individual filters to create custom versions.
What happens if no customers match my filters? The system returns an empty result. Try adjusting filter criteria to broaden the search.
Can I see how many customers match before applying? Yes, the system typically shows the count of matching customers when you create or apply filters.
Should I use AND or OR for multiple filters? Use AND when customers must meet all criteria (narrower results). Use OR when customers can meet any criteria (broader results).