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Filtering Customers in Baremetrics

Turn your spaghetti mess of customers into clean segments.

Allison Barkley avatar
Written by Allison Barkley
Updated today

You can slice and dice your customer data in powerful ways using filters in Baremetrics. Whether you're analyzing churn, segmenting high-value accounts, or targeting trial users, filters help you zoom in on exactly the group you care about.

This guide breaks down all available filters, pulled from Stripe, your billing behavior, and enrichment tools like Clearbit, so you can create precise segments and gain the insights you need quickly.

Related Guides

This guide covers all default filters available in Baremetrics. If you're passing in custom attributes, don’t worry - we’ll automatically create a filter for every one of those too.

Let’s dig in 👇

👤 Customer Info

  • Email Address: Filter by full or partial (e.g., @acme.com)

  • Provider / Source: Identify payment provider/billing source (e.g., Stripe or multiple Stripe instances)

  • Signup Date: Track cohorts or churn over time

  • Unsubscribed from Emails: See who’s opted out (Messaging, Recover, etc.)

  • Customer ID: Payment Provider's unique identifier for a customer

  • Notes: Search internal notes saved to a customer's profile


💳 Payment Activity

  • Total Charges Count: Count of all charges, ever

  • Recurring Charges Count / Value: Subscription-only payments

  • One-Time Charges Count / Value: Non-subscription purchases
    → Spot high spenders, upsells, or freemium upgrades


💰 Revenue & Subscription

  • Current MRR: Today’s monthly recurring revenue

  • LTV: Total customer spend

  • Plan / Plan Months / Canceled Plan: What they signed up for, and how long

  • Cancellation Reason: Stripe-provided feedback

  • Current Stripe Products: Active product identifiers

  • Last Known MRR / Plan / Products: For analyzing current and churned customers

    → Useful for churn analysis and cohort comparisons


📉 Lifecycle Status

  • Canceled?: True if the account is canceled

  • Active Subscription? / Count: Who’s still subscribed, and how many subscriptions

  • Is Trialing?: In trial phase

  • Is Delinquent?: Payment failed but not canceled

  • Cancellation / Conversion / Delinquent Date: Key lifecycle milestones


🏢 Enrichment Data (via Clearbit)

Company

  • Name / Description / Tags

  • Location: Country, state, city

  • Industry / Sector / Sub-industry

  • Tech Stack: e.g., “uses HubSpot”

  • Employees / Revenue / Founded Year

  • Twitter Followers / Amount Raised

Person

  • Full Name / Email

  • Country / Gender

  • Role / Seniority: e.g., “CEO”, “Director”

  • Twitter Followers: Social reach proxy


💡 Example Use Cases

Here are some common ways you can combine filters to answer key questions or take action on specific segments.

Goal

Filters to Use

Why It’s Useful

Find churn risks before they cancel

Is Delinquent? = true
Active Subscription? = true
LTV > $500

Identify valuable customers with failed payments before they churn

Compare churn rates for monthly vs. annual plans

Canceled? = true
Group by Last Known Plan Months or Last Known Plan
Optional: Segment by Signup Date or Company Size

Include churned accounts to compare how plan intervals impact retention.

Spot lost revenue from big accounts

Canceled? = true
Last Known MRR > $1,000
Company Employees > 100

Focus on enterprise churn with high revenue impact

Re-engage trial users who ghosted

Is Trialing? = false
Active Subscription? = false
Signup Date = 14–30 days ago

Find users who finished the trial but never converted

Identify usage-based churn

Canceled? = true
Last Known Stripe Products = “API Access”
Plan Months < 2

Spot users who churn quickly after light usage

Find happy customers to ask for reviews

Active Subscription = true
Total Charges Count > 12
Unsubscribed from Emails = false

Target long-time, paying, engaged customers for advocacy

Upsell Opportunities

One-Time Charges Value > $200
Recurring Charges Count > 1

Find engaged customers with a history of spending

Measure campaign success by source

UTM = “Google Ads”
Signup Date last 90 days
Group by LTV

Analyze ROI of recent paid acquisition campaigns

Compare ARPU by customer size

- Bucket 1: Last Known MRR < $100
- Bucket 2: $100 ≤ Last Known MRR < $500
- Bucket 3: $500 ≤ Last Known MRR

Understand where the most revenue comes from, even if customers have churned. This avoids excluding $0 Current MRR users who paid substantially in the past.

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