User Segment Advanced Configuration Guide

To effectively use user segments, proper condition settings are essential.

How to Create User Segments explains the general flow of segment configuration,

This article explains filter conditions for user segments in detail.

Filter Types

User segment filters fall into three main categories.

User Behavior

Visit Attributes

User Labels

1. User Behavior🏃‍♂️

User behavior creates segments based on specific actions on your website. It can be configured from the following 4 items:

Website: Filter by how many times a page was visited / not visited within the past ● days Configuration Example Users who visited product pages 5 or more times in the past 30 days

  Users who have not visited the blog even once in the past 7 days

**Usage Tips**
Identifying highly interested users

  Extracting users requiring re-engagement

Event: Filter by how many times an event occurred / did not occur within the past ● days Configuration Example: Users who added to cart 3 or more times in the past 14 days

  Users who have not submitted the inquiry form even once in the past 30 days

**Usage Tips**:
Identifying users with high purchase intent

  Targeting conversion promotion campaigns

Visit: Regardless of specific pages or actions, filter by how many times the entire site was visited / not visited within the past ● days Configuration Example: Users who visited the site 5 or more times in the past 7 days

  Users who have not visited the site even once in the past 30 days

**Usage Tips**:
Identifying loyal users

  Re-engaging users at high risk of churn

Experience: Filter by which experience pattern was / was not seen within the past ● days Configuration Example: Users who saw Pattern A of A/B test in the past 14 days

  Users who have not seen a specific popup in the past 30 days

**Usage Tips**:
Analyzing A/B test results

  Measuring marketing campaign effectiveness

2. Visit Attributes🌐

Visit attribute data is important information obtained from browser cookies each time a user visits your site. By default, the following 18 types of attribute data can be collected.

Of the following, campaign name, traffic source, medium, keyword, and content correspond to utm_campaign, utm_source, utm_media, utm_keyword, and utm_content of UTM parameters, respectively.

3. User Labels🏷️

The following are default user attribute data labels that Ptengine can identify.

While partially the same as visit attributes, when setting conditions with user labels, time specification is not required.

Additionally, if you are collecting custom user attribute data using user identification tags, those attributes are included above.

How to Set Filter Conditions💅

With Ptengine, you can set diverse filter conditions when creating user segments. This allows you to accurately extract users with specific behaviors or attributes and apply them to targeted marketing and analysis.

Basic Configuration Structure

Filter conditions are set with the following structure:

"Filter" >> "Data" >> "Matching Rule" >> "Period"

Matching Rule Details

Matching rules include the following options (specific content is not provided, but generally includes the following):

Equals (=)

Not Equals (≠)

Greater Than or Equal To (≥)

Less Than or Equal To (≤)

Contains

Does Not Contain

Starts With

Ends With

Practical Filter Configuration Examples

🛒 High-Frequency Purchasers Filter: User behavior on website

Data: Purchase event

Matching Rule: 3 or more times

Period: Past 30 days

📱 Mobile Users Filter: Visit attributes

Data: Device

Matching Rule: Equals

Value: Mobile

🆕 New Registered Users Filter: User labels

Data: first seen

Matching Rule: Since

Value: [30 days before current date]

🌟 VIP Users Filter: User labels (Custom)

Data: Member rank

Matching Rule: Equals

Value: Gold

DANGER

Important Notes

Data Accuracy: Before setting filter conditions, confirm that related events or user labels are properly configured and collected

Combination Effects: By combining multiple filters, you can create more refined segments

Regular Review: Periodically review and update filter conditions according to changes in user behavior and market trends

Importance of Testing: Before applying new filter conditions, perform small-scale testing to confirm the intended results are achieved