# User Segment Advanced Configuration Guide To effectively use user segments, proper condition settings are essential. [How to Create User Segments](/en/experience/user-group/segment-advanced.md) 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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