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Adding Segments to Gain Deeper Customer Insights

Segment your data to uncover the 'who' behind the 'what'

Updated this week

Customer segments transform how you analyze data by revealing patterns across different customer groups. Instead of seeing general trends, you'll understand which specific customer types are experiencing issues or showing opportunities.

Why Add Segments?

Customer segments let you:

  • Identify which customer types experience specific issues

  • Discover high-value segments with unique needs

  • Target your retention efforts where they matter most

  • Uncover behavior patterns by customer segment

These segments power multiple features in the platform:

  • Highlights Report: See key changes and trends for specific customer segments

  • Insight Summary: Break down insights by segment to understand which customer groups are affected

  • Custom Analysis: Create targeted views filtered to the segments that matter most

Adding segment columns enhances all these features simultaneously, giving you a comprehensive view of how different customer segments experience your product or service.

How to Add Segments

Adding segments is simple - you just need to include the right columns in your Snowflake SQL query:

  1. Identify valuable segment dimensions for your business (like customer type, subscription plan, region)

  2. Include these columns in your SQL query when setting up your AI agent

  3. Select which segments to use from the "Segment Filters" section

Here is an example SQL query based on our demo agent:

SELECT 
id,
text,
creation_date,
-- Segment columns below
PLAN_TYPE,
COMPANY_SIZE,
INDUSTRY,
MEETING_FREQUENCY,
SUBSCRIPTION_LENGTH
FROM LANGAI_APP.APP_DATA.CANCELLATION_REASONS_DATASET_DEMO

Recommended Segment Dimensions

The most insightful segment columns typically include:

Segment Type

Examples

Customer attributes

customer_type, plan_type, industry, company_size

Behavioral data

usage_frequency, meeting_frequency, feature_adoption

Financial metrics

lifetime_value, annual_revenue, subscription_length

Geographic data

region, country, market

What If My Table Structure Is Different?

If your Snowflake table uses different column names, you can easily map them using SQL aliases:

SELECT 
ticket_number as id, -- map to required id field
customer_feedback as text, -- map to required text field
ticket_date as creation_date, -- map to required date field
-- Segment columns
customer_tier as PLAN_TYPE,
org_size as COMPANY_SIZE,
sector as INDUSTRY
FROM CUSTOMER_SERVICE.TICKETS

Next Steps

After adding segment columns to your query:

  1. The AI agent will automatically detect available segments

  2. You'll see these dimensions in the "Segment Filters" section

  3. Select which segments to analyze or compare

  4. Your insights will now show patterns across different customer groups

With properly configured segments, you'll be able to:

  • Filter the Highlights Report to focus on specific customer groups

  • See segment-specific trends in the Insight Summary

  • Identify which customer types are experiencing particular issues or opportunities

  • Compare performance and feedback across different segments

By adding segments, you'll transform basic insights into targeted actions that drive retention and growth for specific customer groups.

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