Advanced Analytics in Snowflake: Modeling Customer Segments with dbt

This blog guides you through building a customer segmentation framework in dbt + Snowflake, using SQL to:

• Transform raw activity logs into behavioral patterns

• Define meaningful segments (lifecycle, tier, persona)

• Feed segments into BI dashboards or activation tools

• Maintain flexibility, auditability, and reusability — all in code

No machine learning or Python required. Just well-structured dbt models and good data design.

💡 Why Customer Segmentation?

Segmentation helps businesses answer:

• Who are my most valuable customers?

• Who’s likely to churn or drop off?

• Which channels or features are most used by whom?

• How do I personalize outreach by lifecycle stage?

Done well, segmentation powers better marketing, retention, support, and strategic insights.

🧭 Real-World Use Cases by Industry

🧱 Recommended Data Model

Structure your dbt project using the entity-activity pattern:

dim_customers:

• One row per user/account

• Demographics, acquisition source, join date

fct_interactions:

• Long-form behavioral log

• Events like: login, purchase, feature use, support ticket

• Fields: customer_id, event_type, channel, timestamp

dim_segments:

• Outputs segment tags or labels per customer

• Can be one model or layered (e.g., lifecycle + tier)

🛠️ Step-by-Step dbt Flow

1. Clean and stage interactions

2. Create customer-level aggregates

3. Define Lifecycle Segments

4. Assign Tiers Based on Engagement

📊 Visualizing & Activating Segments

Once dim_segments is ready, plug it into:

BI Tools (Power BI / Tableau):

• Visualize segment size and trends over time

• Breakdown revenue or support tickets by segment

• Track churn or conversion rates by lifecycle stage

Marketing & Ops:

• Export high-value or churn-risk segments to:

  • Salesforce or HubSpot (via reverse ETL)

  • Paid ad platforms (for lookalikes or suppression)

  • Internal tools or Streamlit apps

You can even use a lightweight Streamlit UI to manage thresholds dynamically.

🧠 Tips for Implementation

• Use is_incremental() logic in fct_interactions for performance

• Tag models with segment, analytics, customer_profile for discoverability

• Use dbt tests to ensure no duplicates or missing keys

• Document segment logic with descriptions or YAML comments

• Use dbt exposures to connect models to dashboards or metrics

💥 Why Use dbt for Segmentation?

• Fully SQL-based => Easy to review, audit, and collaborate on

• Modular + maintainable => Break up logic into steps

• CI/CD + version controlled => Transparent and testable

• Tool-agnostic => Outputs go anywhere — BI, activation, apps

• Business-readable logic => No black-box ML needed

🧩 Artefacts

• stg_events.sql, fct_interactions.sql, dim_segments.sql

• Macros for generate_lifecycle_segment() and generate_engagement_tier()

• Sample BI visuals:

  • Funnel by lifecycle

  • Segment heatmap

  • RFM matrix chart

• Segment management UI mock (Streamlit)

✅ Summary

With just Snowflake and dbt, you can deliver:

• Lifecycle and engagement insights

• Segments for marketing or ops

• Improved dashboards with clear personas

• Reusable logic, built with best practices

💡 No need for ML, tools like Segment, or a CDP.

Just smart SQL + dbt modeling = advanced analytics, simplified.