How to build Expansion Signals in BigQuery using Segment data

---

The Architecture: Headless Product Intelligence

To implement Expansion Signals effectively, you need to move beyond simple event tracking, interactive funnel analysis and siloed dashboards. By leveraging your existing BigQuery infrastructure, you can create a single source of truth for your GTM and Product teams.

---

The "Logic Gap": Why Manual Implementation is Hard

1. Siloed Billing Data: Expansion signals often require joining product usage data with billing tier limits (e.g., '80% of seat limit'). This join is hard to maintain in ad-hoc SQL.
2. Missed Timing: The best time to upsell is the moment a limit is hit. Batch-based SQL reports often surface this opportunity days too late.
3. False Positives: Without sophisticated filtering, you'll spam sales reps with low-quality leads, causing them to ignore your alerts.

---

Implementation: The BigQuery-Native Code

If you were to build this manually, your dbt model or SQL query in BigQuery might look like this:

/*
   Generic example for Expansion Signals
   Tailored for BigQuery architecture
*/

WITH raw_events AS (
    SELECT
        account_id,
        event_name,
        timestamp
    FROM `bigquery.raw_data.events`
    WHERE event_name IN ('seat_added', 'storage_limit_reached', 'api_usage_spike')
),

calculated_metrics AS (
    -- BigQuery-specific logic for Expansion Signals
    -- e.g., using specific window functions or time-travel
    SELECT
        account_id,
        COUNT(*) as signal_volume,
        DATE_TRUNC('day', timestamp) as metric_date
    FROM raw_events
    GROUP BY 1, 3
)

SELECT * FROM calculated_metrics;

---

The GrowthCues Advantage: Automate the Signal

While the code above provides a starting point, GrowthCues eliminates the need for manual SQL maintenance entirely. You can define Expansion Signals using our no-code Milestone Editor, and GrowthCues will automatically:

• Calculate the signal at the account level using your Segment data.
• Detect anomalies and behavioral shifts using robust statistical methods.
• Push actionable signals back into your BigQuery, where you can activate them directly into the tools your team uses (Slack, HubSpot, Salesforce).

GrowthCues acts as the Headless Intelligence Layer for your BigQuery. It connects directly to your Segment tables and automatically:

• Calculates Expansion Signals at the account level.
• Detects anomalies and behavioral shifts in real-time.
• Pushes actionable signals back into your warehouse, where you can activate them directly into the tools your team uses (Slack, HubSpot, Salesforce).

Deep Dive: Spotting Expansion

Don't leave money on the table. Check out Identify Expansion Opportunities to learn the 4 key signals to watch.

Ready to turn your raw BigQuery data into revenue?

Join the Technical Preview