Drowning in Product Data? Here's How We Fixed That with AI

WITH source AS (
    SELECT
        account_id,
        event_name,
        happened_at,
        unique_id
    FROM
        census_on_census.production.product_usage_summary
    WHERE
        happened_at >= CURRENT_DATE - INTERVAL '90 days'
)
SELECT
    account_id,
    ARRAY_AGG(OBJECT_CONSTRUCT(
        'event_name', event_name,
        'unique_id', unique_id,
        'happened_at', happened_at
    )) AS events
FROM
    source
GROUP BY
    account_id;

Summarize the product usage trends based on available event types, focusing on key activities and changes over the last 30 days compared to the previous 30 days. Highlight significant positive, exploratory, and negative actions with a focus on actionable insights for customer engagement and issue resolution.

Event Types:

• Negative Events contain: failed, deleted, paused
• Exploratory Events contain: clicked
• Positive Events contain: created, configured

Data Sources:

Guidelines:

1. Overview of Last 30 Days:
   • Briefly outline overall product usage, with a focus on significant positive, negative, and exploratory actions, compared to the previous 30 days.
2. Growth & Potential Issues:
   • Identify growth signals (e.g., increases in positive events like new destination creation) and potential concerns (e.g., a rise in negative events like sync deletion) without repeating details from other sections.
3. Feature Usage Breakdown:
   • Summarize engagement with key actions such as sync configuration, model creation, and exploratory clicks, highlighting notable trends.
4. Service Connection Type Concerns:
   • Focus on any issues by service type related to negative actions like failures or deletions.
5. Suggested Next Steps (most detailed):
   • Outline actionable next steps for Customer Success to address negative events, promote positive actions, and encourage further exploratory engagement.
6. Sales Talking Points (most detailed):
   • Provide talking points for addressing any negative usage trends, reinforcing the benefits of positive actions, and encouraging deeper product exploration. Use relevant case studies or success stories where applicable.

Example Output Structure:

Overview of Last 30 Days:

• Key Activity: Active model creation and new sync configurations, with some increases in negative events like sync deletion.

Growth Signals & Potential Issues:

• 🟢 Growth: 20% increase in new source creation, indicating stronger product adoption.
• 🟡 Concern: 10% rise in sync deletions, indicating potential disengagement.

Feature Usage Breakdown:

• 📄 Sync Configured: Increased configuration attempts.
• 📄 Model Creation: Consistent activity in new model creation.
• 📄 Negative Events: Sync deletions and pauses have increased.

Service Connection Type Concerns:

• ⚠️ Investigate causes for increased sync deletions and workspace deletions.

Suggested Next Steps:

• 🛠️ Address the increase in sync deletions by offering troubleshooting resources.
• 📧 Schedule a follow-up to discuss setup improvements and encourage continued feature adoption.

Talking Points:

• 📊 Emphasize the value of new sync configurations and model creation.
• 🔍 Address any negative trends with proactive support, using success stories as applicable.