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Fictional data only

query_loyalty_segments.sql

ValidPostgreSQL

1-- segment: high-intent loyalty members, last 30 days
2WITH active_members AS (
3  SELECT m.member_id, m.email_hash, m.tier
4  FROM loyalty_members m
5  WHERE m.status = 'active'
6    AND m.last_seen_at >= NOW() - INTERVAL '30 days'
7)
8SELECT a.tier,
9       COUNT(DISTINCT a.member_id) AS members,
10       AVG(p.order_value)          AS avg_order_value
11FROM active_members a
12LEFT JOIN purchase_history p
13       ON p.member_id = a.member_id
14GROUP BY a.tier
15ORDER BY members DESC;

Health 94 ~120ms est. 2 joins

Auto-saved

Built for analytics & marketing-ops teams likeAcme AnalyticsNimbus LabsOrbit MktgQuokka DataAtlas OpsPixel Loyalty

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Designed around how marketing operations teams actually write, review, and reuse queries.

Lightning editor

Syntax highlighting, autocomplete, tabs, and inline error hints.

Smart validator

Catch missing joins, ambiguous aliases, unsafe SELECT *, and more.

Performance score

Estimate cost, join weight, and complexity before you hit run.

CRM Playbooks

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paste-your-query.sql

-- Lapsed loyalty members eligible for win-back
SELECT m.member_id, m.tier, MAX(p.ordered_at) AS last_order
FROM loyalty_members m
LEFT JOIN purchase_history p ON p.member_id = m.member_id
WHERE m.status = 'active'
GROUP BY m.member_id, m.tier
HAVING MAX(p.ordered_at) < NOW() - INTERVAL '120 days';

Explanation

AI confidence · 95%

Aggregates from loyalty_members joined with 1 other table, filtered by your WHERE clause.

Aggregates rows from loyalty_members grouped by a dimension.

Health

100

Complexity

Joins

Duplicate risk

Optimization

Query safety

100

Aggregation

100

Risk analysis

No issues detected.

Optimization tips

Add an index on the time column used in the interval filter.
Ensure foreign key columns used in joins are indexed.
Pre-aggregate in a CTE for large fact tables.

Validator

Catch query bugs before they ship to production.

QueryPilot inspects every query for the issues that quietly break CRM segments and skew campaign reports.

Missing JOIN conditions and Cartesian products
Duplicate aliases and ambiguous columns
Unsafe SELECT * on wide CRM tables
Aggregations with invalid GROUP BY
Missing WHERE filters on event tables
Likely-duplicated row outputs

Validation report · campaign_lift.sql

High

Missing JOIN condition

campaign_events ↔ customer_master has no ON clause — likely Cartesian product.

Medium

Unsafe SELECT *

customer_master has 84 columns. Project only what you need.

Low

Implicit type cast

member_id compared with text literal '0042' — wrap with CAST.

Info

Tip: add index

Consider an index on email_tracking(opened_at).

Health

Complexity

Join weight

Sequential scans2

Index hits6

Estimated rows1.2M

Est. runtime~ 120 ms

Performance analyzer

Know what your query will cost — before it runs.

Visual scoring across complexity, joins, and estimated load. Get actionable optimization suggestions tailored to CRM-shaped data.

Query complexity meter

Join cost visualizer

Index suggestions

Heuristics & tips

Query examples

Patterns made for marketing ops.

Re-engage dormant subscribers

SELECT s.subscriber_id, s.email_hash, MAX(e.sent_at) AS last_email
FROM email_tracking e
JOIN customer_master s ON s.subscriber_id = e.subscriber_id
WHERE e.opened_at IS NULL
GROUP BY s.subscriber_id, s.email_hash
HAVING MAX(e.sent_at) < NOW() - INTERVAL '90 days';

Abandoned session funnel

SELECT step_name, COUNT(*) AS sessions
FROM abandoned_sessions
WHERE started_at >= NOW() - INTERVAL '14 days'
GROUP BY step_name
ORDER BY sessions DESC
LIMIT 10;

Campaign engagement lift

SELECT c.campaign_code,
       SUM(CASE WHEN e.event_type = 'click' THEN 1 ELSE 0 END) AS clicks,
       SUM(CASE WHEN e.event_type = 'open'  THEN 1 ELSE 0 END) AS opens
FROM campaign_events e
JOIN customer_master c ON c.subscriber_id = e.subscriber_id
GROUP BY c.campaign_code;

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Anika D.Lifecycle Manager, Pixel Loyalty

Illustrative quotes. Names, companies and data shown across the site are fictional.

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