✔ PASS
Temporal & Time-Series
task_025_Q25_multi_touch_marketing_attribution
Scaled: 1M+ Records
Ordered: Yes
Multi-Touch Marketing Attribution Comparison in a Single Pass
Candidate Latency (1M Rows)
147.35 ms
(1.1x faster than golden)
Golden Latency (1M Rows)
157.73 ms
DuckDB in-memory engine
Underlying Data Volume
2,000,000 rows
57.2 MiB across 2 tables
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 13.3s
✔ Correctness Verified on 1M+ Records
Candidate SQL output matches ground truth golden query across all multiset rows and numeric tolerances (tol=1e-3). Zero data skew or anti-patterns detected on full dataset scale.
1. Analytical Significance & What It Means
Scenario & Frontier TrapBusiness Scenario & Problem
CMOs need to compare 3 attribution models (First-Touch, Last-Touch, and Uniform Linear) for touchpoints occurring within 30 days prior to conversion.
⚡ The Frontier Trap (Why LLMs Fail)
Running three separate join queries triples warehouse compute costs and risks data inconsistency.
Prompt / User Request Given to Model
CMOs need to compare 3 attribution models (First-Touch, Last-Touch, and Uniform Linear) for touchpoints occurring within 30 days prior to conversion. Execute the required analytical SQL calculation in DuckDB. Expected output columns: channel, first_touch_revenue, last_touch_revenue, linear_touch_revenue. Results should be ordered as specified.
2. Underlying Tables, Row Counts & Data Volume
2 Tables (2,000,000 rows • 57.2 MiB)Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:
📊 ad_clicks
1,000,000 rows
30.5 MiB Data Volume
user_id (INTEGER) channel (VARCHAR) click_time (TIMESTAMP)Data Preview (showing sample rows from 1,000,000 total rows):
| # | user_id | channel | click_time |
|---|---|---|---|
| 1 | 10 | Google Search | 2026-03-01 09:00:00 |
| 2 | 10 | Facebook Ad | 2026-03-05 14:00:00 |
| 3 | 10 | Email Promo | 2026-03-10 18:00:00 |
| 4 | 10 | Google Search | 2026-03-01 09:30:00.000019 |
| 5 | 11 | Facebook Ad | 2026-03-01 09:30:00.000032 |
| 6 | 12 | Email Promo | 2026-03-01 09:30:00.000045 |
| 7 | 13 | Google Search | 2026-03-01 09:30:00.000061 |
| 8 | 14 | Facebook Ad | 2026-03-01 09:30:00.000082 |
| 9 | 15 | Email Promo | 2026-03-01 09:30:00.000102 |
| 10 | 16 | Google Search | 2026-03-01 09:30:00.000136 |
| 11 | 17 | Facebook Ad | 2026-03-01 09:30:00.000140 |
| 12 | 18 | Email Promo | 2026-03-01 09:30:00.000168 |
| 13 | 19 | Google Search | 2026-03-01 09:30:00.000199 |
| 14 | 20 | Facebook Ad | 2026-03-01 09:30:00.000201 |
| 15 | 21 | Email Promo | 2026-03-01 09:30:00.000221 |
📊 conversions
1,000,000 rows
26.7 MiB Data Volume
user_id (INTEGER) conversion_time (TIMESTAMP) revenue (DECIMAL(10,2))Data Preview (showing sample rows from 1,000,000 total rows):
| # | user_id | conversion_time | revenue |
|---|---|---|---|
| 1 | 10 | 2026-03-11 10:00:00 | 300 |
| 2 | 10 | 2026-03-01 09:30:00.000008 | 257.26 |
| 3 | 11 | 2026-03-01 09:30:00.000022 | 287.73 |
| 4 | 12 | 2026-03-01 09:30:00.000048 | 346.02 |
| 5 | 13 | 2026-03-01 09:30:00.000064 | 322.77 |
| 6 | 14 | 2026-03-01 09:30:00.000083 | 317.42 |
| 7 | 15 | 2026-03-01 09:30:00.000101 | 291.89 |
| 8 | 16 | 2026-03-01 09:30:00.000127 | 341.56 |
| 9 | 17 | 2026-03-01 09:30:00.000144 | 277.69 |
| 10 | 18 | 2026-03-01 09:30:00.000167 | 338.36 |
| 11 | 19 | 2026-03-01 09:30:00.000190 | 245.79 |
| 12 | 20 | 2026-03-01 09:30:00.000201 | 294.34 |
| 13 | 21 | 2026-03-01 09:30:00.000226 | 335.87 |
| 14 | 22 | 2026-03-01 09:30:00.000242 | 309.43 |
| 15 | 23 | 2026-03-01 09:30:00.000268 | 254.07 |
3. Graphical Query Execution Plan & Cost Bottlenecks
Physical Operator Tree (DuckDB 2.0 Profiler)Visual flowchart of the physical execution pipeline. Operators with high relative costs (≥ 20% of query runtime or heavy latency) are visually highlighted:
0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['__internal_decomp...
0.0%
Latency: 0.13 ms
Rows In/Out: 3
Data Vol: 168 B
0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['__internal_compre...
0.0%
Latency: 0.01 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['channel', 'first_...
0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['__internal_decomp...
2.6%
Latency: 12.21 ms
Rows In/Out: 3
Data Vol: 168 B
2.0%
Latency: 9.24 ms
Rows In/Out: 534,007
Data Vol: 20.37 MB
📁 Projections: ['channel', 'CASE ...
1.5%
Latency: 7.12 ms
Rows In/Out: 534,007
Data Vol: 24.44 MB
📁 Projections: ['#0', '__internal_...
0.0%
Latency: 0.14 ms
Rows In/Out: 534,007
Data Vol: 24.44 MB
📁 Projections: ['revenue', 'channe...
0.1%
Latency: 0.24 ms
Rows In/Out: 534,007
Data Vol: 30.56 MB
📁 Projections: ['#0', '#1', '#2', ...
🔥 28.9% Cost
Latency: 136.81 ms
Rows In/Out: 534,007
Data Vol: 30.56 MB
📁 Projections: row_number() OVER (...
🔥 35.4% Cost
Latency: 167.39 ms
Rows In/Out: 534,007
Data Vol: 26.48 MB
📁 Projections: ['row_number() OVER...
🔥 25.8% Cost
Latency: 121.82 ms
Rows In/Out: 534,007
Data Vol: 18.34 MB
3.4%
Latency: 15.98 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 memory.main.ad_clicks
0.4%
Latency: 1.84 ms
Rows In/Out: 999,998
Data Vol: 19.07 MB
📁 memory.main.conversions
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| WINDOW | 35.4% | 167.39 ms | 534,007 | 26.48 MB | Projections: ['row_number() OVER (PARTITION BY u | 🔥 BOTTLENECK |
| WINDOW | 28.9% | 136.81 ms | 534,007 | 30.56 MB | Projections: row_number() OVER (PARTITION BY use | 🔥 BOTTLENECK |
| HASH_JOIN | 25.8% | 121.82 ms | 534,007 | 18.34 MB | - | 🔥 BOTTLENECK |
| TABLE_SCAN | 3.4% | 15.98 ms | 1,000,000 | 26.70 MB | memory.main.ad_clicks | Normal |
| HASH_GROUP_BY | 2.6% | 12.21 ms | 3 | 0.00 MB | - | Normal |
| PROJECTION | 2.0% | 9.24 ms | 534,007 | 20.37 MB | Projections: ['channel', 'CASE WHEN ((touch_asc | Normal |
| PROJECTION | 1.5% | 7.12 ms | 534,007 | 24.44 MB | Projections: ['#0', '__internal_compress_string_ | Normal |
| TABLE_SCAN | 0.4% | 1.84 ms | 999,998 | 19.07 MB | memory.main.conversions | Normal |
| PROJECTION | 0.1% | 0.24 ms | 534,007 | 30.56 MB | Projections: ['#0', '#1', '#2', '#3', '#4', '#6' | Normal |
| PROJECTION | 0.0% | 0.14 ms | 534,007 | 24.44 MB | Projections: ['revenue', 'channel', 'touch_asc', | Normal |
| ORDER_BY | 0.0% | 0.13 ms | 3 | 0.00 MB | - | Normal |
| PROJECTION | 0.0% | 0.01 ms | 3 | 0.00 MB | Projections: ['channel', 'first_touch_revenue', | Normal |
| PROJECTION | 0.0% | 0.00 ms | 3 | 0.00 MB | Projections: ['__internal_decompress_string(#0)' | Normal |
| PROJECTION | 0.0% | 0.00 ms | 3 | 0.00 MB | Projections: ['__internal_compress_string_uhugei | Normal |
| PROJECTION | 0.0% | 0.00 ms | 3 | 0.00 MB | Projections: ['__internal_decompress_string(#0)' | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedWITH eligible_touches AS
(SELECT c.user_id,
c.revenue,
a.channel,
a.click_time,
row_number() OVER (PARTITION BY c.user_id
ORDER BY a.click_time ASC) AS touch_asc,
row_number() OVER (PARTITION BY c.user_id
ORDER BY a.click_time DESC) AS touch_desc,
count(*) OVER (PARTITION BY c.user_id) AS total_touches
FROM conversions c
JOIN ad_clicks a ON c.user_id = a.user_id
AND a.click_time >= c.conversion_time - INTERVAL '30 days'
AND a.click_time <= c.conversion_time)
SELECT channel,
sum(CASE
WHEN touch_asc = 1 THEN revenue
ELSE 0
END) AS first_touch_revenue,
sum(CASE
WHEN touch_desc = 1 THEN revenue
ELSE 0
END) AS last_touch_revenue,
round(sum(revenue / total_touches), 2) AS linear_touch_revenue
FROM eligible_touches
GROUP BY channel
ORDER BY channel;
-- Multi-touch attribution comparison (First-touch, Last-touch, Linear)
WITH eligible_touches AS
(SELECT c.user_id,
c.revenue,
a.channel,
a.click_time,
row_number() OVER (PARTITION BY c.user_id
ORDER BY a.click_time ASC) AS touch_asc,
row_number() OVER (PARTITION BY c.user_id
ORDER BY a.click_time DESC) AS touch_desc,
count(*) OVER (PARTITION BY c.user_id) AS total_touches
FROM conversions c
JOIN ad_clicks a ON c.user_id = a.user_id
AND a.click_time >= c.conversion_time - INTERVAL '30 days'
AND a.click_time <= c.conversion_time)
SELECT channel, -- First Touch: 100% credit if first
sum(CASE
WHEN touch_asc = 1 THEN revenue
ELSE 0
END) AS first_touch_revenue, -- Last Touch: 100% credit if last
sum(CASE
WHEN touch_desc = 1 THEN revenue
ELSE 0
END) AS last_touch_revenue, -- Linear: Equal credit across all touchpoints
round(sum(revenue / total_touches), 2) AS linear_touch_revenue
FROM eligible_touches
GROUP BY channel
ORDER BY channel;
CREATE TABLE ad_clicks (user_id INT, channel VARCHAR, click_time TIMESTAMP);
CREATE TABLE conversions (user_id INT, conversion_time TIMESTAMP, revenue decimal(10, 2));
INSERT INTO ad_clicks
VALUES (10, 'Google Search', '2026-03-01 09:00:00'),
(10, 'Facebook Ad', '2026-03-05 14:00:00'),
(10, 'Email Promo', '2026-03-10 18:00:00');
INSERT INTO conversions
VALUES (10, '2026-03-11 10:00:00', 300.00);
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(3 rows × 4 cols)
| # | channel | first_touch_revenue | last_touch_revenue | linear_touch_revenue |
|---|---|---|---|---|
| 1 | Email Promo | 53,554,668.37 | 53,554,968.37 | 53,554,728.37 |
| 2 | Facebook Ad | 53,304,917.15 | 53,304,917.15 | 53,304,977.15 |
| 3 | Google Search | 53,379,643.27 | 53,379,386.01 | 53,379,557.46 |
Expected Golden Output
(3 rows × 4 cols)
| # | channel | first_touch_revenue | last_touch_revenue | linear_touch_revenue |
|---|---|---|---|---|
| 1 | Email Promo | 53,554,668.37 | 53,554,968.37 | 53,554,728.37 |
| 2 | Facebook Ad | 53,304,917.15 | 53,304,917.15 | 53,304,977.15 |
| 3 | Google Search | 53,379,643.27 | 53,379,386.01 | 53,379,557.46 |