✔ 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 Trap
Business 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:

Pipeline Latency Distribution (Total Plan Time: 472.93 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['__internal_decomp...
⏳ ORDER_BY 0.0%
Latency: 0.13 ms
Rows In/Out: 3
Data Vol: 168 B
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['__internal_compre...
📐 PROJECTION 0.0%
Latency: 0.01 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['channel', 'first_...
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 168 B
📁 Projections: ['__internal_decomp...
📊 HASH_GROUP_BY 2.6%
Latency: 12.21 ms
Rows In/Out: 3
Data Vol: 168 B
📐 PROJECTION 2.0%
Latency: 9.24 ms
Rows In/Out: 534,007
Data Vol: 20.37 MB
📁 Projections: ['channel', 'CASE ...
📐 PROJECTION 1.5%
Latency: 7.12 ms
Rows In/Out: 534,007
Data Vol: 24.44 MB
📁 Projections: ['#0', '__internal_...
📐 PROJECTION 0.0%
Latency: 0.14 ms
Rows In/Out: 534,007
Data Vol: 24.44 MB
📁 Projections: ['revenue', 'channe...
📐 PROJECTION 0.1%
Latency: 0.24 ms
Rows In/Out: 534,007
Data Vol: 30.56 MB
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 🔥 28.9% Cost
Latency: 136.81 ms
Rows In/Out: 534,007
Data Vol: 30.56 MB
📁 Projections: row_number() OVER (...
⚠️ Heavy Execution Bottleneck (28.9% of query runtime)
🪟 WINDOW 🔥 35.4% Cost
Latency: 167.39 ms
Rows In/Out: 534,007
Data Vol: 26.48 MB
📁 Projections: ['row_number() OVER...
⚠️ Heavy Execution Bottleneck (35.4% of query runtime)
⚡ HASH_JOIN 🔥 25.8% Cost
Latency: 121.82 ms
Rows In/Out: 534,007
Data Vol: 18.34 MB
⚠️ Heavy Execution Bottleneck (25.8% of query runtime)
🔍 TABLE_SCAN 3.4%
Latency: 15.98 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 memory.main.ad_clicks
🔍 TABLE_SCAN 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 Highlighted
Candidate SQL Executed by gemini-3.8-flash-low (Runtime on 1M Rows: 147.35 ms)
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,
       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;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 157.73 ms)
-- 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;
DuckDB Fixture Setup DDL & Data Population
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
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