Learn Attribution Pitfalls and Best Practices | Attribution and Campaign Performance

Attribution analysis is a powerful tool for understanding how your marketing campaigns drive user actions, but it is not without its challenges. One major challenge is dealing with multi-device users. When a single user interacts with your brand on multiple devices—such as a phone, tablet, and computer—it becomes difficult to accurately attribute their conversions to the correct campaign, since their actions may be split across different user identifiers. Another challenge is missing data. Sometimes, campaign or event tracking fails, resulting in incomplete information that can skew attribution results. Finally, overlapping campaigns can cause confusion when users are exposed to multiple campaigns at the same time or in rapid succession, making it unclear which campaign should receive credit for a conversion.


              123456789101112131415
            
-- Handle NULL campaign_id and deduplicate events for attribution
SELECT
  user_id,
  event_type,
  event_timestamp,
  COALESCE(campaign_id, 0) AS campaign_id -- Assign 0 for missing campaign
FROM (
  SELECT *,
         ROW_NUMBER() OVER (
           PARTITION BY user_id, event_type, event_timestamp
           ORDER BY event_id
         ) AS rn
  FROM events
) deduped
WHERE rn = 1;

Definition

Deduplication means removing duplicate records to ensure accurate attribution.

To achieve reliable attribution analysis, you should follow a few best practices. First, focus on data quality by ensuring all tracking is implemented correctly and consistently. Second, create clear definitions for conversions, campaign touchpoints, and attribution windows so everyone in your organization interprets results the same way. Third, perform a regular review of your attribution logic and results to catch errors or changes in user behavior that may require updates to your approach.


              12345678
            
-- Filter out duplicate conversion events (e.g., multiple purchases logged for the same user and timestamp)
SELECT
  user_id,
  MIN(event_timestamp) AS first_purchase_time,
  campaign_id
FROM events
WHERE event_type = 'purchase'
GROUP BY user_id, campaign_id;

This query identifies unique purchase conversions by removing duplicate purchase events for the same user.

It filters the events table to include only rows where event_type is 'purchase', focusing the analysis specifically on conversion actions. Using grouped aggregation, the query groups records by user_id and campaign_id and applies the MIN aggregate function to event_timestamp to capture the earliest purchase time within each group.

By selecting the minimum timestamp per user and campaign, the query effectively deduplicates multiple purchase logs that may occur for the same conversion, such as repeated event tracking or retries. This ensures that each user–campaign pair is counted once, based on the first recorded conversion.

Methods used:

Filtering (WHERE) to isolate conversion events
Aggregation (MIN) to select the first occurrence of a conversion
Grouping (GROUP BY) to deduplicate events at the user–campaign level

This pattern is commonly used in conversion tracking, campaign attribution, and marketing funnel analysis to ensure accurate and non-inflated purchase counts.

Task

Swipe to start coding

Your goal is to analyze campaign performance by identifying how many unique users made a purchase attributed to each campaign. Use the events table to answer this question.

Follow these steps:

Filter the events table to include only purchase events.
For each user and campaign, consider only their first purchase event (use the earliest timestamp).
For each campaign, count the total number of unique users who made at least one purchase.
Return a result with columns: campaign_id and unique_purchasers (the user count).

Solution

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

single

Ask AI

Ask anything or try one of the suggested questions to begin our chat

Swipe to show menu


              123456789101112131415
            
-- Handle NULL campaign_id and deduplicate events for attribution
SELECT
  user_id,
  event_type,
  event_timestamp,
  COALESCE(campaign_id, 0) AS campaign_id -- Assign 0 for missing campaign
FROM (
  SELECT *,
         ROW_NUMBER() OVER (
           PARTITION BY user_id, event_type, event_timestamp
           ORDER BY event_id
         ) AS rn
  FROM events
) deduped
WHERE rn = 1;