Write a query to find conversion rate from clicks

Question

Write a SQL query to calculate conversion rate from the clicks table, considering nulls and duplicates.

Codemia · Accepted Answer

The goal is to calculate the conversion rate from a 'clicks' table. The conversion rate is typically defined as the number of conversions (e.g., purchases) divided by the number of clicks. In this case, we need to account for possible NULL values in the data, which can occur in fields like 'conversion_id', and also handle duplicates that may skew our results. The 'clicks' table should contain at least two relevant columns: 'click_id' and 'conversion_id'. We will need to ensure we accurately count unique clicks and conversions to derive the conversion rate.

1. **Identify Relevant Data**: Start by determining the structure of the 'clicks' table. Look for 'click_id' and 'conversion_id'.  
2. **Filter Out NULLs**: Use the COALESCE function to handle NULL values in the 'conversion_id' column, treating them as non-conversions.  
3. **Count Unique Clicks and Conversions**: Use a Common Table Expression (CTE) to aggregate the counts of unique clicks and unique conversions.  
4. **Calculate Conversion Rate**: Finally, compute the conversion rate as the ratio of unique conversions to unique clicks.  
5. **Optimize for Performance**: Consider indexing on the 'click_id' and 'conversion_id' for faster lookups.

```sql
WITH UniqueCounts AS (
    SELECT
        COUNT(DISTINCT click_id) AS total_clicks,
        COUNT(DISTINCT conversion_id) AS total_conversions
    FROM clicks
    WHERE conversion_id IS NOT NULL
)
SELECT
    total_clicks,
    total_conversions,
    CASE
        WHEN total_clicks = 0 THEN 0
        ELSE total_conversions * 1.0 / total_clicks
    END AS conversion_rate
FROM UniqueCounts;
```

To optimize this query for large datasets, consider the following strategies:  
1. **Indexing**: Create indexes on 'click_id' and 'conversion_id' to speed up the counting process.  
2. **Partitioning**: If the dataset is particularly large, consider partitioning the 'clicks' table by date or another relevant dimension to improve query performance.  
3. **Materialized Views**: For frequent queries, use materialized views to pre-compute the counts of clicks and conversions, which can significantly reduce compute time.

It's crucial to handle various edge cases:  
1. **NULL Values**: Ensure that NULL values in 'conversion_id' are filtered out to avoid miscounting conversions.  
2. **Duplicates**: If a click can have multiple entries, using COUNT(DISTINCT click_id) will help ensure that each click is counted only once.  
3. **Zero Clicks**: Handle scenarios where there are no clicks (total_clicks = 0) to avoid division by zero errors by returning a conversion rate of 0 in that case.

Write a query to find conversion rate from clicks

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