This project for SQL involved the following steps:

Step 1: A SQL query was composed to retrieve data from the facebook_ads_basic_daily table, including fields such as ad_date, campaign_id, cost, impressions, clicks, and conversions.

Step 2: Data grouping was applied on fields ad_date and campaign_id to aggregate information by the date of ad display and the unique campaign identifier.

Step 3: Metrics like CPC, CPM, CTR, and ROMI were calculated using aggregated values of expenses and conversions. Conditional operators were utilized to handle scenarios where the denominator equals zero to avoid division by zero errors. The resulting query selects data, groups it by date and campaign ID, and then computes the specified metrics.

This SQL code was written to analyze data on advertising campaigns from the public.facebook_ads_basic_daily table. It performs the following actions:

Selects fields ad_date (date of ad display) and campaign_id (campaign identifier).

Sums the values of fields spend (expenses), impressions (number of displays), clicks (number of clicks), and value (total conversion Value) for each unique combination of date and campaign.

Metrics Calculated:

- CPC (Cost Per Click)

- CPM (Cost Per Mille)

- CTR (Click-Through Rate)

- ROMI (Return on Marketing Investment)

Data filtering is applied to exclude cases where the number of clicks, displays, and expenses equals zero. Insights from this code:

- It analyzes the effectiveness of Facebook advertising campaigns across various metrics.

- Data filtering excludes incomplete or incorrect data.

- Calculated metrics (CPC, CPM, CTR, and ROMI) can be utilized to evaluate advertising campaign effectiveness and make decisions regarding marketing strategies.