Facebook Ads is Meta’s advertising platform that allows businesses to create and manage digital advertising campaigns across Facebook, Instagram, and other Meta-owned platforms. It provides tools for targeting audiences, tracking performance, and optimizing ad spend for maximum return on investment.

Configuring Facebook Ads as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Facebook Ads option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need to authorize Nekt to access your Facebook Ads data. Click on the Facebook Authorization button and log in with your Facebook account. Grant the necessary permissions for the ad accounts you want to extract data from. After authentication, select the specific Ad Account for this source and define a start date for data retrieval. Facebook’s API allows fetching reports up to 36 months in the past. You can also enable advanced reports for gender, country, age and device platform segmentations, but be aware that this will increase extraction times. Additionally, you can configure attribution windows and lookback periods in the Advanced Settings. Once you’re done, click Next.

2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select a name for each table (which will contain the fetched data) and the type of sync.

Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix and make this process faster!
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
- Full table: every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can determine when to execute a full sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while. Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Facebook Ads and their corresponding fields:

Campaigns

Ad Sets

Ads

Creatives

Ad Insights

Data Model

The following diagram illustrates the relationships between the core data streams in Facebook Ads. The arrows indicate the join keys that link the different entities, providing a clear overview of the data structure.

Use Cases for Data Analysis

This guide outlines valuable business intelligence use cases when consolidating Facebook Ads data, along with ready-to-use SQL queries that you can run on Explorer.

Campaign Performance Analysis

1. Campaign Performance Metrics

Track the overall performance of your campaigns, including detailed conversion metrics. Business Value:

Identify which campaigns deliver the best value for money
Understand the relationship between quality scores and performance
Optimize budget allocation based on performance metrics

SQL code

WITH
	campaign_metrics AS (
		SELECT
			c."name" AS "campaign_name",
			c."objective",
			c."status",
			c."buying_type",
			SUM(TRY_CAST (ai."impressions" AS BIGINT)) AS "total_impressions",
			SUM(TRY_CAST (ai."reach" AS BIGINT)) AS "total_reach",
			SUM(TRY_CAST (ai."clicks" AS BIGINT)) AS "total_clicks",
			SUM(TRY_CAST (ai."spend" AS DOUBLE)) AS "total_spend",
			AVG(TRY_CAST (ai."frequency" AS DOUBLE)) AS "avg_frequency",
			SUM(TRY_CAST (ai."clicks" AS DOUBLE)) * 100.0 / NULLIF(SUM(TRY_CAST (ai."impressions" AS DOUBLE)), 0) AS "ctr",
			SUM(TRY_CAST (ai."spend" AS DOUBLE)) / NULLIF(SUM(TRY_CAST (ai."clicks" AS DOUBLE)), 0) AS "cpc",
			SUM(TRY_CAST (ai."spend" AS DOUBLE)) * 1000.0 / NULLIF(SUM(TRY_CAST (ai."impressions" AS DOUBLE)), 0) AS "cpm"
		FROM
			"nekt_raw"."facebook_ads_campaigns" c
			LEFT JOIN "nekt_raw"."facebook_ads_adsinsights" ai ON c."id" = ai."campaign_id"
		WHERE
			c."effective_status" = 'ACTIVE'
			AND date_parse (ai."date_start", '%Y-%m-%d') >= CURRENT_DATE - INTERVAL '30' DAY
		GROUP BY
			c."name",
			c."objective",
			c."status",
			c."buying_type"
	)
SELECT
	"campaign_name",
	"objective",
	"total_spend",
	"total_impressions",
	"total_reach",
	"total_clicks",
	"avg_frequency",
	"ctr",
	"cpc",
	"cpm"
FROM
	campaign_metrics

2. Creative Performance and Content Analysis

Analyze how different creative elements and content types perform across your campaigns, helping optimize your creative strategy. Business Value:

Identify which creative formats drive the best engagement
Understand the impact of different call-to-action types
Optimize creative elements based on performance data
Guide future creative development with data-driven insights

SQL code

WITH
	creative_metrics AS (
		SELECT
			cr.name AS creative_name,
			cr.title,
			cr.body,
			cr.call_to_action_type,
			cr.object_type,
			COALESCE(cr.video_id, 'image') AS content_type,
			SUM(TRY_CAST (ai.impressions AS BIGINT)) AS total_impressions,
			SUM(TRY_CAST (ai.reach AS BIGINT)) AS total_reach,
			SUM(TRY_CAST (ai.clicks AS BIGINT)) AS total_clicks,
			SUM(TRY_CAST (ai.spend AS DECIMAL(20, 2))) AS total_spend,
			AVG(TRY_CAST (ai.frequency AS DECIMAL(10, 2))) AS avg_frequency,
			CAST(
				SUM(TRY_CAST (ai.clicks AS DOUBLE)) * 100.0 / NULLIF(SUM(TRY_CAST (ai.impressions AS DOUBLE)), 0) AS DECIMAL(10, 2)
			) AS ctr,
			SUM(
				CASE
					WHEN action.action_type = 'video_view' THEN TRY_CAST (action.value AS DECIMAL(20, 2))
					ELSE 0
				END
			) AS total_video_views,
			AVG(
				CASE
					WHEN action.action_type = 'video_view' THEN TRY_CAST (action.value AS DECIMAL(20, 2))
					ELSE NULL
				END
			) AS avg_video_completion_rate
		FROM
			nekt_raw.facebook_ads_creatives cr
			LEFT JOIN nekt_raw.facebook_ads_ads a ON cr.id = a.creative.id
			LEFT JOIN nekt_raw.facebook_ads_adsinsights ai ON a.id = ai.ad_id
			CROSS JOIN UNNEST (ai.actions) AS t ("action")
		WHERE
			date_parse (ai.date_start, '%Y-%m-%d') >= CURRENT_DATE - INTERVAL '30' DAY
		GROUP BY
			cr.name,
			cr.title,
			cr.body,
			cr.call_to_action_type,
			cr.object_type,
			cr.video_id
	)
SELECT
	creative_name,
	content_type,
	call_to_action_type,
	object_type,
	total_impressions,
	total_reach,
	total_clicks,
	total_spend,
	ctr,
	CAST(
		total_spend / NULLIF(total_clicks, 0) AS DECIMAL(10, 2)
	) AS cost_per_click,
	CASE
		WHEN content_type != 'image' THEN total_video_views
		ELSE NULL
	END AS video_views,
	CASE
		WHEN content_type != 'image' THEN avg_video_completion_rate
		ELSE NULL
	END AS video_completion_rate
FROM
	creative_metrics
ORDER BY
	total_clicks DESC

3. Time-Based Performance Analysis

Track campaign performance trends over time to identify patterns and optimize campaign timing. Business Value:

Understand daily and weekly performance patterns
Identify best-performing days and times
Optimize campaign scheduling and budget pacing
Track performance trends for better planning

SQL code

WITH
	daily_metrics AS (
		SELECT
			date_parse (ai.date_start, '%Y-%m-%d') AS date,
			date_format (date_parse (ai.date_start, '%Y-%m-%d'), '%W') AS day_of_week,
			SUM(TRY_CAST (ai.impressions AS BIGINT)) AS total_impressions,
			SUM(TRY_CAST (ai.reach AS BIGINT)) AS total_reach,
			SUM(TRY_CAST (ai.clicks AS BIGINT)) AS total_clicks,
			SUM(TRY_CAST (ai.spend AS DECIMAL(20, 2))) AS total_spend,
			SUM(TRY_CAST (ai.clicks AS DOUBLE)) * 100.0 / NULLIF(SUM(TRY_CAST (ai.impressions AS DOUBLE)), 0) AS ctr,
			SUM(TRY_CAST (ai.spend AS DOUBLE)) / NULLIF(SUM(TRY_CAST (ai.clicks AS DOUBLE)), 0) AS cpc,
			COUNT(DISTINCT ai.campaign_id) AS active_campaigns,
			SUM(
				CASE
					WHEN action.action_type = 'purchase' THEN TRY_CAST (action.value AS DECIMAL(20, 2))
					ELSE 0
				END
			) AS total_conversions
		FROM
			nekt_raw.facebook_ads_adsinsights ai
			CROSS JOIN UNNEST (ai.actions) AS t (action)
		WHERE
			date_parse (ai.date_start, '%Y-%m-%d') >= CURRENT_DATE - INTERVAL '30' DAY
		GROUP BY
			ai.date_start
	),
	weekly_summary AS (
		SELECT
			day_of_week,
			AVG(total_impressions) AS avg_daily_impressions,
			AVG(total_reach) AS avg_daily_reach,
			AVG(total_clicks) AS avg_daily_clicks,
			AVG(total_spend) AS avg_daily_spend,
			AVG(ctr) AS avg_ctr,
			AVG(cpc) AS avg_cpc,
			AVG(total_conversions) AS avg_daily_conversions,
			AVG(active_campaigns) AS avg_active_campaigns
		FROM
			daily_metrics
		GROUP BY
			day_of_week
	),
	daily_vs_average AS (
		SELECT
			dm.*,
			ws.avg_daily_spend AS typical_daily_spend,
			ws.avg_daily_clicks AS typical_daily_clicks,
			ws.avg_daily_conversions AS typical_daily_conversions,
			ROUND(
				(total_spend - ws.avg_daily_spend) * 100.0 / NULLIF(ws.avg_daily_spend, 0),
				2
			) AS spend_vs_typical,
			ROUND(
				(total_clicks - ws.avg_daily_clicks) * 100.0 / NULLIF(ws.avg_daily_clicks, 0),
				2
			) AS clicks_vs_typical,
			ROUND(
				(total_conversions - ws.avg_daily_conversions) * 100.0 / NULLIF(ws.avg_daily_conversions, 0),
				2
			) AS conversions_vs_typical
		FROM
			daily_metrics dm
			JOIN weekly_summary ws ON dm.day_of_week = ws.day_of_week
	)
SELECT
	date,
	day_of_week,
	total_impressions,
	total_reach,
	total_clicks,
	CAST(total_spend AS DECIMAL(20, 2)) AS total_spend,
	CAST(ctr AS DECIMAL(10, 2)) AS ctr,
	CAST(cpc AS DECIMAL(10, 2)) AS cpc,
	total_conversions,
	active_campaigns,
	CAST(typical_daily_spend AS DECIMAL(20, 2)) AS typical_daily_spend,
	CAST(spend_vs_typical AS DECIMAL(10, 2)) AS spend_vs_typical_pct,
	CAST(clicks_vs_typical AS DECIMAL(10, 2)) AS clicks_vs_typical_pct,
	CAST(conversions_vs_typical AS DECIMAL(10, 2)) AS conversions_vs_typical_pct
FROM
	daily_vs_average
ORDER BY
	date DESC

This analysis provides valuable insights into:

Daily performance trends
Day-of-week patterns
Performance against typical metrics
Campaign activity levels
Spend and conversion efficiency

You can use these insights to:

Adjust campaign scheduling
Optimize budget allocation across days
Identify and investigate performance anomalies
Plan campaign launches around high-performing periods
Improve budget pacing strategies

Get started

Using Nekt

Understand

Use cases

FAQ

Facebook Ads as a data source

Configuring Facebook Ads as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

Campaign Performance Analysis

1. Campaign Performance Metrics

2. Creative Performance and Content Analysis

3. Time-Based Performance Analysis

Get started

Using Nekt

Understand

Use cases

FAQ

​Configuring Facebook Ads as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Data Model

​Use Cases for Data Analysis

​Campaign Performance Analysis

​1. Campaign Performance Metrics

​2. Creative Performance and Content Analysis

​3. Time-Based Performance Analysis

Configuring Facebook Ads as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

Campaign Performance Analysis

1. Campaign Performance Metrics

2. Creative Performance and Content Analysis

3. Time-Based Performance Analysis