LinkedIn Ads Reports

LinkedIn Ads Reports is a Make.com integration app, not a standalone reporting platform — it exposes a single module, "Generate an Ad Statistics Report," that pulls ad performance data from the LinkedIn Marketing API. Any automation (scheduled delivery, recurring pulls, routing results to Slack or a spreadsheet) is built by wiring this module into a Make scenario and attaching Make's own scheduler, not by configuring a built-in dashboard scheduler inside the app itself.

Practical Details

Because the module returns a single aggregated report per run, a typical automated scenario calls it on a schedule (say, daily) with a fixed campaign or account identifier and date range, then routes the JSON output into a Google Sheet, database, or downstream module for formatting. There's no native trigger for real-time events — every run is a request-response call you schedule yourself.

Actionable Considerations

1. Attach a Make scheduler to the module rather than expecting built-in delivery cadence
2. Fix your pivot dimension (campaign, creative, account) and date range in the module config before scheduling
3. Route the module's JSON output to a sheet or database module for formatting, since the app itself has no dashboard

Scaling LinkedIn Ads Reports across more campaigns or accounts means adding more scheduled calls to the same module in Make, not upgrading a plan tier inside the app — since it's a single-action integration, there's no concept of "seats" or "teams" within the app itself. The practical ceiling is LinkedIn's own Marketing API rate limits and your Make plan's operation quota, since every report pull consumes one Make operation.

Practical Details

Running this module across many accounts or a fine-grained date range (for example, daily pulls per campaign rather than one weekly account-level pull) multiplies the number of API calls and Make operations consumed, so high-frequency, highly-segmented reporting can burn through a Make plan's operation quota faster than expected.

Adoption tips

1. Batch report requests at the account or campaign-group level before drilling into per-creative detail
2. Monitor your Make operation usage as you add more scheduled report pulls
3. Coordinate with whoever manages your LinkedIn Marketing API app to avoid hitting rate limits

Setting up LinkedIn Ads Reports starts outside Make entirely: you need a LinkedIn Marketing API developer app with Marketing Developer Platform access approved by LinkedIn, since the Ads Reporting endpoints aren't available on a bare self-serve API key. Once that's approved, you add the "Generate an Ad Statistics Report" module to a Make scenario and authorize it via LinkedIn's OAuth 2.0 flow (using Make's default app credentials or your own, with the redirect URI Make provides).

Implementation details

After authorizing, you configure the module with the account, campaign, or campaign-group identifier you want reported on, the date range, and the pivot dimension (such as by campaign or by creative); the module then returns the matching statistics as structured data you can pass to the next step in your scenario.

Implementation checklist

1. Confirm your LinkedIn developer app has approved Marketing Developer Platform access before building the scenario
2. Add the module to a Make scenario and complete the LinkedIn OAuth authorization
3. Set the campaign/account identifier, date range, and pivot dimension for the report you need
4. Run a manual test execution before attaching a schedule, to confirm the returned fields match what your downstream steps expect

Because LinkedIn Ads Reports queries LinkedIn's Marketing API directly for each run, the numbers you get back are only as current as LinkedIn's own ad-serving and attribution pipeline — third-party click and conversion data on LinkedIn can lag by up to a day or two before it's finalized, so a report pulled immediately after a campaign change may understate recent performance.

Practical Details

The module returns whatever metrics and pivot dimension you configure (for example, impressions and clicks by campaign, or spend by creative) rather than a fixed, opinionated metric set, so consistency across reports depends on your Make scenario always requesting the same fields and date-range logic — there's no built-in metric glossary or versioning inside the app itself.

Actionable steps

• Re-pull the previous day's report rather than trusting same-day numbers as final
• Standardize the fields and pivot dimension requested across every scenario that calls this module
• Cross-check totals against LinkedIn Campaign Manager's own dashboard if a number looks off

Access to LinkedIn Ads Reports is governed entirely by LinkedIn's own OAuth 2.0 authorization code flow — you connect the Make module to a LinkedIn account that has admin or reporting access on the relevant Ads account, and LinkedIn's own permission model (not a permission system inside this app) determines what campaigns and data that connection can see. There's no separate role-based access layer, activity log, or admin console built into the integration itself.

Implementation details

Because the module uses your LinkedIn credentials' existing ad-account permissions, tightening or loosening access means changing that person's role in LinkedIn Campaign Manager, not configuring anything inside Make. Getting programmatic access in the first place also requires your LinkedIn Marketing API app to be approved for Marketing Developer Platform access, which is a manual LinkedIn review step, not a self-serve API key signup.

Best practices

• Connect the module using a dedicated LinkedIn user with reporting-level (not full admin) access where possible
• Confirm your LinkedIn developer app has active Marketing Developer Platform approval before relying on scheduled pulls
• Revoke and reconnect the OAuth token if the connecting user's LinkedIn ad-account access changes

LinkedIn Ads Reports uses AI-assisted analytics to surface trends and anomaly detection, helping teams identify optimization opportunities. LinkedIn Ads Reports emphasizes clear, explainable recommendations rather than black-box automation.

Practical use

AI-driven recommendations in LinkedIn Ads Reports highlight underperforming assets and suggest adjustments. LinkedIn Ads Reports supports human-in-the-loop decision making to validate any suggested changes.

Considerations

• Review AI-generated insights within your governance framework
• Combine AI signals with contextual business knowledge
• Monitor model behavior and data freshness

LinkedIn Ads Reports isn't a stakeholder-facing dashboard tool — it returns raw statistical data from LinkedIn's Marketing API into a Make scenario, so "engagement" here means how reliably your automation delivers that data to the people who need it, not a UI stakeholders log into. Any review cadence, commentary, or shared dashboard has to be built downstream, typically by routing the module's output into a spreadsheet, BI tool, or Slack message.

Practical Details

Because each run returns one report scoped to the campaign, account, or date range you specify, teams that want a recurring stakeholder update usually chain this module with a formatting step (a Google Sheets or Data Studio connector module) and a delivery step (email or Slack) rather than expecting the app to present results itself.

Implementation tips

• Build the downstream formatting and delivery steps in the same Make scenario, not as an afterthought
• Keep the pivot dimension consistent so recurring reports are comparable week over week
• Treat the module's raw JSON as an input to your reporting layer, not the final deliverable

LinkedIn Ads Reports provides consolidated insights into campaign performance, helping teams make informed optimization decisions. LinkedIn Ads Reports enables clear visibility across metrics and audiences while maintaining security and governance.

Key benefits

With LinkedIn Ads Reports you can quickly assess de-duplicated audience reach, creative effectiveness, and budget pacing to drive better outcomes. This tool aligns with standard workflows and reduces manual reporting effort.

Getting started considerations

• Plan data access roles within LinkedIn Ads Reports to ensure responsible use
• Define a reporting cadence that fits your team’s decision cycle
• Leverage built-in templates to standardize dashboards

Because LinkedIn Ads Reports is a Make.com integration rather than a standalone vendor product, support is split across two places: Make's own support and community forum for issues with the module or scenario configuration, and LinkedIn's Marketing API documentation and developer support for anything related to authentication, rate limits, or the underlying report data itself.

Resolution approach

Most reported problems trace back to one of two things: an expired or insufficiently-scoped OAuth connection (fixed by reconnecting the LinkedIn account in the module), or a LinkedIn Marketing Developer Platform access issue (fixed on LinkedIn's developer portal, not inside Make). Data that looks wrong is usually a pivot-dimension or date-range mismatch in the module's configuration rather than a reporting bug.

Proactive measures

1. Re-authorize the LinkedIn OAuth connection if scheduled runs start failing
2. Check your LinkedIn developer app's Marketing Developer Platform status before assuming the module is broken
3. Verify the configured pivot dimension and date range whenever returned numbers look inconsistent