Automation

Automated Marketing Reports & Dashboards

Nobody should be exporting CSVs on the first Monday of the month. I build the pipeline that pulls Google Ads, Meta, GA4, Search Console and CRM data into one place on a schedule, joins it so spend and revenue sit in the same row, and delivers a report that is already written when you open it.

  • Looker Studio, Sheets, BigQuery
  • Spend joined to CRM outcomes, not just clicks
  • Setup quoted after scoping

Who this is for

In-house marketers who rebuild the same slide deck every month, agencies producing client reports by hand, and owners who currently learn how the month went two weeks after it ended. It is one of the builds under marketing and business automation services. Agencies with several clients should also read agency operations automation, where reporting is one part of a larger system.

A report is only worth automating when someone acts on it. If the current deck is produced because a report is expected rather than because a decision depends on it, the honest recommendation is to cut it down to five numbers before automating anything.

What gets automated

  • Data collection. Google Ads, Meta Ads, GA4, Search Console, Google Business Profile, email platform, CRM, call tracking and your own sales sheet, pulled on a schedule with the history stored so it survives platform retention limits.
  • Joining, which is the actual work. Ad spend from the platforms matched to leads in the CRM and to closed revenue, using consistent campaign naming and a persistent identifier. Most reporting projects fail here rather than at the chart stage.
  • Currency and cost normalisation, so campaigns billed in USD sit next to revenue in NPR without someone applying last month’s rate by hand.
  • Dashboards in Looker Studio or your preferred tool, with one page per audience: an operating view for whoever manages the channels, and a short view for whoever pays.
  • Scheduled delivery. The report emailed or sent to Slack or WhatsApp on a fixed day, with a written summary paragraph naming what changed and what caused it, drafted from the numbers and reviewed before it goes out.
  • Alerting between reports. Spend pacing, cost per lead moving outside a band, a conversion tag that stopped firing, a campaign that ran out of budget on a Saturday.
  • Data quality checks, so a report never quietly shows zero because an API token expired.

What I deliberately do not do

  • Report only on the flattering numbers. Impressions and reach go in the appendix; cost per qualified lead and revenue go at the top.
  • Automate a report nobody reads. Where that is the situation, the first deliverable is a shorter report, not a scheduled version of the long one.
  • Blend channels into a single attributed number and present it as truth. Attribution is reported with its assumptions visible, and platform-reported conversions are shown next to CRM-recorded outcomes rather than instead of them.
  • Move client data into tools nobody agreed to. Storage location and access are decided before anything is connected.
  • Silently change historical figures. Where a definition changes, the report shows when and why.

How a build runs

  1. Agree the decisions the report supports. Five to eight numbers and, for each, what you would do if it moved. Anything that survives this question goes in; the rest goes to the appendix.
  2. Fix naming and tracking first. Consistent campaign naming, working conversion tracking and a lead source field that is always populated. Reporting built on broken tracking produces confident, wrong pictures, and the fixes belong under analytics and tracking.
  3. Choose the storage. Sheets for modest volumes, a warehouse when history, joins or several accounts make sheets slow. I start with the cheaper option and say clearly when it will stop being enough.
  4. Build the pipeline and reconcile every figure against the platform’s own interface until they match or the difference is explained in writing.
  5. Design the report for its audience, then run it in parallel with the manual version for one cycle so people trust it before the manual one stops.
  6. Hand over with documentation of every metric definition, the refresh schedule and what to do when a connection fails.

What you receive

  • The pipeline and dashboards in your own accounts, with the data in your own storage.
  • A metric dictionary defining every number, its source and its calculation, which is what stops two people arguing about which figure is right.
  • Scheduled delivery to the people who need it, in the format they actually open.
  • Freshness and failure alerts, so a stale report announces itself.
  • Documentation, a handover session and optional monthly maintenance for API changes and new channels.

Pricing pointer

Setup is quoted on request after scoping; a single-channel dashboard with scheduled delivery sits at the lower end of that range. A multi-account, multi-channel stack joining ad spend to CRM revenue with a warehouse sits at the top, and fixing tracking first is often a separate piece of work. Monthly maintenance is quoted on request; warehouse and connector costs are billed to your own accounts. Where the reporting is really an SEO monitoring need, see SEO automation.

Next step

Send your current monthly report and read-only access to one ad account. I will come back within four business hours with the five numbers I think belong at the top, whether your tracking supports them and a fixed price.

Frequently asked questions

Why not just use Looker Studio's built-in connectors?

For a single account with modest history, they are often enough and I will tell you so. They struggle once you need joined data across platforms and your CRM, historical data beyond a platform's retention window, currency normalisation, or several accounts in one view. The pipeline exists to solve those; the dashboard on top can still be Looker Studio.

Do we need BigQuery or a data warehouse?

Only when Sheets stops coping, which happens with large row counts, many accounts or complex joins. Sheets is free, familiar and adequate for a lot of businesses. I start there and say clearly at what point the cost of a warehouse becomes worth it rather than selling one on day one.

Will the report match what Google Ads and Meta show?

Where it can, and where it cannot, the difference is explained in writing rather than smoothed over. Platform conversion counts, GA4 and your CRM will never agree exactly, because they count different things over different windows. The report shows platform-reported and CRM-recorded figures side by side so the gap is visible instead of hidden.

Can it include our sales data?

Yes, and it should. Joining ad spend to CRM leads and closed revenue is the point of the exercise, because cost per lead without close rates leads to cutting the campaign that produces the best customers. This needs consistent lead source capture, which is part of the build if it does not exist yet.

How long does it take?

One to two weeks for a single-channel dashboard on working tracking. Three to six weeks for a joined multi-channel stack, and longer if campaign naming and conversion tracking need fixing first. The reconciliation step, where every figure is matched to the platform interface, always takes longer than people expect and is not worth rushing.

What happens when a connection breaks?

The report tells you, rather than showing a zero. Freshness checks compare the last successful pull against the schedule and alert on failure, and expired tokens are the most common cause. Under maintenance I reconnect and re-run the backfill; the historical data is not lost because it is stored in your own account.

Ready to talk about your project?

A free 30-minute call, a straight answer about what would move the numbers, and a written proposal within 48 hours if we are a fit.