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Agency reporting automation

Automated client reporting that builds itself.

We pipe Amazon, Walmart, Google, Meta and TikTok data into one warehouse, define every metric once, and generate white-label client dashboards and scheduled reports for every account, in Looker Studio, Power BI or the BI tool you already use.

What you get

Less assembly, more account management.

The month-end scramble ends

Account managers stop spending the first week of every month building reports.

Numbers that hold up

Every client report draws on the same tested definitions, so errors don't multiply with the client count.

New clients, no new busywork

Adding a client means adding configuration, not another afternoon of exports.

No connector ceiling

You own the raw data, so a custom metric is a dbt model away, not a feature request.

The problem

Manual reporting scales worse than linearly

Every new client adds exports, VLOOKUPs, and a deck. At 20 clients it's annoying; at 100 it consumes whole roles, and the error rate climbs with the volume. A wrong number in a client report costs trust that took years to build. Reporting tools help until you need a metric they don't support; then you're back in spreadsheets.

Software or pipeline

Client reporting software vs a custom pipeline.

Client reporting software works well when every client looks alike and every metric is on the vendor's list. Agencies outgrow it when they need Amazon and retail media depth, margin-adjusted ROAS or a client-specific view the tool doesn't support, or when per-client pricing climbs with the roster. A custom pipeline puts the raw data in a warehouse you own, so automated client reporting keeps working as clients, platforms and metrics change, and white label reporting is a styling choice rather than a plan upgrade.

Before and after

What changes.

Manual reporting

  • Exports, VLOOKUPs and a deck for every client
  • The first week of each month goes to assembly
  • Error rates climb with the client count
  • Back in spreadsheets when a tool lacks a metric

Automated reporting

  • The first week of the month goes back to client work
  • Error rates stay flat as the client count grows
  • Reports show yesterday's data, not last month's
  • Custom metrics without a return to spreadsheets

What we automate

What’s included.

  • Automated data pulls from Amazon, Walmart, Meta, Google, TikTok, and other retail media networks
  • A reporting mart where every client metric is defined once, in dbt, with tests
  • White-labeled live dashboards per client: your brand, their data, always current
  • Scheduled report delivery: PDFs and summaries generated and sent automatically
  • Anomaly flags so account managers hear about a spend spike from the system, not the client
  • Historical restatement handling so last month's numbers stay honest after attribution updates

How we work

How we run it.

  1. Step 1

    Template the report your best account manager already sends; that becomes the spec

  2. Step 2

    Pipe every platform into one warehouse instead of point-to-point dashboard connectors

  3. Step 3

    Automate the top-N client reports first; long-tail clients inherit the same templates

  4. Step 4

    Keep a human review step exactly where judgment is added, and nowhere else

Typical stack

What it runs on.

  • Amazon Ads API
  • Meta / Google / TikTok APIs
  • dbt
  • Snowflake / BigQuery
  • Looker / Power BI
  • Custom report generation

FAQ

Common questions.

What is agency reporting automation?

Agency reporting automation replaces manual client-report assembly with a data pipeline: ad-platform data flows automatically into a warehouse, metrics are computed once with consistent definitions, and client-facing dashboards and scheduled reports are generated from that single source, so reports are always current and never hand-built.

How is this different from tools like AgencyAnalytics or Swydo?

Off-the-shelf reporting tools are excellent until you need a metric, platform, or client-specific view they don't support. A warehouse-based pipeline owns the raw data, so custom metrics (margin-adjusted ROAS, inventory-aware pacing, cross-network dedup) are a dbt model away, and you're never locked into a vendor's connector list or per-client pricing.

How much time does automated reporting actually save?

Mau Brands cut manual reporting time by 25% after we automated it. How many hours come back depends on client count and report depth. The larger win is usually fewer errors and faster numbers: clients see yesterday's numbers today, instead of last month's numbers next week.

Can automated reports still be customized per client?

Yes. The right architecture is templates over a shared metric layer. Every client gets the same tested metric definitions, while layout, KPIs shown, branding, and commentary sections vary per client. Customization lives in configuration, not in a hand-edited spreadsheet.

Can the client reports be white label?

Yes. White label reporting is the default for agencies: your logo and colors on every client dashboard and scheduled PDF, and each client logs in to see only their own accounts.

Does this cover PPC reporting for agencies across Amazon, Google and Meta?

Yes. PPC reporting for agencies is the most common starting point: Amazon Sponsored Ads, Walmart Connect, Google Ads, Meta and TikTok spend, clicks and attributed sales side by side for each client, with a ROAS dashboard for every client built on the same metric definitions.

Which BI tools do the client dashboards run in?

Looker Studio, Power BI, Looker or Tableau, whichever your team already uses. Every tool reads the same tested tables in your warehouse, so a client sees the same number in the dashboard and in the scheduled report.

Kill the reporting week

Show us one client report you build by hand and we'll map the pipeline that builds it automatically for every client.

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