The month-end scramble ends
Account managers stop spending the first week of every month building reports.
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Agency reporting automation
Client reports that build themselves.
We pipe Amazon, Walmart, Google, Meta and TikTok data into one warehouse, define every metric once, and generate white-label dashboards and scheduled reports for each client.
What you get
Less assembly, more account management.
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.
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.
Step 1
Template the report your best account manager already sends; that becomes the spec
Step 2
Pipe every platform into one warehouse instead of point-to-point dashboard connectors
Step 3
Automate the top-N client reports first; long-tail clients inherit the same templates
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
Results
Teams we've built this for.
120+ automations live
Tinuiti
We rebuilt the ETL, warehouse and automation layer across Amazon, Walmart, TikTok, Instacart and Criteo.
Read the case study
50+ campaigns, one dashboard
BTR Media
We built ETL pipelines into one data model and one dashboard for Amazon, Walmart and Instacart campaigns.
Read the case study
One pipeline, every client account
SellerFuse
We built the SP-API and Ads API pipelines, live dashboards and automation behind SellerFuse.
Read the case study
25% less reporting time
Mau Brands
We built Mau Brands' Amazon analytics platform: sales, inventory, ads and pricing in one view.
Read the case studyFAQ
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.
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.