Automated client reporting
A marketing-agency business where this lands in Operations.
02 — What is actually going wrong
The first week of every month disappears into building the same reports. Account managers copy numbers between platforms, paste them into a deck, and write a commentary that says roughly what last month's said. It is entirely non-billable and clients skim most of it.
How it works
Data pulls from the ad platforms and analytics on a schedule, populates a house template, and drafts the commentary: what moved, by how much, and what is unusual against the account's own history. The account manager rewrites the "so what" and sends it.
What good looks like
Reports land on the same day every month without anyone blocking out a week. Account managers spend their reclaimed time on the strategic conversation rather than the assembly, and the anomaly flags start catching account problems before the client does.
03 — The problem, in money
- Active client accounts25 client_count251 client_countPlaceholder — not a validated benchmarkStarting point
- Hours per report2 hours10.25 hoursPlaceholder — not a validated benchmarkStarting point
- Reports per client per month1 reports4 reportsPlaceholder — not a validated benchmarkStarting point
- Blended blended_hourly_cost cost60 USD135 USDPlaceholder — not a validated benchmarkStarting point
- Share of freed time redeployed0.5 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
04 — The options
05 — The call
Build it. A custom system is the right move here — the payback justifies owning it rather than renting a compromise.
06 — How this goes wrong
- Generating commentary that describes the chart. Clients can see the chart. If the narrative does not carry an interpretation and a recommendation, you have automated the worthless half.
- Reporting on metrics you chose because they were easy to pull. Automation makes vanity metrics cheaper to produce, not more useful.
- Freed account-manager hours quietly becoming slack instead of billable work. The model discounts this on purpose.
- Platform API changes silently breaking a pull, so the report ships with last month's numbers. Add a freshness check.
07 — How you would actually do it
- 01Standardise the report template first. You cannot automate six bespoke formats — that is the actual project.
- 02Inventory which platforms have usable APIs and which will need manual entry.
- 03Build the data pipeline and validate a month of output against hand-built reports.
- 04Add the narrative layer, with anomaly detection against each account's own baseline.
- 05Keep a mandatory human edit on the recommendation section.
- 06Decide explicitly what the reclaimed hours are for, and track that.
08 — Tools worth looking at
Named for orientation, not endorsement. No affiliate arrangements, and nothing here has been paid for.
09 — Run it on your numbers
Start from industry averages, then drag the inputs to match your business. The number moves live.Drag each input to your number — the starting points are neutral midpoints, not industry data. The number moves live.
10 — The evidence · what others report
Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.
11 — Questions owners ask
Is this your business?
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