Problem
Collecting the numbers was consuming the time needed to interpret them.
Across a growing multi-client portfolio, daily spend collection had become repetitive operational work. At scale, manual collection took about one hour per day. The harder problem was connecting those numbers to a plan and deciding what needed to change.
A useful system needed to answer more than “What did we spend?” It needed to connect the original budget, the observed pace, the likely month-end result, and the action required today.
- Daily manual collection at scale
- ~1 hour
- Brands/accounts at peak
- 12–15
Constraints
One operating view. Multiple clients, platforms, and changing rates of spend.
- Planning had to reflect each client’s budget and strategy across channels and platforms.
- Actual spend needed a consistent daily structure, even as ingestion methods evolved.
- Projections had to be understandable enough to use in recurring operational meetings.
- The system needed to support both forward planning and an ongoing feedback/control loop.
Analysis
The reporting task was part of a larger control loop.
A collection task
- Gather spend figures
- Enter data manually
- Interpret the numbers in isolation
An operating system
- Standardize observation
- Compare plan, actual, and projection
- Translate a variance into a daily target
The important shift was to connect the stages. A plan establishes intent. Actual spend provides evidence. A projection estimates where the current pace leads. Comparing those views makes exceptions visible and gives the team a basis for corrective action.
System Model
Plan → observe → project → diagnose → act.
- 01Plan
- 02Observe
- 03Project
- 04Diagnose
- 05Act
Forward planning + continuous feedback
- 01Planning engine
Client budget + strategy → channel/platform plan
- 02Observation engine
Actual spend data → standardized daily tracker
- 03Projection
Spend to date + recent spend rate × remaining days
- 04Diagnostic layer
Compare plan vs actual vs projected outcome
- 05Action layer
Calculate corrective daily spend requirements
Solution
Connect data collection to a decision, and the decision to an action.
The Google Sheets system combined monthly budget planning, actual spend tracking, pacing, projections, and corrective recommendations. Data collection evolved from manual entry toward automated ingestion using sources and tools such as Triple Whale, Supermetrics, Dataslayer, Google Analytics, and Google Apps Script.
Projected spend
actual spend to date + yesterday’s spend × days remaining
Daily increase needed
budget remaining / days left
Total daily spend needed
current/yesterday spend + daily increase needed
Reading the formulas: an incremental increase must use the projected budget gap. Total unspent budget ÷ days left instead gives the total daily target; adding the current rate to that would overstate the target. Both calculations require days left greater than zero.
- 01Manual reporting
- 02Automated ingestion
- 03Standardized reporting
- 04Budget planning
- 05Pacing
- 06Projections
- 07Exception detection
- 08Prescribed daily targets
Validation
The system became part of the operating rhythm.
The system was used company-wide and in recurring meetings for roughly one to two years. Its planning, observation, and correction layers were used together in real operational discussions across the portfolio. The evidence is sustained operational adoption: a shared structure for interpreting the numbers and deciding on corrections.
Outcome
A shared structure for seeing the situation and deciding what to do next.
What began as repetitive reporting developed into a decision-support system: a consistent place to plan, observe, project, diagnose, and prescribe corrective daily targets. The work connected automation to operational judgment, rather than treating data collection as the end goal.