KPI Pulse Drilldown
Spot abnormal movement, then drill from totals into regions and channels
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 98%
The KPI Pulse Drilldown is a management diagnostic for detecting when an operating system moves off course. Define the main KPIs and the range considered healthy, then compare the newest result with the expectation and several historical views. Assia described looking at daily, weekly, monthly, quarterly, yearly, and same-day eight-week averages. If a headline measure such as registrations fell materially below expectation, he would drill into countries and then channels to find where the difference originated. The mechanism moves from anomaly detection to localisation: a total signals that something may be wrong, while dimensional breakdowns narrow the investigation. It does not prove a cause on its own. The output is a focused question about the region, channel, or other component that deserves deeper investigation.
Origin
Extracted from The Foundr Podcast
Core principles
- 01Monitor a small set of vital company measures
- 02Compare actual results with an expected range and relevant history
- 03Treat a material deviation as a prompt to investigate
- 04Drill from the total into useful dimensions
- 05Use reports to find questions rather than assume causes
How to run it
- 1
Define the pulse
Choose the few measures that reveal whether the operation is broadly healthy. Define their calculation, reporting frequency, and expected range.
Pro tip Use measures close enough to operations to change before lagging financial results do.
Watch out A dashboard with dozens of equal-priority metrics obscures the pulse.
- 2
Compare across time
View the latest result beside relevant daily, weekly, monthly, quarterly, and longer-term comparisons. Include a same-period average when seasonality makes it useful.
Pro tip Compare like with like, such as the same weekday across recent weeks.
Watch out A single prior day may create a misleading baseline.
- 3
Flag the deviation
Identify results far enough from expectation to justify investigation. Distinguish genuine anomalies from ordinary variation.
Pro tip Set investigation thresholds before seeing the latest result.
Watch out Reacting to every fluctuation creates noise and unnecessary intervention.
- 4
Drill by dimension
Break the headline result into regions, channels, products, or other components that can explain the total. Continue until the movement is localised enough to investigate.
Pro tip Start with dimensions that map to accountable operating owners.
Watch out A correlated slice is not automatically the root cause.
- 5
Investigate and respond
Confirm what changed in the affected component and determine whether action is required. Track whether the response restores the expected pattern.
Pro tip Pair quantitative drilldown with context from the people closest to the work.
Watch out Do not take corrective action solely from a dashboard without checking the underlying cause.
In the wild
Assia described expecting roughly 30,000 daily registrations and treating a fall to 15,000 as a reason to investigate. His proposed drilldown was to inspect country trends first and then acquisition channels if countries did not explain the difference, while comparing several time windows and an eight-week same-day average.
→ A broad registration anomaly becomes a narrower operating investigation.
A marketplace sees completed orders fall below its normal Monday range. The operator compares recent Mondays, then breaks the total down by city and traffic source. One city accounts for most of the decline, prompting a check of local supply and payment operations rather than a platform-wide promotion.
→ The team investigates the affected component instead of reacting to the aggregate blindly.
Common mistakes
Monitoring without an expectation
A number has little diagnostic value when nobody has defined its healthy range or relevant comparison.
Stopping at the company total
An aggregate can reveal a change but not which region, channel, or product is driving it.
Confusing a symptom with a cause
A drilldown localises the issue but still requires investigation before corrective action.
Is it for you?
Best for
It is best for organisations with frequent, reliable data across regions, channels, products, or other diagnostic dimensions.
Not ideal for
It is not ideal when reporting is delayed, definitions are unstable, or normal variation is mistaken for a meaningful problem.
From the transcript
“if for example i expect uh registrations to be 30 000 and suddenly they're not 30 000 they're 15 000 then i i know i…”
“i'll run down to the countries and i'll look at the trend of the countries”
“yesterday this week this month this quarter this year uh today and uh last eight week uh the same day eight week average”
From the episode
354: What Yoni Assia Of eToro Learned During Dinner with Warren Buffett
Yoni Assia