Diagnose Decline in First Day Funding Rate

Q: Diagnose Decline in First Day Funding Rate

This question evaluates a data scientist's diagnostic analytics skills, including metric decomposition, funnel and cohort analysis, attribution, data quality assessment, and evaluation of experiment and market impacts.

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Question

Diagnostic Case: First-Day Funding Rate Drop

Context

You are on a product analytics team monitoring onboarding performance. The team observes a decline in the First-Day Funding Rate.

Assume this metric is defined as:

First-Day Funding Rate (FDFR) = (Unique new users who complete their first funding within 24 hours of signup) / (Unique new users who signed up on that day).
Time window is rolling 24 hours from each user's signup timestamp (not calendar day), using consistent timezone and event sources.

Question

The percentage of new customers who fund their account on day-1 has fallen.

How would you systematically diagnose this decline end-to-end?
Which internal and external factors would you examine?
If those drivers are ruled out, what other angles would you explore to isolate the cause?

Consider approaches like breaking the metric into numerator/denominator components, segmenting by cohort and traffic source, inspecting funnel steps, seasonality, UI/feature changes, experiments, and external market shifts.

Diagnose Decline in First Day Funding Rate

Diagnostic Case: First-Day Funding Rate Drop

Context

Question

Solution

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