Extract insights from a multi-entry funnel scorecard

Quick Overview

This question evaluates a candidate's ability to analyze multistep funnel metrics, handle highly skewed traffic across many access points, and prioritize insights from sparse or long-tail data, testing competencies in product analytics and measurement.

Extract insights from a multi-entry funnel scorecard

Company: Intuit

Role: Product Analyst

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: Onsite

## Scenario You are given a scorecard for a QuickBooks-like product showing a funnel by **access point** (15+ entry points, highly skewed traffic). For each `access_point`, you have: - `visits` - `application_start` - `application_complete` - `paid` (or `subscribe`) - Potentially other step-level counts/rates (7–8 columns total) Assume the funnel is: **Visit → Application Start → Application Complete → Paid**. ## Task 1. Share **three insights** you would report from this scorecard (prioritize by impact, not just rate extremes). 2. Recommend **next steps** (product, experimentation, or data work) based on those insights. ## Notes - Many access points have tiny traffic; explain how you handle the long tail (e.g., 80/20).

Quick Answer: This question evaluates a candidate's ability to analyze multistep funnel metrics, handle highly skewed traffic across many access points, and prioritize insights from sparse or long-tail data, testing competencies in product analytics and measurement.

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Oct 21, 2025, 12:00 AM
easyProduct AnalystOnsiteAnalytics & Experimentation
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Scenario

You are given a scorecard for a QuickBooks-like product showing a funnel by access point (15+ entry points, highly skewed traffic).

For each access_point, you have:

  • visits
  • application_start
  • application_complete
  • paid (or subscribe )
  • Potentially other step-level counts/rates (7–8 columns total)

Assume the funnel is: Visit → Application Start → Application Complete → Paid.

Task

  1. Share three insights you would report from this scorecard (prioritize by impact, not just rate extremes).
  2. Recommend next steps (product, experimentation, or data work) based on those insights.

Notes

  • Many access points have tiny traffic; explain how you handle the long tail (e.g., 80/20).
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