Why has the average order value for Klarna's one-time card feature decreased by 20% this quarter?

Introduction

The recent 20% decrease in average order value for Klarna's one-time card feature is a concerning trend that requires immediate attention. To address this issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term and long-term implications for the product and business.

Step 1

Clarifying Questions (3 minutes)

• Considering the timing, I'm wondering if there have been any recent changes to the one-time card feature. Have we implemented any updates or modifications to the feature in the last quarter?

Why it matters: Recent changes could directly impact user behavior and order values. Expected answer: Yes, we made some UI changes to simplify the card creation process. Impact on approach: If changes were made, we'd focus on analyzing their specific impact on user behavior.

• Looking at user segments, I'm curious about the distribution of this decrease. Is the 20% drop consistent across all user segments, or are we seeing more significant decreases in specific groups?

Why it matters: Identifying affected segments can help pinpoint the root cause. Expected answer: The decrease is more pronounced among new users. Impact on approach: We'd focus on onboarding and first-time user experience if new users are disproportionately affected.

• Considering external factors, have there been any significant changes in the competitive landscape or market conditions that might influence consumer spending habits?

Why it matters: External factors could be driving the decrease in average order value. Expected answer: No major market shifts, but a new competitor has entered with aggressive promotions. Impact on approach: We'd need to assess our value proposition and potentially adjust our marketing strategy.

• Regarding data integrity, can we confirm that the definition of "average order value" has remained consistent and that our measurement systems are functioning correctly?

Why it matters: Ensures we're addressing a real issue and not a data anomaly. Expected answer: The definition and measurement systems have remained consistent. Impact on approach: If there are discrepancies, we'd need to address data issues before proceeding with further analysis.