What metrics would you use to evaluate Riskified's Policy Protect feature?

Introduction

Evaluating Riskified's Policy Protect feature requires a comprehensive approach to product success metrics. To address this challenge effectively, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Riskified's Policy Protect is a feature designed to help e-commerce businesses enforce their policies and reduce policy abuse. It uses machine learning algorithms to analyze customer behavior and identify potential policy violations in real-time.

Key stakeholders include:

• E-commerce merchants: Seeking to reduce losses from policy abuse
• Customers: Wanting a smooth shopping experience
• Riskified: Aiming to expand its product offerings and increase revenue

User flow:

1. Customer places an order
2. Policy Protect analyzes the order in real-time
3. The system approves, flags, or declines the order based on policy rules
4. Merchant reviews flagged orders and takes appropriate action

Policy Protect fits into Riskified's broader strategy of providing comprehensive fraud prevention and chargeback protection solutions for e-commerce businesses. It complements their existing products by addressing a specific pain point in policy enforcement.

Compared to competitors like Signifyd or Forter, Policy Protect differentiates itself by focusing specifically on policy abuse rather than general fraud detection.

Product Lifecycle Stage: Policy Protect is likely in the growth stage, as it's a relatively new feature in the evolving e-commerce risk management landscape.

Software-specific context:

• Platform: Cloud-based SaaS solution
• Integration points: E-commerce platforms, payment gateways, and order management systems
• Deployment model: API-based integration with merchant systems