For Convoy (Road)'s load matching algorithm, should we optimize for maximizing carrier earnings or minimizing empty miles to improve sustainability?

Introduction

The trade-off we're examining today is whether Convoy's load matching algorithm should optimize for maximizing carrier earnings or minimizing empty miles to improve sustainability. This decision sits at the heart of Convoy's value proposition and has significant implications for both our business model and our environmental impact. I'll analyze this trade-off by considering the product context, stakeholder impacts, metrics, and potential experiments to inform our decision-making process.

Step 1

Clarifying Questions (3 minutes)

• Business Context: I'm thinking this trade-off might be driven by competing strategic priorities. Could you share more about our current business goals and how they relate to carrier satisfaction versus sustainability initiatives?

Why it matters: Helps prioritize the trade-off against broader business objectives Expected answer: Both are important, but carrier satisfaction is critical for growth Impact on approach: Would influence the weighting of metrics in our decision framework

• User Impact: Based on our user segments, I'm assuming this affects both small independent carriers and larger fleets. Can you confirm if that's correct, and if there are any specific carrier segments we're particularly focused on?

Why it matters: Different carrier segments may have varying priorities and sensitivities to earnings vs. sustainability Expected answer: Focus on independent owner-operators and small fleets Impact on approach: Would tailor our experiment design and metrics to these key segments

• Technical Feasibility: I'm thinking our current algorithm might have some constraints. Can you give me an overview of our technical capabilities for implementing complex multi-objective optimizations?

Why it matters: Determines the feasibility of potential solutions that balance both objectives Expected answer: We have some flexibility but major overhauls would require significant development time Impact on approach: Would influence the complexity of solutions we consider in the near term

• Resource Constraints: Given the potential impact of this decision, I'm curious about our available resources. What's our current capacity for running experiments and analyzing results in this area?

Why it matters: Affects the scope and timeline of our experimentation and implementation Expected answer: Limited data science resources but high priority for the product team Impact on approach: Would shape the complexity and duration of our proposed experiments

• Timeline Pressure: I'm wondering if there are any external factors driving urgency on this decision. Are there any upcoming regulatory changes or competitive moves we need to consider in our timeline?

Why it matters: Influences the speed and risk tolerance of our decision-making process Expected answer: Increasing pressure from sustainability-focused shippers, but no immediate regulatory changes Impact on approach: Would balance thorough analysis with the need for timely action