Should QI Tech prioritize expanding the features of its credit scoring API or focus on improving the accuracy of its existing risk assessment models?

Introduction

The trade-off question at hand is whether QI Tech should prioritize expanding the features of its credit scoring API or focus on improving the accuracy of its existing risk assessment models. This scenario involves balancing product expansion with core functionality enhancement. I'll analyze this trade-off by examining the business context, user impact, technical considerations, and potential outcomes.

Step 1

Clarifying Questions (3 minutes)

• Business Context: I'm thinking our revenue model might heavily influence this decision. Could you share how our pricing structure is tied to API features versus model accuracy?

Why it matters: Helps determine if expansion or improvement aligns better with revenue goals. Expected answer: Revenue is primarily driven by API usage, with a premium for higher accuracy. Impact on approach: Would lean towards API expansion if it drives more usage and revenue.

• User Impact: Based on our client base, I'm assuming we serve both large financial institutions and smaller fintech startups. Can you confirm our primary user segments and their specific needs?

Why it matters: Different user segments may prioritize features or accuracy differently. Expected answer: Mix of large banks (accuracy-focused) and fintech startups (feature-hungry). Impact on approach: Would need to balance the needs of both segments in the solution.

• Technical Feasibility: Considering our current architecture, I'm curious about the complexity of expanding API features versus improving model accuracy. What's our engineering team's assessment of the technical challenges for each option?

Why it matters: Technical constraints could limit our ability to execute on either option. Expected answer: API expansion is straightforward, model improvement requires significant ML expertise. Impact on approach: Would factor in our current team capabilities and potential need for new hires.

• Resource Allocation: Given our current team structure, I'm wondering about our capacity to pursue both options simultaneously. What's our current resource allocation between API development and data science teams?

Why it matters: Helps understand if we need to make a clear choice or can pursue a hybrid approach. Expected answer: Resources are currently split 60/40 between API and model teams. Impact on approach: Would influence whether we recommend a focused or balanced strategy.

• Market Dynamics: Considering the competitive landscape, I'm curious about how our offering compares to key competitors in terms of features and accuracy. Can you provide insights on where we stand in the market?

Why it matters: Helps identify whether features or accuracy are more critical for maintaining competitiveness. Expected answer: We lead in features but lag slightly in accuracy compared to top competitors. Impact on approach: Would influence whether we focus on maintaining our feature advantage or closing the accuracy gap.