For Divert's data analytics services, should we emphasize developing more advanced predictive models or focus on creating simpler, more user-friendly dashboards for clients?

Introduction

The trade-off we're examining today is whether Divert's data analytics services should prioritize developing more advanced predictive models or focus on creating simpler, more user-friendly dashboards for clients. This decision is crucial for Divert's product strategy and will significantly impact our client relationships and competitive positioning. I'll analyze this trade-off by exploring the context, evaluating potential impacts, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming Divert is a B2B company providing data analytics services. Could you confirm if this is correct, and if so, what industries are our primary clients in?

Why it matters: Helps tailor our solution to specific client needs Expected answer: Confirmation of B2B model, focus on retail or finance industries Impact on approach: Would influence the complexity and type of analytics needed

• Business Context: Based on our current revenue model, I'm thinking advanced models might command higher prices. How does our pricing structure currently differentiate between basic and advanced analytics offerings?

Why it matters: Informs potential revenue impact of each option Expected answer: Tiered pricing based on complexity and features Impact on approach: Would help quantify the financial trade-offs of each option

• User Impact: Considering our user base, I'm guessing we have a mix of technical and non-technical users. What's the current split between data scientists and business users among our clients?

Why it matters: Helps balance complexity with usability Expected answer: 30% technical, 70% business users Impact on approach: Would influence the emphasis on user-friendly interfaces vs. advanced capabilities

• Technical: Given our current infrastructure, I'm wondering about the scalability of more advanced models. What's our current computational capacity for handling complex predictive analytics?

Why it matters: Determines feasibility of advanced model development Expected answer: Moderate capacity with room for expansion Impact on approach: Would inform the technical constraints and investment needed for advanced models

• Timeline: Considering market dynamics, I'm thinking time-to-market might be crucial. How urgent is this decision in terms of competitive pressure or client demands?

Why it matters: Helps prioritize speed vs. perfection in our approach Expected answer: Moderate urgency, aiming for implementation within 6 months Impact on approach: Would influence the depth of analysis and experimentation timeline