Should Scalable Capital prioritize expanding its ETF selection to attract more investors or focus on improving its robo-advisor algorithm for better portfolio performance?

Introduction

The trade-off question at hand is whether Scalable Capital should prioritize expanding its ETF selection to attract more investors or focus on improving its robo-advisor algorithm for better portfolio performance. This scenario involves balancing user acquisition with product quality improvement. I'll analyze this trade-off by examining the product context, potential impacts, key metrics, and experimental approaches to inform a strategic recommendation.

Step 1

Clarifying Questions (3 minutes)

• Based on the current market conditions, I'm thinking investor sentiment might be leaning towards safer, more diverse options. Could you share any recent data on user preferences or market trends that might influence our ETF selection strategy?

Why it matters: Helps tailor our approach to current market demands Expected answer: Shift towards defensive ETFs or sector-specific funds Impact on approach: Would prioritize expanding ETF selection in those areas

• Considering our revenue model, I assume we charge a management fee based on assets under management (AUM). Is this correct, and are there any other significant revenue streams we should consider?

Why it matters: Determines whether we should focus on attracting new investors or increasing AUM from existing ones Expected answer: Primarily AUM-based fees with some additional services Impact on approach: Might lean towards improving algorithm if it leads to higher AUM retention

• Regarding our user segments, are we primarily serving retail investors, or do we have a significant institutional client base as well?

Why it matters: Different investor types have varying needs and expectations Expected answer: Mix of retail and smaller institutional clients Impact on approach: Would need to balance algorithm improvements with ETF diversity to cater to both segments

• From a technical standpoint, how modular is our current robo-advisor algorithm? Could we potentially improve it incrementally while also expanding our ETF selection?

Why it matters: Determines if we can pursue both options simultaneously to some degree Expected answer: Moderately modular, allowing for some parallel development Impact on approach: Might consider a hybrid strategy rather than a strict either/or approach

• In terms of resources, how does our current team capacity and expertise align with these two potential focus areas?

Why it matters: Influences the feasibility and timeline of each option Expected answer: Stronger in-house expertise in algorithm development Impact on approach: Might favor algorithm improvement unless we can quickly acquire ETF expertise