Should IXL Learning prioritize expanding its math skill coverage or improving the adaptive learning algorithm for existing skills?

Introduction

The trade-off between expanding IXL Learning's math skill coverage and improving the adaptive learning algorithm for existing skills presents a critical decision point for the company's product strategy. This scenario involves balancing breadth versus depth in our educational offering, with significant implications for user experience, learning outcomes, and business growth. I'll analyze this trade-off by examining key factors, proposing metrics, and designing an experiment to inform our decision.

Step 1

Clarifying Questions (3 minutes)

• Context: Based on IXL's position in the edtech market, I'm thinking we might be facing increased competition. Could you share insights on our current market share and how it's trending?

Why it matters: Helps determine if expansion or refinement is more critical for competitiveness. Expected answer: Stable market share with emerging competitors in niche areas. Impact on approach: Would influence whether to focus on breadth or depth of offering.

• Business Context: I'm assuming our revenue model is subscription-based. Can you confirm this and share how our pricing tiers are structured?

Why it matters: Affects whether expanding skills or improving algorithms would have a greater impact on revenue. Expected answer: Tiered subscription model with premium features. Impact on approach: Would guide decision on which option provides more value to justify higher tiers.

• User Impact: Thinking about our user segments, are we primarily serving K-12 students or do we have a significant adult learner base as well?

Why it matters: Different user groups may value skill breadth vs. adaptive learning differently. Expected answer: Primarily K-12 with growing adult learner segment. Impact on approach: Would influence which option better serves our core users and growth areas.

• Technical: Regarding our current adaptive learning algorithm, what's its performance in terms of accurately assessing and adapting to student needs?

Why it matters: Determines the potential impact of algorithm improvements. Expected answer: Good performance with room for improvement in certain skill areas. Impact on approach: Would indicate whether algorithm refinement could yield significant benefits.

• Resource: Can you give me an idea of our current team composition? Are we more heavily staffed on content creation or machine learning?

Why it matters: Influences feasibility and time-to-market for each option. Expected answer: Balanced team with slight emphasis on content creation. Impact on approach: Would affect which option we could execute more efficiently.