Should ADARA prioritize expanding its data partnerships to enhance targeting accuracy or focus on improving its existing AI algorithms for better campaign performance?

Introduction

The trade-off we're examining today is whether ADARA should prioritize expanding its data partnerships to enhance targeting accuracy or focus on improving its existing AI algorithms for better campaign performance. This decision is crucial for ADARA's future growth and market position in the ad tech industry. I'll analyze this trade-off by considering the business context, user impact, technical feasibility, and resource allocation.

Step 1

Clarifying Questions (3 minutes)

• Business Context: I'm thinking ADARA's current market position might influence this decision. Could you share how we're performing against competitors in terms of campaign effectiveness and data richness?

Why it matters: Helps prioritize which area needs more immediate attention Expected answer: We're lagging in data breadth but competitive in AI capabilities Impact on approach: Would lean towards data partnerships if we're significantly behind

• User Impact: Based on client feedback, I'm assuming larger advertisers might benefit more from expanded data. Can you confirm our key customer segments and their primary pain points?

Why it matters: Ensures solution addresses most valuable customers' needs Expected answer: Mix of large brands and mid-size companies, struggling with targeting accuracy Impact on approach: Would influence whether to focus on broad data or niche, high-value datasets

• Technical Feasibility: Considering our current AI stack, I'm curious about the potential gains from algorithm improvements. What's the estimated performance increase we could achieve with our existing data?

Why it matters: Determines if AI improvements can yield significant results without more data Expected answer: 10-15% potential improvement with current data Impact on approach: If high, might prioritize AI development over new data sources

• Resource Allocation: Given the potential scope of both options, I'm wondering about our current team structure. How are our data science and partnership teams resourced relative to each other?

Why it matters: Influences which option is more readily actionable Expected answer: Data science team is larger and more established Impact on approach: Might favor AI improvements if partnership team needs significant scaling

• Timeline Pressure: Thinking about market dynamics, I'm curious about any upcoming product launches or competitive moves we need to consider. Are there any critical deadlines or events driving urgency for either option?

Why it matters: Helps prioritize short-term vs. long-term strategy Expected answer: Major industry conference in 6 months where we showcase capabilities Impact on approach: Could push for a hybrid solution to show progress in both areas