What features could MadHive add to its linear TV attribution solution to provide more actionable insights?

Introduction

To improve MadHive's linear TV attribution solution, we need to focus on adding features that provide more actionable insights. This challenge involves enhancing the product's ability to deliver valuable, data-driven information that clients can readily use to optimize their TV advertising strategies. I'll approach this by examining the current product context, identifying key user segments, analyzing pain points, generating solutions, and proposing metrics for success.

Step 1

Clarifying Questions (5 mins)

• Looking at the product context, I'm thinking MadHive's solution might be targeting mid to large-size advertisers or agencies. Could you confirm the primary user base and their typical use cases for the linear TV attribution solution?

Why it matters: Determines the depth and complexity of insights needed Expected answer: Primarily used by media agencies and brand advertisers Impact on approach: Would tailor features to either agency workflow or brand KPIs

• Considering the evolving TV landscape, I'm curious about cross-platform attribution. Does the current solution integrate data from connected TV (CTV) or over-the-top (OTT) platforms, or is it strictly focused on traditional linear TV?

Why it matters: Influences the scope of data integration and insight generation Expected answer: Currently focused on linear TV, with plans to expand to CTV/OTT Impact on approach: Would prioritize features that bridge the gap between linear and digital

• Given the focus on actionable insights, I'm wondering about the current data visualization and reporting capabilities. What types of reports or dashboards are currently available to users, and what feedback have we received about their utility?

Why it matters: Identifies areas for improvement in data presentation and analysis Expected answer: Basic reporting with some customization, users requesting more real-time and predictive insights Impact on approach: Would focus on advanced analytics and intuitive data exploration tools

• Thinking about the competitive landscape, I'm interested in understanding MadHive's unique value proposition. What sets our linear TV attribution solution apart from competitors, and where do we see the most room for improvement?

Why it matters: Helps identify areas to double down on or address gaps in the offering Expected answer: Strong in deterministic matching, but lacking in granular audience insights Impact on approach: Would prioritize features that leverage our strengths while addressing competitive weaknesses