For TransUnion's SmartMove tenant screening, how do we balance comprehensive background checks against faster turnaround times for landlords?

Introduction

The trade-off between comprehensive background checks and faster turnaround times for landlords in TransUnion's SmartMove tenant screening is a critical challenge. This scenario involves balancing the depth of information provided to landlords with the speed of service delivery. I'll analyze this trade-off by examining the product, stakeholders, metrics, and potential solutions.

Step 1

Clarifying Questions (3 minutes)

• Based on the competitive landscape, I'm thinking this trade-off might be driven by market pressures. Could you share insights on how our competitors are balancing speed and comprehensiveness in their screening products?

Why it matters: Helps position our solution in the market Expected answer: Competitors focusing on speed with less comprehensive checks Impact on approach: Would emphasize differentiation through depth of screening

• Considering our revenue model, I assume faster turnaround could lead to higher volume but potentially lower-priced offerings. How does our pricing structure currently account for speed vs. comprehensiveness?

Why it matters: Informs potential revenue impacts of trade-off decisions Expected answer: Tiered pricing based on depth of screening Impact on approach: Would explore hybrid models balancing speed and depth

• Looking at user behavior, I'm curious about the typical landlord's decision-making process. What percentage of landlords make decisions solely based on our screening results versus using it as one factor among many?

Why it matters: Helps prioritize speed vs. comprehensiveness based on user needs Expected answer: Majority use screening as one factor among many Impact on approach: Would focus on providing key insights quickly with options for deeper dives

• From a technical perspective, I'm wondering about our current data processing capabilities. What's our current average turnaround time for a comprehensive check, and what are the main bottlenecks?

Why it matters: Identifies potential areas for optimization Expected answer: 24-48 hour turnaround with data aggregation as main bottleneck Impact on approach: Would explore parallel processing or pre-fetching strategies

• Regarding resources, I'm curious about our current team structure. Do we have dedicated teams for data processing and user experience, or is it a shared resource?

Why it matters: Determines feasibility of simultaneous improvements in speed and comprehensiveness Expected answer: Shared resources across multiple products Impact on approach: Would consider reorganization or focused sprint teams