In developing Internet Brands's CarsDirect pricing tool, how do we weigh providing more accurate pricing data against potential reduced conversions from higher displayed prices?

Introduction

The CarsDirect pricing tool trade-off presents a critical challenge: balancing accurate pricing data against potential reduced conversions due to higher displayed prices. This scenario encapsulates the tension between providing value through precision and maintaining user engagement. I'll address this by examining the product context, analyzing potential impacts, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming CarsDirect is a significant revenue driver for Internet Brands. Could you confirm if this is one of our top-performing products in terms of revenue and user engagement?

Why it matters: Helps prioritize the importance of this decision Expected answer: Yes, it's a key product Impact: Would justify more resources and careful consideration

• Business Context: Based on the competitive landscape, I'm thinking accurate pricing is a key differentiator. How does our pricing accuracy compare to our main competitors currently?

Why it matters: Informs the potential competitive advantage of improved accuracy Expected answer: We're on par or slightly behind Impact: If behind, it might justify pushing for higher accuracy despite potential conversion drops

• User Impact: Considering user behavior, I'm hypothesizing that price-sensitive users might be more likely to convert on lower prices. Do we have data on the price elasticity of demand for our user base?

Why it matters: Helps predict the impact of price changes on conversions Expected answer: Some elasticity data available Impact: High elasticity would suggest caution in displaying higher prices

• Technical: Given the complexity of car pricing, I'm assuming we're using machine learning models. What's our current confidence level in our pricing predictions?

Why it matters: Indicates the potential for improvement in accuracy Expected answer: Moderate confidence with room for improvement Impact: High confidence might suggest focusing on conversion optimization instead

• Timeline: Considering potential market changes, how urgent is this decision? Are we facing any immediate competitive pressures?

Why it matters: Determines the speed and scale of our approach Expected answer: Important but not critical in the short term Impact: Would allow for a more measured, iterative approach to testing and implementation