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Company focus

GoodRx

Why has GoodRx's prescription discount card usage dropped 15% among new users in the past month?

Prepared by NextSprints

15 mins
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Data Analysis Problem Solving Strategic Thinking Healthcare Technology E-commerce Fintech Data Analysis User Retention Product Metrics Root Cause Analysis Healthcare Tech
Product Management Root Cause Analysis Question: Investigating GoodRx's prescription discount card usage decline among new users

Introduction

GoodRx's prescription discount card usage has dropped 15% among new users in the past month, signaling a potential issue with user acquisition or retention. This analysis will systematically identify, validate, and address the root cause while considering both immediate and long-term implications for the product.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into product understanding, metric breakdown, and data analysis. From there, I'll form hypotheses, conduct root cause analysis, and propose validation methods and solutions.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

"Looking at the timing, I'm thinking there might have been a recent change in the onboarding process. Have there been any updates to the new user experience in the past 1-2 months?"

Why it matters: Changes in onboarding could directly impact new user adoption. Expected answer: Yes, there was a redesign of the sign-up flow. Impact on approach: If confirmed, I'd focus on analyzing the new vs. old onboarding process.

"I'm curious about the competitive landscape. Has there been any significant move from competitors or new entrants in the prescription discount space recently?"

Why it matters: Competitive pressure could explain the drop in new user adoption. Expected answer: A new player launched an aggressive marketing campaign. Impact on approach: I'd investigate our marketing efforts and value proposition compared to competitors.

"Considering the 15% drop, I'm wondering about the baseline. What was the typical month-over-month variation in new user adoption before this drop?"

Why it matters: Understanding normal fluctuations helps determine if this is truly anomalous. Expected answer: Typical variation was around 3-5% month-over-month. Impact on approach: Confirms this is a significant deviation, warranting deeper investigation.

"Given that this affects new users, I'm thinking about our acquisition channels. Have there been any changes in our marketing mix or spend in the past month?"

Why it matters: Changes in acquisition strategy could directly impact new user numbers. Expected answer: Marketing spend was consistent, but there were shifts in channel allocation. Impact on approach: I'd analyze the performance of different acquisition channels in detail.

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Updated Jan 22, 2025