Title/Initiative Google News Enhancement Project - Improving User Next-Day Retention
Date & Version 1 Nov 2024, v3.0
Product - Point of Contact (POC) Suraksha Hegde - Product Manager
Quinn Teoh - Product Manager
Design POC Ji-Eun Lee - UX Design Lead
Tech POC Radhi Devlukia - AI Engineer
David Jonas - AI Engineer
Raj Sharma - Backend Engineer
Ming Jun Lim - Backend Engineer
Nina Torres - Front-end Engineer
Tony Parker - Front-end Engineer
Sarah Chen - Data Analyst
Priyanka Chopra - QA Engineer
Marketing POC James Wilson - Product Marketing

🎯 Our Objectives

This project aims to increase the next-day retention rate of the Google News app from 20% to 25% within 6 months by addressing key user pain points through smarter onboarding and interest management, focus in fact-based content and personalized news delivery.

For Business

This means driving a growth in daily active users, strengthening user retention through increased engagement and session duration, and solidifying Google News' position as a leader in personalized news delivery.

For User

The project seeks to deliver a seamless experience where they can quickly access relevant news, eliminate irrelevant content, and easily manage their preferences. By fostering a reading habit with personalized content and notifications, the project will not only enhance user satisfaction but also build trust and loyalty, making Google News an indispensable part of users' routines.


🏆 How do we measure success?

Our Goal

<aside> 🎯 To increase next-day retention of Google News app from 20% to 25% over 6 months (Next Day Retention: % Users who came back to view a news content the next day after viewing a news content on any given day)

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Success Metrics & Guardrail Metrics

Metrics prioritizations are evaluated based on:

Smart Interest Management and Content Discovery

🏆 Success Metrics

  1. Click-through rate (CTR) on suggested articles Recommendation effectiveness
  2. Time spent on app per session Content relevance
  3. Increase in daily/weekly active users User retention rate

Content Classification through AI and Community Feedback

🏆 Success Metrics

  1. Classification accuracy percentage Core functionality
  2. Increase in fact-checked news Aligns with company strategy to focus on fact-based news
  3. Number of mislabelled reports System reliability

AI summaries and Multiple Content Formats

🏆 Success Metrics

  1. Click-through rate (CTR) on AI icon Feature adoption
  2. Time spent per content type Format effectiveness & Understanding user preferences
  3. Content completion rate Content usage & engagement

🛡️ Guardrail

  1. Long onboarding time (> 3 minutes) Positive user experience
  2. Low recommendation accuracy (< 90%) Maintain user trust & retention
  3. Outdated content (> 24 hours) Make sure newer content gets higher visibility

🛡️ Guardrail

  1. Low tag accuracy rate (< 90%) Feature credibility
  2. Long response time for reports ( > 24 hours) Maintain user trust

🛡️ Guardrail

  1. Low AI response accuracy Maintain user trust & Feature credibility
  2. Long page load time (> 2 seconds) Positive user experience
  3. Limited scroll depth (< 75%) User engagement with content