Product metrics - part 5 - Engagement Metrics


Engagement Metrics

1. Daily Active Users (DAU)

Definition: Number of unique users who engage with the product daily.

Examples:

  • Social Media App: 50,000 users log in daily.
  • News App: 15,000 people read articles each day.
  • Stock Trading Platform: 8,000 traders place at least one order daily.

2. Monthly Active Users (MAU)

Definition: Number of unique users who engage at least once in a month.

Examples:

  • E-learning Portal: 1,00,000 unique students logged in during the month.
  • Gaming App: 3 lakh players active in the last 30 days.
  • Mental Health App: 12,000 patients used chat/counseling features this month.

3. DAU/MAU Ratio

Definition: Measures stickiness — how often monthly users return daily.
Formula:
DAU / MAU

Examples:

  • Social App: DAU = 50,000, MAU = 2,00,000 →
    Stickiness = 0.25 or 25%
  • Fintech App: DAU = 5,000, MAU = 50,000 → 10%
  • Fitness App: DAU = 10,000, MAU = 40,000 → 25%

4. Session Length

Definition: Average duration a user spends per session.
Formula:
Total Session Time / Number of Sessions

Examples:

  • Video Streaming App: Total 10,000 mins across 1,000 sessions →
    = 10 minutes/session
  • Online Retail: Avg. session time = 7 minutes.
  • Online Chess App: 15 minutes average per game session.

5. Session Frequency

Definition: How often a user returns in a given time frame.

Examples:

  • Banking App: Active users open the app 12 times/month on average.
  • Learning App: Users return for 5 sessions/week.
  • Telemedicine App: Patients open app twice a week on average.

6. Feature Adoption Rate

Definition: % of users actively using a specific feature.
Formula:
Feature Adoption = (Users of Feature / Total Users) × 100

Examples:

  • CRM: 3,000 of 10,000 users use automation workflows →
    = 30%
  • UPI App: 70% of users have enabled auto-pay.
  • Learning App: 2,000 of 5,000 use discussion forums → 40%.

7. Engaged Users

Definition: Users who use the product meaningfully in a period.
Criteria depends on product (e.g., time spent, key actions taken).

Examples:

  • News App: 20,000 users read 5+ articles weekly.
  • Fitness Tracker: 5,000 users hit daily step goals 5x/week.
  • EdTech: 8,000 users watched 3+ lessons per week.

8. Click-Through Rate (CTR)

Definition: % of users who click on a link/button/ad.
Formula:
CTR = (Clicks / Impressions) × 100

Examples:

  • Email Campaign: 500 clicks from 10,000 opens →
    = 5%
  • Banner in App: 300 of 8,000 impressions clicked → 3.75%
  • Product Tour CTA: 100 clicks from 1,000 visits → 10%


Comments

Post a Comment

Popular posts from this blog

Beyond Google: The Best Alternative Search Engines for Academic and Scientific Research

Google may be the most popular search engine, but it isn’t always the best for academic and scientific research. Many valuable scholarly resources are buried under commercial results or hidden behind paywalls. Fortunately, there are specialized search engines designed to help researchers, students, and professionals find high-quality, peer-reviewed content. Here’s a selection of the best alternatives to Google, focusing on science, technology, medicine, and economics. 1. RefSeek ( www.refseek.com ) Best for: General academic research. RefSeek indexes over a billion documents, including research papers, encyclopedias, and books. Unlike Google, it prioritizes educational content and filters out commercial websites. 2. WorldCat ( www.worldcat.org ) Best for: Finding books and research materials in libraries worldwide. WorldCat allows users to search for books, articles, and historical archives in over 20,000 libraries. It’s ideal for tracking down rare or specialized academic ma...
Read more

LLM-based systems- Comparison of FFN Fusion with Other Approaches

  Comparison of FFN Fusion with Other Approaches & Suitable Use Cases FFN Fusion (NVIDIA) FFN Fusion optimizes transformers by identifying feed-forward layers that can be executed in parallel. By analyzing dependencies and fusing low-interaction FFN layers, it achieves significant reductions in inference latency and computational cost. Unlike traditional techniques that modify numerical precision or prune parameters, this approach restructures the model while preserving accuracy. Best Use Cases High-throughput AI applications : Ideal for AI assistants, chatbots, and large-scale LLM-based systems that need rapid multi-token generation. Enterprise-level LLM deployments : Works well where cost efficiency is important without compromising model performance. Real-time scientific research tools : Can enhance inference speed in AI-driven analytics, simulations, and predictive modeling. Quantization Quantization reduces the precision of numerical calculations (e.g., from 32-...
Read more

Product management. Metrics and examples

  Business Metrics focused on startups: A Comprehensive Guide to Measuring Growth and Success for a Product manager  For startups, tracking the right metrics is critical for measuring financial performance, customer acquisition, retention, and market potential. These key indicators help founders, investors, and executives make informed decisions, optimize business strategies, and drive sustainable growth. While some metrics are universal, their application varies across industries. This guide provides a detailed breakdown of essential startup metrics, with diverse examples from SaaS, healthcare, pharma, automotive, fintech, e-learning, and more. Revenue Metrics: Understanding Financial Performance 1. Monthly Recurring Revenue (MRR) The total revenue generated from paid customer subscriptions each month. Example 1 (SaaS): A cloud-based project management platform earns $50,000 monthly from subscriptions. Example 2 (Healthcare): A telemedicine platform generates $30,00...
Read more