User Growth
What user growth do we expect to plan for...
Purpose:
- Growth estimates help us correctly plan our systems design.
- Some app may be reaching for the Moon, while others are more specialized with less users.
- Actions per user (APU) projections are also important in estimating growth.
- Growth impacting systems can be increased usage per user, not only # of users.
- Actions can include various types such as a sale, a pageview, a subscription, a product return, a support ticket] etc.
Format:
- Starter: At least a few of these topline estimates ca help right-size our plans.
- Extended: An extra session with product manager may help overall project design, organization.
- It may help product managers and owners find and fill gaps in their product plans, which helps us in system planning later.
Summary - Calculations
Calculate user growth rates for a new app, including:
| Metric | Definition |
|---|---|
| Monthly Active Users (MAUs) | Unique users who have engaged with the app in a given month. |
| Daily Active Users (DAUs) | Unique users who have engaged with the app on a given day. |
| Monthly Recurring Users (MRUs) | Users who have engaged with the app on a monthly basis. |
| Daily Recurring Users (MRUs) | Users who have engaged with the app on a daily basis. |
| Actions Per User (APUs) | Number of actions per user on some time interval. |
| Retention rate | Percentage of users who return to the app after their initial visit. |
| Acquisition rate | Rate at which new users are acquiring the app. |
| Net Promoter Score (NPS) | Likelihood that users will recommend the app to their friends and family. |
| Cohort analysis | User behavior over time by grouping users based on when they first engaged with the app. |
| Churn rate | This measures the rate at which users stop using the app over time. |
| Average Revenue per User (ARPU) | This measures the average revenue generated by each user. |
| Lifetime Value (LTV) | Projected revenue that a user will generate over their lifetime as a user of the app. |
| Engagement rate | Level of engagement of users with the app, such as the number of sessions per user per day. |
| Viral coefficient | This measures the rate at which users invite new users to the app. |
| Stickiness | This measures the ratio of DAUs to MAUs, indicating how frequently users return to the app. |
Monthly Active Users (MAUs)
- The formula for MAUs is the number of unique users who have engaged with the app in a given month.
- For example, if an app had 10,000 unique users in January, 15,000 unique users in February and 12,000 unique users in March, then the MAUs for that quarter would be 37,000.
Daily Active Users (DAUs)
- The formula for DAUs is the number of unique users who have engaged with the app on a given day.
- For example, if an app had 1,000 unique users on Monday, 1,200 unique users on Tuesday, and 900 unique users on Wednesday, then the DAUs for that week so far would be 3,100.
| Growth Rate | DAU (End of Month 1) | DAU (Month 2) | DAU (Month 3) | DAU (Month 6) | DAU (Month 9) | DAU (Month 12) |
|---|---|---|---|---|---|---|
| 1% | 5,050 | 5,101 | 5,152.01 | 6,079.8 | 6,637.87 | 7,227.98 |
| 3% | 5,150 | 5,303 | 5,459 | 6,639.11 | 7,532.32 | 8,821.9 |
| 5% | 5,250 | 5,413.5 | 5,566.33 | 7,254.47 | 8,848.15 | 14,934.3 |
| 10% | 5,500 | 6,050 | 6,655 | 9,738.26 | 13,817.94 | 44,037.8 |
| 20% | 6,000 | 7,200 | 8,640 | 14,912 | 27,648 | 282,934.3 |
| 30% | 6,500 | 8,450 | 11,185 | 25,816.5 | 75,947.5 | 3,557,926.9 |
Projecting the growth of Daily Active Users (DAU) on an app over a year starting at 5,000 at different growth rates per month can be done using the formula:
DAU(t) = DAU(0) * (1 + r)^t
Where:
- DAU(t) is the projected number of DAU at time t (in months)
- DAU(0) is the starting number of DAU, which is 5,000
- r is the growth rate per month, which can be 3%, 5%, 10%, or 20%
- t is the number of months
To calculate the projected number of DAU at the end of the year, we will use 12 months as t:
- At 3% growth per month, DAU(12) = 5,000 * (1 + 0.03)^12 = 8,821.9
- At 5% growth per month, DAU(12) = 5,000 * (1 + 0.05)^12 = 14,934.3
- At 10% growth per month, DAU(12) = 5,000 * (1 + 0.1)^12 = 44,037.8
- At 20% growth per month, DAU(12) = 5,000 * (1 + 0.2)^12 = 282,934.3
These projections are based on a steady growth rate, which is not always the case in the real world. There are many factors that can affect the growth of an app, such as changes in the market, user needs, or competition.
Monthly Recurring Users (MRUs)
- Dividing the total number of unique users by the total number of months and rounding down to the nearest whole number.
- For example, if an app had 10,000 unique users in January, 15,000 unique users in February, and 12,000 unique users in March, to calculate the MRUs, the app would take the unique users from each month, and then remove any duplicates from the previous months.
- So, if there were 2,000 users who appeared in all three months, then the MRUs for the quarter would be (10,000+15,000+12,000) - 2,000 = 25,000
Daily Recurring Users (MRUs)
- Number of users who have engaged with the app on a daily basis.
Retention rate
- (Number of users who return to the app / Number of users who initially acquired the app) x 100
- For example, if an app had 1,000 initial users and 800 of them returned to the app after their initial visit, the retention rate would be (800 / 1,000) x 100 = 80%.
Acquisition rate
- (Number of new users acquired / Number of total users) x 100
- For example, if an app had 10,000 total users and 1,000 new users were acquired in a given month, the acquisition rate would be (1,000 / 10,000) x 100 = 10%.
Net Promoter Score (NPS)
- (% of users who are promoters - % of users who are detractors)
- Promoters are users who rate the app 9 or 10 on a scale of 1-10. Detractors are users who rate the app 0-6 on the same scale.
- For example, if an app had 100 users, 20 of them rated the app 9 or 10, 10 of them rated the app 7 or 8, and 70 of them rated the app 0-6, the NPS would be (20 - 70) = -50.
- It's worth noting that the NPS score ranges from -100 to 100, a positive score indicates that there are more promoters than detractors
- a score of zero means that there are equal number of promoters and detractors, and
- a negative score means that there are more detractors than promoters.
Cohort analysis
- Cohort analysis is a method of grouping users based on when they first engaged with the app.
- This allows for the analysis of user behavior over time and helps identify patterns and trends.
- For example, a company may group users based on the month they first signed up and track their engagement and retention over time.
Churn rate
- Churn rate is a measure of the rate at which users stop using the app over time.
- It is typically expressed as a percentage and can be calculated by dividing the number of users who * churned (stopped using the app) by the total number of users at the beginning of a specific period.
Average Revenue per User (ARPU)
- ARPU is a measure of the average revenue generated by each user.
- It can be calculated by dividing the total revenue by the number of users.
- For example, if a company generated $100,000 in revenue and had 10,000 users, the ARPU would be $10.
Lifetime Value (LTV)
- LTV is a measure of the projected revenue that a user will generate over their lifetime as a user of the app.
- It can be calculated by multiplying the average revenue per user (ARPU) by the average user lifetime (in years or months).
- Fo* r example, if the ARPU is $10 and the average user lifetime is 3 years, the LTV would be $30.
Engagement rate:
- Engagement rate is a measure of the level of engagement of users with the app.
- It can be calculated by taking the total number of sessions per user per day, and dividing that by the total number of active users.
- For example, if there were 100 active users and they had a total of 500 sessions per day, the engagement rate would be 5 sessions per user per day.
Viral coefficient
- Viral coefficient is a measure of the rate at which users invite new users to the app.
- It can be calculated by taking the number of new users that were acquired through invitations and dividing that by the number of existing users who invited them.
- For example, if an app has 100 existing users and they invite 200 new users, the viral coefficient would be 2 (200 new users / 100 existing users).
Stickiness
- Stickiness is a measure of how frequently users return to the app.
- It can be calculated by taking the ratio of Daily Active Users (DAUs) to Monthly Active Users (MAUs).
- DAUs are the number of unique users that engaged with the app on a given day.
- MAUs are the number of unique users that engaged with the app within a given month.
- Stickiness ratio is calculated by dividing DAUs by MAUs.
- For example, if an app has 100 DAUs and 1000 MAUs, the stickiness ratio would be 0.1 (100 DAUs / 1000 MAUs).
Analytics Methods
There are several methods that can be used to track user engagement:
AWS:
- Amazon Pinpoint: This service can be used to track user engagement and behavior on a website or mobile app. You can use it to track user interactions, such as page views and clicks, and analyze the data to identify patterns and trends.
- Amazon Kinesis: This service can be used to collect, process, and analyze real-time streaming data from a website or mobile app. You can use it to track user interactions, such as mouse movements and clicks, and analyze the data to identify patterns and trends.
- Amazon QuickSight: This service can be used to create visualizations and reports of your website or mobile app data. You can use it to analyze user engagement and behavior, such as page views and clicks, and identify patterns and trends over time.
- Amazon Redshift : This service can be used to store, manage, and analyze large amounts of data. You can use it to store and analyze your website or mobile app data and create reports and visualizations to track user engagement and behavior over time.
Azure:
- Azure Application Insights: This service allows you to track user interactions with your web or mobile app, such as page views, clicks, and exceptions. It also provides real-time performance monitoring, analytics and can be integrated with other Azure services.
- Azure Event Hubs: This service allows you to collect, store, and process large amounts of data from different sources, such as IoT devices, mobile apps, and websites. You can use it to track user interactions and analyze the data to identify patterns and trends.
- Azure Stream Analytics: This service allows you to analyze and process real-time data streams from different sources, such as IoT devices, mobile apps, and websites. You can use it to track user interactions and analyze the data to identify patterns and trends.
- Azure Log Analytics: This service allows you to collect, search, and analyze log data from different sources, such as Azure resources, applications, and other systems. You can use it to track usr interactions and analyze the data to identify patterns and trends.
- Azure Power BI: This service allows you to create interactive visualizations and reports of your data. You can use it to analyze user engagement and behavior and identify patterns and trends over tie.
- Azure Machine Learning: This service allows you to build, deploy, and manage machine learning models. You can use it to analyze user data and predict patterns of behavior and engagement.
Google Cloud Platform (GCP):
- Google Analytics: This service allows you to track user interactions with your web or mobile app, such as page views, clicks, and conversions. It also provides real-time performance monitoring and analytics.
- Google BigQuery: This service allows you to collect, store, and analyze large amounts of data from different sources, such as mobile apps, websites, and IoT devices. You can use it to track user interactions and analyze the data to identify patterns and trends.
- Google Cloud Dataflow: This service allows you to process and analyze large amounts of data in real-time. You can use it to track user interactions and analyze the data to identify patterns and trends.
- Google Cloud Data Studio: This service allows you to create interactive visualizations and reports of your data. You can use it to analyze user engagement and behavior and identify patterns and trends over time.
- Google Cloud Machine Learning Engine: This service allows you to build, deploy, and manage machine learning models. You can use it to analyze user data and predict patterns of behavior and engagement.
- Stackdriver: This service allows you to collect, store, and analyze logs and metrics from various sources, such as GCP resources and applications. You can use it to track user interactions and analyze the data to identify patterns and trends.
Standalone and other:
- Google Analytics: This is a free tool that can be used to track user engagement on a website or mobile app. It allows you to see the number of DAUs and MAUs, and calculate the stickiness ratio.
- Mixpanel: This is a paid tool that provides detailed analytics for mobile and web apps. It allows you to track user engagement, including the number of DAUs and MAUs, and calculate the stickiness ratio.
- Amplitude: This is another paid tool that provides detailed analytics for mobile and web apps. It allows you to track user engagement, including the number of DAUs and MAUs, and calculate the stickiness ratio.
- Custom tracking: If you don't want to use any external tools, you can also implement your own tracking system. This could involve using server-side tracking to record user engagement data, then calculating the stickiness ratio by dividing the DAUs by the MAUs.
- A/B testing: you can use A/B testing to compare the stickiness ratio of two or more versions of your app or website to see which one is more successful in keeping users engaged.
User Growth Questions
- What is the expected rate of user growth in the short and long term?
- How will the user base be segmented (e.g. by geography, demographics, etc.)?
- Are there any known constraints or limitations on user growth (e.g. market saturation, regulatory issues, etc.)?
- How do you expect behavior and usage patterns change over time?
- What is the size and potential of the target market for the app?
- How does the app compare to similar products or services currently available in the market?
- What are the key features and benefits of the app that will drive user adoption?
- How will the app be marketed and promoted to reach potential users?
- Is there a marketing schedule that may affect usage?
- What is the expected user acquisition cost and lifetime value of a user?
- What is the expected conversion rate from app download to active user?
- How do you expect the app be able to retain its user base over time?
- How will the app leverage network effects to drive growth?
- How will the software need to scale to accommodate increased user numbers?
- How do you expect the software handle an influx of new users?
- How do you expect the architecture be built to handle the high availability needs?
- How will the software handle the data growth with increases?
- How will the app leverage word-of-mouth or viral marketing to drive growth?
- What are the key performance indicators for user growth and how will they be tracked and measured?
- How will the security be handled for increased user numbers?
- How will the software handle the load balancing for increases?
- How can user onboarding and registration be managed to handle a large number of new users?
- How will we handle data privacy and security with increased users?
- How can we handle authentication and authorization with increased user numbers?
- How will it affect support and customer service with increased users?
- How will it affect engagement and retention with increases?
- How do you handle feedback and feature requests with increased user numbers?
- How will it affect analytics and metrics gathering with increased user numbers?
- How does it affect testing and quality assurance with increased users?
- How will you handle performance and scalability with increased user numbers?
- How will you handle integration and interoperability with other systems and platforms with increased user numbers?